Rice Yield Estimation Using Vegetation Indexes (NDVI and EVI) Derived from Sentinel-2 Imagery for Sustainable Agriculture

Rice Yield Estimation Using Vegetation Indexes (NDVI and EVI) Derived from Sentinel-2 Imagery for Sustainable Agriculture | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Rice Yield Estimation Using Vegetation Indexes (NDVI and EVI) Derived from Sentinel-2 Imagery for Sustainable Agriculture Gagad Restu Pratiwi, Indarto Indarto, Farid Lukman Hakim, Wawan Sulistiono, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6150129/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 08 Oct, 2025 Read the published version in Discover Sustainability → Version 1 posted 8 You are reading this latest preprint version Abstract Organic farming has emerged as an alternative agricultural production practice, accompanied by a growing market for organic products. Advanced remote sensing technology, exemplified by Sentinel-2 imagery as an optical satellite, can efficiently determine the rice planting season and project total rice production. It also enables the monitoring of plant health, irrigation, and fertiliser management, and environmental quality in organic cultivation areas. This study explored two vegetation indices, namely NDVI (Normalized Difference Vegetation Index) and EVI (Enhanced Vegetation Index). The study was conducted on 30 agricultural parcels in Candipuro District, Lumajang Regency, East Java, Indonesia. NDVI and EVI values were derived from Sentinel-2 imagery corresponding to each harvesting date; therefore, two harvesting periods were used in this study. Statistical indices—including the Spearman-rho test, correlation analysis, linear regression, coefficient of determination, and RMSE (Root Mean Square Error)—were used to compare rice yield estimates from satellite imagery with actual measurement results. The findings conclude that the EVI index provided the most accurate prediction, with R² ≈ 0.7 and RMSE = 0.03 for the second period. Both NDVI and EVI demonstrated promising outcomes for pre-harvest rice yield estimation, with EVI showing slightly superior performance, although data support remains limited. Crop Yield Estimation EVI NDVI Rice Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 1. Introduction In Asia, rice has been the staple food in many countries, with production continually striving to increase each year. China leads global rice production with 145.95 million metric tons, while Indonesia ranks fourth with 34 million metric tons in the 2022/2023 period [1]. In 2024, Indonesia's rice harvest area reached approximately 10.05 million hectares, yielding 53.14 million tons of rice [2]. The highest rice production was recorded in five provinces: East Java, Central Java, West Java, South Sulawesi, and South Sumatra. East Java ranked first in rice production in 2024, with 9.27 million tons of dry unhusked rice, equivalent to 5.353 million tons of milled rice. In 2022, East Java reported organic rice production of 828.767 tons from 423.98 hectares [3]. The report aligns with the fact that Lumajang became the first district in East Java to implement organic rice cultivation in 2001. Organic rice farming in the Lumajang subdistricts of Pronojiwo and Candipuro became the focus of government regulation and support. This support was reflected in the development of organic rice initiatives, funding projects to subsidise organic farmers, and the issuance of operational guidelines for organic rice cultivation [4]. Organic farming has become an alternative agricultural production practice practised by farmers worldwide. Furthermore, the market for organic products is increasing in Asia today. One is due to the negative impact of pesticide-derived products on agriculture, unsustainable farming practices, and increasing public health concerns [5],[6]. Research results show that conventional (or inorganic) agricultural practice negatively impacts the environment and people's health. Along with precision agriculture technologies for site-specific management (i.e., more output with the same or fewer inputs), sustainability factors related to inefficient fertiliser usage and low profitability from high input levels should also be considered. Thus, it can be closed sustainably by boosting productivity and decreasing its environmental impact [7]. The effect appeared as the decrease of water quality in the surrounding ecosystem [8], reduce soil fertility and health [9], produce more emission [10], done more negative risk to the health of farm and other peoples [11], [12], [13], and other negative impacts [14]. Concerning research done in six Asian countries that produce rice as a staple food, improved management techniques are desperately needed for fertiliser (N, P, K), irrigation water efficiency, and reducing the frequency of pesticide application without compromising production and profitability. It will result in a minor environmental impact, and rice yield and profit gaps may be bridged sustainably [15]. However, several constraints are still confronting organic farming practices, i.e., the local farmer’s mindset and habit, the land conversion, and the lack of incentive from the government. Yield prediction is urgently concerned with answering the question of the feasibility of the organic farming system. Why did the production decrease during the first conversion of land resources from chemical to organic farming? How about the production cost for inorganic agriculture? Historically, the estimation process relied heavily on conventional methods, involving agricultural extension officers collecting total production data from farmers. However, this traditional approach proved inefficient, time-consuming, and resource-intensive, contributing to challenges such as delayed information, high costs, labour intensity, and various technical and non-technical issues [16]. Remote sensing technologies allow for the assessment of crop productivity over vast spatial and temporal dimensions, and they have completely changed agricultural surveillance. Using vegetation indices like the NDVI and EVI, satellite sensors like Landsat, MODIS, and Sentinel-2 offer reliable multispectral data that may be used to evaluate agricultural conditions. The reflectance of vegetation in the red and near-infrared wavelengths, which have a significant correlation with photosynthetic activity and chlorophyll concentration, is the source of these indices [17]. Because of its ease of use and efficiency in identifying green vegetation, NDVI is frequently utilized; but, in regions with high biomass, it may become saturated. EVI, an enhanced measure that minimizes atmospheric distortions and soil background noise by using blue light reflectance and correction factors, was created to get around this restriction [17]. Researchers and policymakers can more accurately anticipate crop growth stages and final yields by examining time-series NDVI and EVI data throughout the growing season. This is especially true when paired with ground truth data and machine learning models [18][19]. In addition to aiding in production forecasts, these satellite-derived indicators improve precision farming methods, facilitating more effective resource management and policy formulation. Addressing issues with food security requires their incorporation into agricultural systems, especially in areas with little access to ground-based monitoring [19]. Advanced remote sensing technology, exemplified by Sentinel-2 imagery as an optical satellite, demonstrates its versatility across various agricultural domains [20], extending to the assessment of organic rice production [21]. Additionally, high-resolution Sentinel-2 imagery has proven helpful for detecting fruit on trees using image processing [22]. Research related to the use of satellites to estimate crop productivity has been conducted previously [23],[24],[25],[26]. However, this has never been done before in the East Java region, especially the Lumajang region. Developing a model for estimating organic rice production utilises satellite image data, enabling the anticipation of total harvests before the actual harvesting period. Identifying rice growth involves applying NDVI (Normalized Difference Vegetation Index) and EVI (Enhanced Vegetation Index) vegetation indices. NDVI aids in estimating chlorophyll content in leaves, while EVI contributes to discerning variations in canopy structure [27]. This function is important because the chlorophyll content differs significantly in the interaction between clones/varieties and the growing environment/fertilization [28]. It was further explained that the NDVI and NDWI-GAO methods were used to identify key phenological stages (i.e., tillering, heading date, and maturity) and field conditions (i.e., hydroperiod), demonstrating strong potential for estimating rice yields across different cultivars [29]. Using NDVI and EVI, derived from Sentinel-2, proves invaluable in efficiently determining the organic rice planting season and projecting total rice production. Therefore, research is imperative to estimate organic rice production in Candipuro District, Lumajang Regency, by optimising Sentinel-2 imagery usage and implementing NDVI and EVI algorithms. The aim is to estimate organic rice production by combining NDVI and EVI. 2. Material and methods The data utilised in this research consist of : (1) Sentinel-2 level 2A images (10m spatial resolution), (2) administrative boundary maps, (3) rice production data of the Candipuro Sub-District, and (4) field areas acquired through field surveys. Sentinel-2 is a pair of Earth observation satellites designed for the Copernicus program by the European Space Agency (ESA). Sentinel-2, a high-resolution multispectral imager, can take comprehensive images of the Earth's surface in 13 spectral bands, especially in the visible, near-infrared, and shortwave infrared ranges. These satellites, which operate in tandem with Sentinel-2a and Sentinel-2B, offer numerous revisits to the same region, ensuring excellent temporal resolution for tracking dynamic environmental changes, broad swath coverage, free and open data policy, and spatial resolutions between 10 and 60 meters [30]. The temporal scope of the data spans two planting periods in 2021, precisely May 10th, 2021, and October 12th, 2021, aligning closely with the harvest periods. The distribution pattern of organic rice was subsequently derived from cropping image results, including three villages: Penanggal, Sumbermujur, and Tambahrejo. A comprehensive survey identified thirty (30) farmers' rice fields covering 14.146 hectares (Fig. 1 ). Figure 1 shows the spatial distribution of organic rice field plots used in this study. The yellow plots on Fig. 1 display as GPS-surveyed shapefiles overlaid on the Arcgis basemap image. The map illustrates the spatial layout of the agricultural areas analysed. All spatial data were projected using the WGS 84 / UTM Zone 49s coordinate system. The study area is predominantly characterized by Regosol soil, with inclusions of Andosol and Gleysol. The cropping pattern in the study area within one year follows a Rice – Fallow – Rice sequence. The first planting period occurred between February and May 2021, and the second planting period took place between July and October 2021. The rice variety used was IR64. The climate type in Candipuro District, Lumajang Regency, East Java in 2021 was classified as a tropical monsoon climate (Am) according to the Köppen climate classification, characterized by distinct wet and dry seasons. The average temperature in Candipuro ranges from 23°C to 31°C throughout the year. The relative humidity is generally high, ranging from 70–90%, peaking during the rainy season. Candipuro District is located at an elevation of approximately 322 meters above sea level. The data processing procedure consists of multiple stages (Fig. 2 ). The procedure involves the creation of a shapefile for mapping the distribution of organic rice fields, transforming vegetation indices, and developing a linear regression model. This investigation employs two distinct vegetation indices: NDVI and EVI [31]. NDVI and EVI are widely used Indices for crop type mapping [32], [33], [34]. EVI optimises the blue channel to rectify NDVI value deficiencies due to atmospheric aerosol content, and it enhances canopy background precision by applying the L factor (soil/land conditions) for more accurate NDVI values [35]. The NDVI and EVI values of the plating areas are calculated as follows: $$\:\varvec{N}\varvec{D}\varvec{V}\varvec{I}=\:\frac{\varvec{N}\varvec{I}\varvec{R}-\varvec{R}\varvec{e}\varvec{d}}{\varvec{N}\varvec{I}\varvec{R}+\varvec{R}\varvec{e}\varvec{d}}$$ 1 $$\:\varvec{E}\varvec{V}\varvec{I}=\varvec{G}\:\times\:\frac{(\varvec{N}\varvec{I}\varvec{R}-\varvec{R}\varvec{e}\varvec{d})}{(\varvec{N}\varvec{I}\varvec{R}+{\varvec{C}}^{1}\times\:\varvec{R}\varvec{e}\varvec{d}-\:\:{\varvec{C}}^{2}\times\:\varvec{B}\varvec{l}\varvec{u}\varvec{e})}+\varvec{L}$$ 2 Where G is a gain factor (2.5), C 1 and C 2 are the coefficients of the aerosol resistance term (6 and 7.5), and L is the canopy background adjustment (1) [35]. The data analysis incorporates various statistical methodologies, including Spearman's test, statistical analysis, and regression model analysis. In constructing the model, rice field plots 1–15 serve as inputs to formulate a mathematical equation, which will subsequently be employed to make predictions for plots 16–30. The model's capabilities are then assessed through the coefficient of determination, and its validation is conducted using RMSE to gauge the accuracy of the prediction results compared to the actual harvest values. The statistical analysis was performed using Excel software 3. Results and discussion Based on the NDVI and EVI transformations, the results are shown in Fig. 3 – 6 and Table 1 . The NDVI values during Period 1 ranged from 0.645 to 0.834, with an average of 0.75, while during Period 2, they ranged from 0.5 to 0.726, with an average of 0.606. The relatively lower average values in Period 2 compared to Period 1 may be attributed to the younger age of the crops [36]. Figure 5. NDVI Period 2 (October 12th, 2021) Color Explanation on the Map: Red: Indicates areas with high index values (close to 0.887938), which may suggest very dense vegetation, areas with optimal crop growth, or very good land conditions. Yellow: Represents areas with moderate index values. This means vegetation is present, but not as optimal as in the red areas, possibly due to soil, water conditions, or the crop growth stage. Green: Indicates areas with low index values (close to 0.00512821), which could suggest sparse vegetation, bare soil, or areas with poor crop growth. Color Gradient (Green to Red): Reflects the transition of vegetation conditions, ranging from areas with poor plant health (green) to those with very good plant health (red). The black boxes on the map likely indicate research plot areas or ground truth observation points. Table 1 Index value and rice yield on periods 1 and 2 of 2021 Field Plot Code Area (ha) Period 1 Period 2 Yield ( tons ha − 1 ) NDVI EVI Yield ( tons ha − 1 ) NDVI EVI 1 0.217 12.875 0.720 0.672 13.709 0.699 0.736 2 0.383 8.974 0.801 0.574 8.535 0.583 0.544 3 0.706 6.211 0.748 0.495 6.487 0.549 0.639 4 1.031 4.520 0.649 0.508 4.567 0.514 0.504 5 1.331 3.952 0.688 0.465 4.345 0.553 0.539 6 0.630 6.623 0.711 0.573 7.012 0.663 0.545 7 0.400 9.252 0.761 0.491 10.871 0.706 0.666 8 1.541 4.125 0.670 0.515 4.918 0.541 0.579 9 1.956 3.707 0.645 0.490 3.919 0.549 0.486 10 0.120 10.713 0.725 0.516 20.347 0.726 0.792 11 0.064 13.521 0.809 0.677 15.690 0.657 0.719 12 0.226 10.484 0.827 0.685 9.826 0.560 0.513 13 0.420 8.988 0.771 0.580 9.655 0.571 0.694 14 0.232 12.208 0.834 0.705 10.145 0.680 0.640 15 0.126 12.568 0.793 0.665 14.336 0.642 0.633 16 0.285 10.709 0.825 0.671 7.804 0.600 0.566 17 0.782 5.628 0.659 0.509 5.947 0.534 0.537 18 0.693 6.253 0.680 0.517 6.015 0.557 0.549 19 0.062 12.993 0.805 0.695 7.780 0.592 0.551 20 0.136 9.861 0.716 0.596 8.871 0.574 0.624 21 0.199 10.993 0.744 0.638 5.431 0.500 0.501 22 0.144 12.911 0.829 0.712 12.876 0.683 0.692 23 0.203 12.223 0.790 0.739 9.118 0.584 0.631 24 0.121 8.561 0.775 0.536 8.677 0.571 0.572 25 0.298 10.694 0.768 0.659 12.236 0.712 0.691 26 0.344 9.803 0.769 0.595 10.690 0.647 0.660 27 0.657 6.423 0.684 0.553 6.372 0.549 0.493 28 0.404 9.154 0.727 0.568 10.756 0.665 0.660 29 0.260 11.251 0.783 0.702 12.192 0.642 0.676 30 0.041 12.684 0.784 0.750 9.163 0.559 0.628 Additionally, the limited availability of cloud-free imagery makes it challenging to find images closer to the harvest season. The EVI values during Period 1 ranged from 0.465 to 0.75, with an average of 0.602, whereas during Period 2, they ranged from 0.486 to 0.792, with an average of 0.61. The Spearman's rank correlation test (ρ) was also conducted to assess the association between harvest outcomes and NDVI and EVI values in each period. The analysis results indicate a strong correlation between NDVI and EVI values for each period and the harvest quantity [37]. The ρ-values show a strong correlation for NDVI in Periods 1 and 2, which are 0.714 and 0.814, respectively, and for EVI, which are 0.796 and 0.793, respectively. Meanwhile, in the Pearson correlation results (r), the values of the indices and harvest outcomes in both periods also indicate a strong association. The NDVI correlation (r) in periods 1 and 2 is 0.782 and 0.785, respectively. The EVI correlation (r) is slightly better, at 0.82 and 0.83, respectively The linear regression results between rice harvest outcomes and index values in each period are presented in Fig. 7 . The findings indicate that EVI yields a better coefficient of determination (R²) than NDVI in both periods. In both periods, EVI achieves R² values of 0.6924 and 0.6773, respectively. This suggests that the EVI model can predict 69.24% and 67.73% of actual field harvest outcomes through the equations y = 0.0164x + 0.4579 and y = 0.02x + 0.4027. The best results were obtained in EVI period 2, with an RMSE value of 0.0340. The estimated harvest value resulting from the EVI model equation is more accurate and closer to the actual value than NDVI. The correlation, determination, and RMSE values of EVI are more precise than those of NDVI. This is partly because EVI can reduce disturbances due to atmospheric and soil factors through existing corrections, making it more sensitive in recording biomass levels [38], [39]. The results of calculating estimated harvest values for each index and period are presented in Tables 2 and 3 below. Comparing the estimated and actual harvest values, we can see that EVI provides better results than NDVI. In the first period, from 15 rice field plots with a total harvest of 150.139 tons ha − 1 , the EVI estimation model gave a total estimate of 156.846 tons ha − 1 (+ 6.707 tons ha − 1 ) with the average difference for each rice field compared to the actual value amounting to 0.936 tons ha − 1 . Meanwhile, the NDVI estimation model provides a total estimated result of 136.788 tons ha − 1 (-13.351 tons ha − 1 ) with an average difference for each rice field plot to the actual value of 1.268 tons ha − 1 . Table 2 Estimated and actual rice yield (ton ha − 1 ) for period 1 Field Code NDVI EVI Estimated Actual Diff. Estimated Actual Diff. 16 12.129 10.709 1.420 11.887 10.709 1.179 17 4.887 5.628 0.741 6.365 5.628 0.737 18 5.800 6.253 0.453 6.639 6.253 0.386 19 11.280 12.993 1.713 12.689 12.993 0.305 20 7.392 9.861 2.468 9.330 9.861 0.530 21 8.607 10.993 2.386 10.742 10.993 0.251 22 12.309 12.911 0.602 13.261 12.911 0.350 23 10.600 12.223 1.623 14.174 12.223 1.952 24 9.976 8.561 1.415 7.275 8.561 1.286 25 9.635 10.694 1.060 11.479 10.694 0.785 26 9.699 9.803 0.104 9.290 9.803 0.513 27 5.971 6.423 0.451 7.855 6.423 1.432 28 7.861 9.154 1.293 8.374 9.154 0.780 29 10.289 11.251 0.962 12.936 11.251 1.685 30 10.355 12.684 2.329 14.549 12.684 1.865 Table 3 Estimated and rice yield (ton ha − 1 ) for period 2 Field Code NDVI EVI Estimated Actual Diff. Estimated Actual Diff. 16 8.913 7.804 1.108 7.542 7.804 0.262 17 5.454 5.947 0.494 6.325 5.947 0.377 18 6.688 6.015 0.673 6.792 6.015 0.777 19 8.528 7.780 0.748 6.904 7.780 0.876 20 7.594 8.871 1.277 9.980 8.871 1.109 21 3.679 5.431 1.752 4.789 5.431 0.642 22 13.328 12.876 0.452 12.883 12.876 0.007 23 8.083 9.118 1.035 10.270 9.118 1.152 24 7.412 8.677 1.265 7.778 8.677 0.899 25 14.872 12.236 2.635 12.821 12.236 0.584 26 11.442 10.690 0.752 11.496 10.690 0.806 27 6.269 6.372 0.104 4.453 6.372 1.919 28 12.361 10.756 1.605 11.492 10.756 0.737 29 11.164 12.192 1.028 12.205 12.192 0.013 30 6.801 9.163 2.363 10.154 9.163 0.991 In the second period, from 15 rice fields with a total actual harvest of 133.929 tons ha − 1 , the EVI-based model gave a total estimated result of 135.883 tons ha − 1 (+ 1.954 tons ha − 1 ). Meanwhile, the NDVI-based model provides a total estimate of 132.587 tons ha − 1 (-1.342 tons ha − 1 ). Even though the total NDVI model estimates are close to the actual total, the EVI model provides a slightly more accurate average difference for each rice plot, 0.743 tons ha − 1 , while the NDVI model is 1.153 tons ha − 1 . Research conducted by Son et al ., in [23] also stated that the EVI-based model was slightly more accurate than the NDVI-based in estimating rice harvest in the Mekong River Delta, Vietnam. This shows that in the future, rice harvest measurements can be calculated using the EVI approach [40], [41]. 4. Conclusion Overall, both NDVI and EVI demonstrate strong potential for pre-harvest rice yield estimation, with EVI consistently outperforming NDVI in terms of both the coefficient of determination (R²) and RMSE across two periods, despite limited data availability. These findings highlight the valuable role of remote sensing in supporting sustainable agriculture by enabling more accurate, timely, and resource-efficient crop monitoring and yield forecasting Declarations Author Contributions Conceptualization, I.I., F.L.H., and M.F.R; software and methodology, F.L.H., M.F.R., and R.P.R; data curation, M.F.R., and R.P.R., validation, G.R.P., I.I., F.L.H., W.S., M.P.R., A.B.P.; investigation, I.I., W.S., F.L.H, A.B.P.; writing-review and editing, G.R.P., I.I., W.S., H.H., A.B.P., visualisation, F.L.H., and M.F.R; supervision, I.I., W.S., H.H., A.B.P. All authors have read and agreed to the published version of the manuscript. Funding The authors did not receive any funding. Data availability The data used to support the findings of this study are available from the corresponding authors upon request. Ethics approval and consent to participate Not applicable. Conflicts of interest The authors have no conflicts of interest to declare that are relevant to the content of this article. Consent for publication Not applicable. Ethics declaration Not applicable. Competing interests The authors declare no competing interests. Clinical trial declarations Not applicable. Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/. References BPS-Statistics Indonesia. Statistical Yearbook of Indonesia 2024. BPS-Statistics Indonesia, 2024; 52: 1-804. BPS-Statistics Indonesia. Statistical Yearbook of Indonesia 2025. BPS-Statistics Indonesia, 2025; 53: 1-852. David W, Alkausar S. Statistik Pertanian Organik Indonesia. 2023;1: 1-84. Martawijaya S, Montgomery, RD. Bureaucrats as Entrepreneurs: A Case Study of Organic Rice Production in East Java. 2004; 40(2): 243-252. Hazra KK, Swain DK, Bohra A, Singh SS, Kumar N, Nath CP. Organic rice: potential production strategies, challenges and prospects. Org. Agric. 2018; 8(1): 39–56. http://doi.org/10.1007/s13165-016-0172-4. Jouzi Z, Azadi H, Taheri F, Zarafshani K, Gebrehiwot K, Van Passel S, Lebailly P. Organic farming and small-scale farmers: main opportunities and challenges. Ecol. Econ . 2017; 132: 144–154. http://doi.org/10.1016/j.ecolecon.2016.10.016. Silva JV, Pede VO, Radanielson AM, Kodama W, Duarte A, de Guia AH, Malabayabas AJB, Pustika AB, Argosubekti N, Vithoonjit D, Hieu PTM, Pame ARP, Singleton GR, Stuart AM. Revisiting yield gaps and the scope for sustainable intensification for irrigated lowland rice in Southeast Asia. Agricultural Systems. 2022; 198: 103383. http://doi.org/10.1016/j.agsy.2022.103383. Sihi D, Dari B, Yan Z, Sharma DK, Pathak H, Sharma OP, Nain L. Assessment of Water Quality in Indo-Gangetic Plain of South-Eastern Asia under Organic vs. Conventional Rice Farming. Water. 2020;12(4): 960. http://doi.org/10.3390/w12040960. Birkhofer K, Smith HG, Rundlöf M. Environmental Impacts of Organic Farming. Encyclopedia of Life Sciences. Wiley. 2016: 1–7. http://doi.org/10.1002/9780470015902.a0026341. Lee KS, Choe YC, Park SH. Measuring the environmental effects of organic farming: A meta-analysis of structural variables in empirical research. J. Environ. Manage. 2015; 162: 263–274. http://doi.org/10.1016/j.jenvman.2015.07.021. Mas FS, Handal AJ, Rohrer RE, Viteri ET. Health and Safety in Organic Farming: A Qualitative Study. J. Agromedicine, 2018; 23(1): 92–104. http://doi.org/10.1080/1059924X.2017.1382409. Costa C, Lestón JG, Costa S, Coelho P, Silva S, Pingarilho M, Valdiglesias V, Mattei F, Dall’Armi V, Bonassi S, Laffon B, Snawder J, Teixeira JP. Is organic farming safer to farmers’ health? A comparison between organic and traditional farming. Toxicol. Lett. 2014; 230(2): 166–176. http://doi.org/10.1016/j.toxlet.2014.02.011. Mie A, Andersen HR, Gunnarsson S, Kahl J, Kesse-Guyot E, Rembiałkowska E, Quaglio G, Grandjean Human health implications of organic food and organic agriculture: a comprehensive review. Environ. Heal . 2017; 16(1): 111. http://doi.org/10.1186/s12940-017-0315-4. Rahmann G, Ardakani MR, Barberi P, Boehm H, Canali S, et al. Organic Agriculture 3.0 is innovation with research. Org. Agric. 2017; 7(3): 169–197. http://doi.org/10.1007/s13165-016-0171-5. Devkota KP, Pasuquin E, Elmido-Mabilangan A, Dikitanan R, Singleton GR, Stuart AM, Vithoonjit D, Vidiyangkura L, Pustika AB, et al. Economic and environmental indicators of sustainable rice cultivation: A comparison across intensive irrigated rice cropping systems in six Asian countries. Ecological Indicators. 2019; 105:199–214. https://doi.org/10.1016/j.ecolind.2019.05.029 Rudiana E, Rustiadi E, Firdaus M, Dirgahayu D. Pengembangan Penggunaan Penginderaan Jauh untuk Estimasi Produksi Padi (Studi Kasus Kabupaten Bekasi). J. Ilmu Tanah dan Lingkung. 2019; 19(1): 6–12. https://doi.org/10.29244/jitl.19.1.6-12. Huete, AR, Liu HQ, Batchily K, van Leeuwen W. A comparison of vegetation indices over a global set of TM images for EOS-MODIS. Remote Sensing of Environment. 1997; 59(3): 440-451. https://doi.org/10.1016/S0034-4257(96)00112-5 Jin Z, Azzari G, Lobell DB. Use time series NDVI and EVI to develop dynamic crop growth metrics for yield modeling. Ecological Indicators. 2021; 121: 106987. https://doi.org/10.1016/j.ecolind.2020.106987 Zhang Q, Liu K, Wu X. Rice Yield Estimation Using Multi-Temporal Remote Sensing Data and Machine Learning: A Case Study of Jiangsu, China. Agriculture. 2024; 14(4): 638. https://doi.org/10.3390/agriculture14040638 Xie G, Niculescu S. Mapping Crop Types Using Sentinel-2 Data Machine Learning and Monitoring Crop Phenology with Sentinel-1 Backscatter Time Series in Pays de Brest, Brittany, France. Remote Sens . 2022; 14(18): 1–27. https://doi.org/10.3390/rs14184437. Karlson M, Ostwald M, Bayala B, Bazie HR, Ouedraogo AS, Soro B, Sanou J, Reese H. The Potential of Sentinel-2 for Crop Production Estimation in a Smallholder Agroforestry Landscape, Burkina Faso. Front. Environ. Sci. 2020; 8: 1-13. https://doi.org/10.3389/fenvs.2020.00085. Parra M, Parra L, Mostaza-Colado D, Mauri P, Lloret J. Using Satellite Imagery and Vegetation Indices to Monitor and Quantify the Performance of Different Varieties of Camelina Sativa. GEOProcessing 2020: The Twelfth International Conference on Advanced Geographic Information Systems, Applications, and Services. 2020: 48–53. Son NT, Chen CF, Chen CR, Minh VQ, Trung NH. A comparative analysis of multitemporal MODIS EVI and NDVI data for large-scale rice yield estimation. Agricultural and Forest Meteorology. 2014; 197: 52–64. https://doi.org/10.1016/j.agrformet.2014.06.007. Islam M D, Di L, Qamer FM, Shrestha S, Guo L, Lin L, Mayer TJ, Phalke AR. Rapid rice yield estimation using integrated remote sensing and meteorological data and machine learning. Remote Sensing. 2023; 15(9), 2374. https://doi.org/10.3390/rs15092374. Wang F, Yao X, Xie L, Zheng J, Xu T. Rice Yield Estimation Based on Vegetation Index and Florescence Spectral Information from UAV Hyperspectral Remote Sensing . Remote Sensing. 2021; 13(17): 3390; https://doi.org/10.3390/rs13173390. Yanti D, Khoirunnisa S, Rusnam, Stiyanto E. Estimation of Rice Productivity Using The Normalized Difference Vegetation Index (NDVI) Algorithm (Case Study of Gunung Talang District, Solok Regency). Jurnal Keteknikan Pertanian. 2022; 10(3): 241–253. https://doi.org/10.19028/jtep.010.3.240-252. Hafizh SA, Cahyono AB, Wibowo A. Penggunaan Algoritma Ndvi Dan Evi pada Citra Multispektral Untuk Analisa Pertumbuhan Padi (Studi Kasus : Kabupaten Indramayu, Jawa Barat). Geoid . 2013; 9(1): 7. https://doi.org/10.12962/j24423998.v9i1.733. Sulistiono W, Sugihono C, Hidayat Y, Assagaf M, Abu HL, Brahmantiyo B, Wahab A. Physiology and early growth of introduced robusta coffee clones in wet climate drylands in Bacan, North Maluku. IOP Conf. Series: Earth and Environmental Science. 2021; 824: 012030. https://doi.org/10.1088/1755-1315/824/1/012030. Soriano-González J, Angelats E, Martínez-Eixarch M, Alcaraz C. Monitoring rice crop and yield estimation with Sentinel-2 data. Field Crops Research. 2022; 281: 108507. https://doi.org/10.1016/j.fcr.2022.108507. European Space Agency. Sentinel-2 User Handbook. ESA Stand. Doc. 2015; 2(1): 64. Rouse JW, Haas JRH, Schell JA, Deering D. Monitoring Vegetation Systems in The Great Plains with ERTS. in 3rd Earth Resources Technology Satellite (ERTS) Symposium. 1974: 309-317. [Online]. Available: https://ntrs.nasa.gov/citations/19740022592 Panek E, Gozdowski D. Analysis of relationship between cereal yield and NDVI for selected regions of Central Europe based on MODIS satellite data. Remote Sens. Appl. Soc. Environ. 2020; 17(August 2019):100286. https://doi.org/10.1016/j.rsase.2019.100286. Huang J, Wang H, Dai Q, Han D. Analysis of NDVI data for crop identification and yield estimation. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 2014; 7(11): 4374-4384. https://doi.org/10.1109/JSTARS.2014.2334332. Aslan M F, Sabanci K, Aslan B. Artificial intelligence techniques in crop yield estimation based on Sentinel-2 data: A comprehensive survey. Sustainability. 2024; 16(18): 8277. https://doi.org/10.3390/su16188277. Huete A, Didan K, Miura T, Rodriguez E, Gao X, Ferreira L. Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sens. Environ. 2002; 83(1-2): 195–213. https://doi.org/10.1016/S0034-4257(02)00096-2. Thapa S, Rudd JC, Xue W, Bhandari M, Reddy SK, Jessup KE, et al. Use of NDVI for characterizing winter wheat response to water stress in a semi-arid environment. J. Crop Improv. 2019; 33(5): 633–648. 2019. https://doi.org/10.1080/15427528.2019.1648348. Schober P, Schwarte LA. Correlation coefficients: Appropriate use and interpretation. Anesth. Analg. 2018;126(5): 1763-1768. https://doi.org/10.1213/ANE.0000000000002864. Jensen JR. Introductory Digital Image Processing: A Remote Sensing Perspective, 4th Editio. Glenview, Illinois: Pearson Education, Inc. 2015. Candra ED, Hartono, Wicaksono P. Above Ground Carbon Stock Estimates of Mangrove Forest Using Worldview-2 Imagery in Teluk Benoa, Bali. IOP Conf. Ser. Earth Environ. Sci. 2016; 47 (2016) 012014. https://doi.org/10.1088/1755-1315/47/1/012014. Khan S, Mazhar T, Shahzad T, Khan MA, Guizani S, Hamam H, Future of sustainable farming: exploring opportunities and overcoming barriers in drone-IoT integration, Discover Sustainability. 2024; 5:470. https://doi.org/10.1007/s43621-024-00736-y. Franch B, Bautista AS, Fita D, Rubio C, Tarrazó-Serrano D, Sánchez A, Skakun S, Vermote E, Becker-Reshef I, Uris A. Within-field rice yield estimation based on sentinel-2 satellite data. Remote Sensing. 2021; 13(20), 4095. https://doi.org/10.3390/rs13204095. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 08 Oct, 2025 Read the published version in Discover Sustainability → Version 1 posted Editorial decision: Revision requested 19 May, 2025 Reviews received at journal 12 May, 2025 Reviews received at journal 09 May, 2025 Reviewers agreed at journal 08 May, 2025 Reviewers agreed at journal 08 May, 2025 Reviewers invited by journal 08 May, 2025 Submission checks completed at journal 06 May, 2025 First submitted to journal 26 Apr, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6150129","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":455094076,"identity":"ec1fde0a-44b9-499f-854f-59f595f8fd80","order_by":0,"name":"Gagad Restu Pratiwi","email":"","orcid":"","institution":"National Research and Innovation Agency (BRIN), Kawasan Sains Teknologi Dr. (H.C) Ir. H. Soekarno","correspondingAuthor":false,"prefix":"","firstName":"Gagad","middleName":"Restu","lastName":"Pratiwi","suffix":""},{"id":455094078,"identity":"488bdd24-5d54-44a2-8bbe-12f4fc08d1a1","order_by":1,"name":"Indarto Indarto","email":"","orcid":"","institution":"University of Jember","correspondingAuthor":false,"prefix":"","firstName":"Indarto","middleName":"","lastName":"Indarto","suffix":""},{"id":455094080,"identity":"6de74786-f957-45f0-bd0e-d4468c57e685","order_by":2,"name":"Farid Lukman Hakim","email":"","orcid":"","institution":"University of Jember","correspondingAuthor":false,"prefix":"","firstName":"Farid","middleName":"Lukman","lastName":"Hakim","suffix":""},{"id":455094082,"identity":"4b8af322-c933-4fa0-a2dc-dfdf8f1d5e3b","order_by":3,"name":"Wawan Sulistiono","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA2UlEQVRIiWNgGAWjYDACZhDBA8QSzAdApAxBHTwILWwJIJKHsBY4S4LHAFUAF7BnZz724IOMXR7/7J7Pr27UWPAwsB8+ugG/w9jSDWfwJBdL3Dm7zTrnGNBhPGlpN/Br4TGT5uFhTmy4kbvNOIcNqEWCx4wYLfWJ82/kPDPO+Ue8lsOJG27kMD/ObSNGy2G2NMkZPMcTN95IM2PO7ZPgYSPkF/b+w8ckPvZUJ867kfz4c863Ojl+9sPH8GoBA8YeMMUmASYJKgeDH2CS+QNxqkfBKBgFo2CkAQC7uz9uMPvY2wAAAABJRU5ErkJggg==","orcid":"","institution":"National Research and Innovation Agency (BRIN), Kawasan Sains Teknologi Dr. (H.C) Ir. H. Soekarno","correspondingAuthor":true,"prefix":"","firstName":"Wawan","middleName":"","lastName":"Sulistiono","suffix":""},{"id":455094084,"identity":"70cf77f4-dd23-47f5-894d-160c6cb107a4","order_by":4,"name":"M. Fakhri Roivansah","email":"","orcid":"","institution":"University of Jember","correspondingAuthor":false,"prefix":"","firstName":"M.","middleName":"Fakhri","lastName":"Roivansah","suffix":""},{"id":455094085,"identity":"04a8ac3d-2b1b-4129-b434-01590e5623b3","order_by":5,"name":"Rizki Putra Ramadhan","email":"","orcid":"","institution":"University of Jember","correspondingAuthor":false,"prefix":"","firstName":"Rizki","middleName":"Putra","lastName":"Ramadhan","suffix":""},{"id":455094086,"identity":"03c62c2b-4bd5-4939-ac7c-7cbb663c9104","order_by":6,"name":"Hendra Helmanto","email":"","orcid":"","institution":"National Research and Innovation Agency (BRIN), Kawasan Sains Teknologi Dr. (H.C) Ir. H. Soekarno","correspondingAuthor":false,"prefix":"","firstName":"Hendra","middleName":"","lastName":"Helmanto","suffix":""},{"id":455094087,"identity":"c557edb5-3935-4b08-a165-fa64ab9df937","order_by":7,"name":"Arlyna B Pustika","email":"","orcid":"","institution":"National Research and Innovation Agency (BRIN), Kawasan Sains Teknologi Dr. (H.C) Ir. H. Soekarno","correspondingAuthor":false,"prefix":"","firstName":"Arlyna","middleName":"B","lastName":"Pustika","suffix":""}],"badges":[],"createdAt":"2025-03-04 02:38:11","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6150129/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6150129/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s43621-025-01743-3","type":"published","date":"2025-10-08T15:57:23+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":82657478,"identity":"333d7516-9079-4598-b785-e01a80af3695","added_by":"auto","created_at":"2025-05-13 19:17:09","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":458142,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eStudy area\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6150129/v1/bf2ccbeafadcdeb2cc9134e6.jpeg"},{"id":82656746,"identity":"2151bf3a-cd3d-4f11-aca5-050ed7b7ee88","added_by":"auto","created_at":"2025-05-13 19:01:09","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":45305,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFlowchart of the research process\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-6150129/v1/f77aad1fdbb99d51e87d8e63.png"},{"id":82656750,"identity":"707a6a76-3f44-438d-96c1-c7d64f1ae75d","added_by":"auto","created_at":"2025-05-13 19:01:09","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1075612,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eNDVI Period 1 (May 10\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003eth \u003c/strong\u003e\u003c/sup\u003e\u003cstrong\u003e2021)\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6150129/v1/07dbc0c3c65f34d7813f61c3.jpeg"},{"id":82656752,"identity":"7535b1db-4ddf-4442-bd21-6887020962c1","added_by":"auto","created_at":"2025-05-13 19:01:09","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1245125,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eEVI Period 1 (May 10\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003eth \u003c/strong\u003e\u003c/sup\u003e\u003cstrong\u003e2021)\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6150129/v1/3925e3204657cb232333b697.jpeg"},{"id":82656756,"identity":"1659e62a-e807-4069-9dbb-828eafbda693","added_by":"auto","created_at":"2025-05-13 19:01:09","extension":"jpeg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":170524,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eNDVI Period 2 (October 12\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003eth\u003c/strong\u003e\u003c/sup\u003e\u003cstrong\u003e, 2021)\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage5.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6150129/v1/5b875d7f2e3a1dbc39bfd6ec.jpeg"},{"id":82657158,"identity":"fc0fd1f2-f63f-4521-aa7f-06880ee50d95","added_by":"auto","created_at":"2025-05-13 19:09:09","extension":"jpeg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":1238406,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eEVI Period 2 (October 12\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003eth\u003c/strong\u003e\u003c/sup\u003e\u003cstrong\u003e, 2021)\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage6.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6150129/v1/ac7208eca0f4be548a964289.jpeg"},{"id":82656747,"identity":"8d921917-4239-4ec9-a6cf-60deb40ce2e2","added_by":"auto","created_at":"2025-05-13 19:01:09","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":178245,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eLinear regression results between rice yield and vegetation indexes\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"7.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6150129/v1/db5b4dbac90b34b2f6c8cc3d.jpg"},{"id":93419685,"identity":"e0c50e25-b62a-456f-8086-f5f92d67fe58","added_by":"auto","created_at":"2025-10-13 16:05:54","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":5365830,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6150129/v1/66a0d030-0fe6-4094-8c0e-ac9efe7c8d22.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Rice Yield Estimation Using Vegetation Indexes (NDVI and EVI) Derived from Sentinel-2 Imagery for Sustainable Agriculture","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eIn Asia, rice has been the staple food in many countries, with production continually striving to increase each year. China leads global rice production with 145.95\u0026nbsp;million metric tons, while Indonesia ranks fourth with 34\u0026nbsp;million metric tons in the 2022/2023 period [1]. In 2024, Indonesia's rice harvest area reached approximately 10.05\u0026nbsp;million hectares, yielding 53.14\u0026nbsp;million tons of rice [2]. The highest rice production was recorded in five provinces: East Java, Central Java, West Java, South Sulawesi, and South Sumatra. East Java ranked first in rice production in 2024, with 9.27\u0026nbsp;million tons of dry unhusked rice, equivalent to 5.353\u0026nbsp;million tons of milled rice. In 2022, East Java reported organic rice production of 828.767 tons from 423.98 hectares [3]. The report aligns with the fact that Lumajang became the first district in East Java to implement organic rice cultivation in 2001. Organic rice farming in the Lumajang subdistricts of Pronojiwo and Candipuro became the focus of government regulation and support. This support was reflected in the development of organic rice initiatives, funding projects to subsidise organic farmers, and the issuance of operational guidelines for organic rice cultivation [4].\u003c/p\u003e \u003cp\u003eOrganic farming has become an alternative agricultural production practice practised by farmers worldwide. Furthermore, the market for organic products is increasing in Asia today. One is due to the negative impact of pesticide-derived products on agriculture, unsustainable farming practices, and increasing public health concerns [5],[6]. Research results show that conventional (or inorganic) agricultural practice negatively impacts the environment and people's health. Along with precision agriculture technologies for site-specific management (i.e., more output with the same or fewer inputs), sustainability factors related to inefficient fertiliser usage and low profitability from high input levels should also be considered. Thus, it can be closed sustainably by boosting productivity and decreasing its environmental impact [7]. The effect appeared as the decrease of water quality in the surrounding ecosystem [8], reduce soil fertility and health [9], produce more emission [10], done more negative risk to the health of farm and other peoples [11], [12], [13], and other negative impacts [14]. Concerning research done in six Asian countries that produce rice as a staple food, improved management techniques are desperately needed for fertiliser (N, P, K), irrigation water efficiency, and reducing the frequency of pesticide application without compromising production and profitability. It will result in a minor environmental impact, and rice yield and profit gaps may be bridged sustainably [15].\u003c/p\u003e \u003cp\u003eHowever, several constraints are still confronting organic farming practices, i.e., the local farmer\u0026rsquo;s mindset and habit, the land conversion, and the lack of incentive from the government. Yield prediction is urgently concerned with answering the question of the feasibility of the organic farming system. Why did the production decrease during the first conversion of land resources from chemical to organic farming? How about the production cost for inorganic agriculture? Historically, the estimation process relied heavily on conventional methods, involving agricultural extension officers collecting total production data from farmers. However, this traditional approach proved inefficient, time-consuming, and resource-intensive, contributing to challenges such as delayed information, high costs, labour intensity, and various technical and non-technical issues [16].\u003c/p\u003e \u003cp\u003eRemote sensing technologies allow for the assessment of crop productivity over vast spatial and temporal dimensions, and they have completely changed agricultural surveillance. Using vegetation indices like the NDVI and EVI, satellite sensors like Landsat, MODIS, and Sentinel-2 offer reliable multispectral data that may be used to evaluate agricultural conditions. The reflectance of vegetation in the red and near-infrared wavelengths, which have a significant correlation with photosynthetic activity and chlorophyll concentration, is the source of these indices [17]. Because of its ease of use and efficiency in identifying green vegetation, NDVI is frequently utilized; but, in regions with high biomass, it may become saturated. EVI, an enhanced measure that minimizes atmospheric distortions and soil background noise by using blue light reflectance and correction factors, was created to get around this restriction [17].\u003c/p\u003e \u003cp\u003eResearchers and policymakers can more accurately anticipate crop growth stages and final yields by examining time-series NDVI and EVI data throughout the growing season. This is especially true when paired with ground truth data and machine learning models [18][19]. In addition to aiding in production forecasts, these satellite-derived indicators improve precision farming methods, facilitating more effective resource management and policy formulation. Addressing issues with food security requires their incorporation into agricultural systems, especially in areas with little access to ground-based monitoring [19].\u003c/p\u003e \u003cp\u003eAdvanced remote sensing technology, exemplified by Sentinel-2 imagery as an optical satellite, demonstrates its versatility across various agricultural domains [20], extending to the assessment of organic rice production [21]. Additionally, high-resolution Sentinel-2 imagery has proven helpful for detecting fruit on trees using image processing [22]. Research related to the use of satellites to estimate crop productivity has been conducted previously [23],[24],[25],[26]. However, this has never been done before in the East Java region, especially the Lumajang region. Developing a model for estimating organic rice production utilises satellite image data, enabling the anticipation of total harvests before the actual harvesting period. Identifying rice growth involves applying NDVI (Normalized Difference Vegetation Index) and EVI (Enhanced Vegetation Index) vegetation indices. NDVI aids in estimating chlorophyll content in leaves, while EVI contributes to discerning variations in canopy structure [27]. This function is important because the chlorophyll content differs significantly in the interaction between clones/varieties and the growing environment/fertilization [28]. It was further explained that the NDVI and NDWI-GAO methods were used to identify key phenological stages (i.e., tillering, heading date, and maturity) and field conditions (i.e., hydroperiod), demonstrating strong potential for estimating rice yields across different cultivars [29].\u003c/p\u003e \u003cp\u003eUsing NDVI and EVI, derived from Sentinel-2, proves invaluable in efficiently determining the organic rice planting season and projecting total rice production. Therefore, research is imperative to estimate organic rice production in Candipuro District, Lumajang Regency, by optimising Sentinel-2 imagery usage and implementing NDVI and EVI algorithms. The aim is to estimate organic rice production by combining NDVI and EVI.\u003c/p\u003e"},{"header":"2. Material and methods","content":"\u003cp\u003eThe data utilised in this research consist of : (1) Sentinel-2 level 2A images (10m spatial resolution), (2) administrative boundary maps, (3) rice production data of the Candipuro Sub-District, and (4) field areas acquired through field surveys. Sentinel-2 is a pair of Earth observation satellites designed for the Copernicus program by the European Space Agency (ESA). Sentinel-2, a high-resolution multispectral imager, can take comprehensive images of the Earth's surface in 13 spectral bands, especially in the visible, near-infrared, and shortwave infrared ranges. These satellites, which operate in tandem with Sentinel-2a and Sentinel-2B, offer numerous revisits to the same region, ensuring excellent temporal resolution for tracking dynamic environmental changes, broad swath coverage, free and open data policy, and spatial resolutions between 10 and 60 meters [30].\u003c/p\u003e \u003cp\u003eThe temporal scope of the data spans two planting periods in 2021, precisely May 10th, 2021, and October 12th, 2021, aligning closely with the harvest periods. The distribution pattern of organic rice was subsequently derived from cropping image results, including three villages: Penanggal, Sumbermujur, and Tambahrejo. A comprehensive survey identified thirty (30) farmers' rice fields covering 14.146 hectares (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows the spatial distribution of organic rice field plots used in this study. The yellow plots on Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e display as GPS-surveyed shapefiles overlaid on the Arcgis basemap image. The map illustrates the spatial layout of the agricultural areas analysed. All spatial data were projected using the WGS 84 / UTM Zone 49s coordinate system.\u003c/p\u003e \u003cp\u003eThe study area is predominantly characterized by Regosol soil, with inclusions of Andosol and Gleysol. The cropping pattern in the study area within one year follows a Rice \u0026ndash; Fallow \u0026ndash; Rice sequence. The first planting period occurred between February and May 2021, and the second planting period took place between July and October 2021. The rice variety used was IR64. The climate type in Candipuro District, Lumajang Regency, East Java in 2021 was classified as a tropical monsoon climate (Am) according to the K\u0026ouml;ppen climate classification, characterized by distinct wet and dry seasons. The average temperature in Candipuro ranges from 23\u0026deg;C to 31\u0026deg;C throughout the year. The relative humidity is generally high, ranging from 70\u0026ndash;90%, peaking during the rainy season. Candipuro District is located at an elevation of approximately 322 meters above sea level.\u003c/p\u003e \u003cp\u003eThe data processing procedure consists of multiple stages (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The procedure involves the creation of a shapefile for mapping the distribution of organic rice fields, transforming vegetation indices, and developing a linear regression model. This investigation employs two distinct vegetation indices: NDVI and EVI [31]. NDVI and EVI are widely used Indices for crop type mapping [32], [33], [34].\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eEVI optimises the blue channel to rectify NDVI value deficiencies due to atmospheric aerosol content, and it enhances canopy background precision by applying the L factor (soil/land conditions) for more accurate NDVI values [35]. The NDVI and EVI values of the plating areas are calculated as follows:\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\n$$\\:\\varvec{N}\\varvec{D}\\varvec{V}\\varvec{I}=\\:\\frac{\\varvec{N}\\varvec{I}\\varvec{R}-\\varvec{R}\\varvec{e}\\varvec{d}}{\\varvec{N}\\varvec{I}\\varvec{R}+\\varvec{R}\\varvec{e}\\varvec{d}}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equ2\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ2\" name=\"EquationSource\"\u003e\n$$\\:\\varvec{E}\\varvec{V}\\varvec{I}=\\varvec{G}\\:\\times\\:\\frac{(\\varvec{N}\\varvec{I}\\varvec{R}-\\varvec{R}\\varvec{e}\\varvec{d})}{(\\varvec{N}\\varvec{I}\\varvec{R}+{\\varvec{C}}^{1}\\times\\:\\varvec{R}\\varvec{e}\\varvec{d}-\\:\\:{\\varvec{C}}^{2}\\times\\:\\varvec{B}\\varvec{l}\\varvec{u}\\varvec{e})}+\\varvec{L}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e2\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWhere G is a gain factor (2.5), C\u003csub\u003e1\u003c/sub\u003e and C\u003csub\u003e2\u003c/sub\u003e are the coefficients of the aerosol resistance term (6 and 7.5), and L is the canopy background adjustment (1) [35].\u003c/p\u003e \u003cp\u003eThe data analysis incorporates various statistical methodologies, including Spearman's test, statistical analysis, and regression model analysis. In constructing the model, rice field plots 1\u0026ndash;15 serve as inputs to formulate a mathematical equation, which will subsequently be employed to make predictions for plots 16\u0026ndash;30. The model's capabilities are then assessed through the coefficient of determination, and its validation is conducted using RMSE to gauge the accuracy of the prediction results compared to the actual harvest values. The statistical analysis was performed using Excel software\u003c/p\u003e"},{"header":"3. Results and discussion","content":"\u003cp\u003eBased on the NDVI and EVI transformations, the results are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e6\u003c/span\u003e and Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The NDVI values during Period 1 ranged from 0.645 to 0.834, with an average of 0.75, while during Period 2, they ranged from 0.5 to 0.726, with an average of 0.606. The relatively lower average values in Period 2 compared to Period 1 may be attributed to the younger age of the crops [36].\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eFigure 5. NDVI Period 2 (October 12th, 2021)\u003c/b\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eColor Explanation on the Map:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eRed: Indicates areas with high index values (close to 0.887938), which may suggest very dense vegetation, areas with optimal crop growth, or very good land conditions.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eYellow: Represents areas with moderate index values. This means vegetation is present, but not as optimal as in the red areas, possibly due to soil, water conditions, or the crop growth stage.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eGreen: Indicates areas with low index values (close to 0.00512821), which could suggest sparse vegetation, bare soil, or areas with poor crop growth.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eColor Gradient (Green to Red): Reflects the transition of vegetation conditions, ranging from areas with poor plant health (green) to those with very good plant health (red).\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eThe black boxes on the map likely indicate research plot areas or ground truth observation points.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eIndex value and rice yield on periods 1 and 2 of 2021\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eField Plot Code\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eArea (ha)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e \u003cp\u003ePeriod 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003ePeriod 2\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eYield (\u003c/b\u003etons ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e\u003cb\u003e)\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eNDVI\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eEVI\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003eYield (\u003c/b\u003etons ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e\u003cb\u003e)\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003eNDVI\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003eEVI\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003csub\u003e1\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.217\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12.875\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.720\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.672\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e13.709\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.699\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.736\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.383\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8.974\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.801\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.574\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e8.535\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.583\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.544\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003csub\u003e3\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.706\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.211\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.748\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.495\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e6.487\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.549\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.639\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003csub\u003e4\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.031\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.520\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.649\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.508\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4.567\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.514\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.504\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003csub\u003e5\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.331\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.952\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.688\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.465\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4.345\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.553\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.539\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003csub\u003e6\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.630\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.623\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.711\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.573\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e7.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.663\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.545\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003csub\u003e7\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.400\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9.252\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.761\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.491\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e10.871\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.706\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.666\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003csub\u003e8\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.541\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.125\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.670\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.515\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4.918\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.541\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.579\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003csub\u003e9\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.956\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.707\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.645\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.490\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.919\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.549\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.486\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003csub\u003e10\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.120\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10.713\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.725\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.516\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e20.347\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.726\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.792\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003csub\u003e11\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.064\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13.521\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.809\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.677\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e15.690\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.657\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.719\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003csub\u003e12\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.226\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10.484\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.827\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.685\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e9.826\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.560\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.513\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003csub\u003e13\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.420\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8.988\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.771\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.580\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e9.655\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.571\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.694\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003csub\u003e14\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.232\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12.208\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.834\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.705\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e10.145\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.680\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.640\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003csub\u003e15\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.126\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12.568\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.793\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.665\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e14.336\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.642\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.633\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003csub\u003e16\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.285\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10.709\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.825\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.671\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e7.804\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.600\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.566\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003csub\u003e17\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.782\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.628\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.659\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.509\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e5.947\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.534\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.537\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003csub\u003e18\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.693\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.253\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.680\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.517\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e6.015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.557\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.549\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003csub\u003e19\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.062\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12.993\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.805\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.695\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e7.780\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.592\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.551\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003csub\u003e20\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.136\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9.861\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.716\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.596\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e8.871\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.574\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.624\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003csub\u003e21\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.199\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10.993\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.744\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.638\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e5.431\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.501\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003csub\u003e22\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.144\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12.911\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.829\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.712\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e12.876\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.683\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.692\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003csub\u003e23\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.203\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12.223\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.790\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.739\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e9.118\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.584\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.631\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003csub\u003e24\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.121\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8.561\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.775\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.536\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e8.677\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.571\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.572\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003csub\u003e25\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.298\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10.694\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.768\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.659\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e12.236\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.712\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.691\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003csub\u003e26\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.344\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9.803\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.769\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.595\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e10.690\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.647\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.660\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003csub\u003e27\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.657\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.423\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.684\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.553\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e6.372\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.549\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.493\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003csub\u003e28\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.404\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9.154\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.727\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.568\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e10.756\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.665\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.660\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003csub\u003e29\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.260\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11.251\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.783\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.702\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e12.192\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.642\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.676\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003csub\u003e30\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.041\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12.684\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.784\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.750\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e9.163\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.559\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.628\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAdditionally, the limited availability of cloud-free imagery makes it challenging to find images closer to the harvest season. The EVI values during Period 1 ranged from 0.465 to 0.75, with an average of 0.602, whereas during Period 2, they ranged from 0.486 to 0.792, with an average of 0.61.\u003c/p\u003e \u003cp\u003eThe Spearman's rank correlation test (ρ) was also conducted to assess the association between harvest outcomes and NDVI and EVI values in each period. The analysis results indicate a strong correlation between NDVI and EVI values for each period and the harvest quantity [37]. The ρ-values show a strong correlation for NDVI in Periods 1 and 2, which are 0.714 and 0.814, respectively, and for EVI, which are 0.796 and 0.793, respectively.\u003c/p\u003e \u003cp\u003eMeanwhile, in the Pearson correlation results (r), the values of the indices and harvest outcomes in both periods also indicate a strong association. The NDVI correlation (r) in periods 1 and 2 is 0.782 and 0.785, respectively. The EVI correlation (r) is slightly better, at 0.82 and 0.83, respectively\u003c/p\u003e \u003cp\u003eThe linear regression results between rice harvest outcomes and index values in each period are presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e7\u003c/span\u003e. The findings indicate that EVI yields a better coefficient of determination (R\u0026sup2;) than NDVI in both periods. In both periods, EVI achieves R\u0026sup2; values of 0.6924 and 0.6773, respectively. This suggests that the EVI model can predict 69.24% and 67.73% of actual field harvest outcomes through the equations y\u0026thinsp;=\u0026thinsp;0.0164x\u0026thinsp;+\u0026thinsp;0.4579 and y\u0026thinsp;=\u0026thinsp;0.02x\u0026thinsp;+\u0026thinsp;0.4027.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe best results were obtained in EVI period 2, with an RMSE value of 0.0340. The estimated harvest value resulting from the EVI model equation is more accurate and closer to the actual value than NDVI. The correlation, determination, and RMSE values of EVI are more precise than those of NDVI. This is partly because EVI can reduce disturbances due to atmospheric and soil factors through existing corrections, making it more sensitive in recording biomass levels [38], [39].\u003c/p\u003e \u003cp\u003eThe results of calculating estimated harvest values for each index and period are presented in Tables\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and \u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e below. Comparing the estimated and actual harvest values, we can see that EVI provides better results than NDVI. In the first period, from 15 rice field plots with a total harvest of 150.139 tons ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, the EVI estimation model gave a total estimate of 156.846 tons ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e (+\u0026thinsp;6.707 tons ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) with the average difference for each rice field compared to the actual value amounting to 0.936 tons ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e. Meanwhile, the NDVI estimation model provides a total estimated result of 136.788 tons ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e (-13.351 tons ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) with an average difference for each rice field plot to the actual value of 1.268 tons ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eEstimated and actual rice yield (ton ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) for period 1\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eField Code\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eNDVI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003eEVI\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEstimated\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eActual\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDiff.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eEstimated\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eActual\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eDiff.\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e12.129\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10.709\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.420\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e11.887\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e10.709\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.179\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.887\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.628\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.741\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6.365\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e5.628\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.737\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.800\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.253\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.453\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6.639\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e6.253\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.386\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11.280\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12.993\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.713\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e12.689\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e12.993\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.305\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7.392\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9.861\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.468\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e9.330\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e9.861\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.530\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8.607\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10.993\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.386\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e10.742\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e10.993\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.251\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e12.309\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12.911\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.602\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e13.261\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e12.911\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.350\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10.600\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12.223\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.623\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e14.174\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e12.223\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.952\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9.976\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8.561\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.415\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e7.275\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e8.561\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.286\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9.635\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10.694\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.060\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e11.479\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e10.694\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.785\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9.699\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9.803\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.104\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e9.290\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e9.803\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.513\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.971\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.423\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.451\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e7.855\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e6.423\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.432\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7.861\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9.154\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.293\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e8.374\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e9.154\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.780\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10.289\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11.251\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.962\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e12.936\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e11.251\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.685\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10.355\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12.684\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.329\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e14.549\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e12.684\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.865\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eEstimated and rice yield (ton ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) for period 2\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eField Code\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eNDVI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003eEVI\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEstimated\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eActual\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDiff.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eEstimated\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eActual\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eDiff.\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8.913\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7.804\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.108\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e7.542\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e7.804\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.262\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.454\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.947\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.494\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6.325\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e5.947\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.377\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6.688\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.673\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6.792\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e6.015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.777\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8.528\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7.780\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.748\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6.904\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e7.780\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.876\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7.594\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8.871\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.277\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e9.980\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e8.871\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.109\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.679\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.431\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.752\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.789\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e5.431\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.642\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e13.328\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12.876\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.452\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e12.883\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e12.876\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8.083\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9.118\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.035\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e10.270\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e9.118\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.152\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7.412\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8.677\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.265\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e7.778\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e8.677\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.899\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e14.872\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12.236\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.635\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e12.821\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e12.236\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.584\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11.442\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10.690\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.752\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e11.496\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e10.690\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.806\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6.269\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.372\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.104\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.453\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e6.372\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.919\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e12.361\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10.756\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.605\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e11.492\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e10.756\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.737\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11.164\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12.192\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.028\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e12.205\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e12.192\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.013\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6.801\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9.163\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.363\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e10.154\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e9.163\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.991\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eIn the second period, from 15 rice fields with a total actual harvest of 133.929 tons ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, the EVI-based model gave a total estimated result of 135.883 tons ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e (+\u0026thinsp;1.954 tons ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e). Meanwhile, the NDVI-based model provides a total estimate of 132.587 tons ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e (-1.342 tons ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e). Even though the total NDVI model estimates are close to the actual total, the EVI model provides a slightly more accurate average difference for each rice plot, 0.743 tons ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, while the NDVI model is 1.153 tons ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e. Research conducted by Son \u003cem\u003eet al\u003c/em\u003e., in [23] also stated that the EVI-based model was slightly more accurate than the NDVI-based in estimating rice harvest in the Mekong River Delta, Vietnam. This shows that in the future, rice harvest measurements can be calculated using the EVI approach [40], [41].\u003c/p\u003e"},{"header":"4. Conclusion","content":"\u003cp\u003eOverall, both NDVI and EVI demonstrate strong potential for pre-harvest rice yield estimation, with EVI consistently outperforming NDVI in terms of both the coefficient of determination (R\u0026sup2;) and RMSE across two periods, despite limited data availability. These findings highlight the valuable role of remote sensing in supporting sustainable agriculture by enabling more accurate, timely, and resource-efficient crop monitoring and yield forecasting\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e Conceptualization, I.I., F.L.H., and M.F.R; software and methodology, F.L.H., M.F.R., and R.P.R; data curation, M.F.R., and R.P.R., validation, G.R.P., I.I., F.L.H., W.S., M.P.R., A.B.P.; investigation, I.I., W.S., F.L.H, A.B.P.; writing-review and editing, G.R.P., I.I., W.S., H.H., A.B.P., visualisation, F.L.H., and M.F.R; supervision, I.I., W.S., H.H., A.B.P. All authors have read and agreed to the published version of the manuscript. \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003eThe authors did not receive any funding.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e The data used to support the findings of this study are available from the corresponding authors upon request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u0026nbsp;\u003c/strong\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of interest\u0026nbsp;\u003c/strong\u003eThe authors have no conflicts of interest to declare that are relevant to the content of this article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u0026nbsp;\u003c/strong\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics declaration\u0026nbsp;\u003c/strong\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u0026nbsp;\u003c/strong\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial declarations\u003c/strong\u003e Not applicable.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eOpen Access\u003c/strong\u003e This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article\u0026rsquo;s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article\u0026rsquo;s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBPS-Statistics Indonesia. Statistical Yearbook of Indonesia 2024. BPS-Statistics Indonesia, 2024; 52: 1-804.\u003c/li\u003e\n\u003cli\u003eBPS-Statistics Indonesia. Statistical Yearbook of Indonesia 2025. BPS-Statistics Indonesia, 2025; 53: 1-852.\u003c/li\u003e\n\u003cli\u003eDavid W, Alkausar S. Statistik Pertanian Organik Indonesia. 2023;1: 1-84.\u003c/li\u003e\n\u003cli\u003eMartawijaya S, Montgomery, RD. Bureaucrats as Entrepreneurs: A Case Study of Organic Rice Production in East Java. 2004; 40(2): 243-252.\u003c/li\u003e\n\u003cli\u003eHazra KK, Swain DK, Bohra A, Singh SS, Kumar N, Nath CP. Organic rice: potential production strategies, challenges and prospects. Org. Agric. 2018; 8(1): 39\u0026ndash;56. http://doi.org/10.1007/s13165-016-0172-4.\u003c/li\u003e\n\u003cli\u003eJouzi Z, Azadi H, Taheri F, Zarafshani K, Gebrehiwot K, Van Passel S, Lebailly P. Organic farming and small-scale farmers: main opportunities and challenges. Ecol. Econ\u003cem\u003e.\u003c/em\u003e 2017; 132: 144\u0026ndash;154. http://doi.org/10.1016/j.ecolecon.2016.10.016.\u003c/li\u003e\n\u003cli\u003eSilva JV, Pede VO, Radanielson AM, Kodama W, Duarte A, de Guia AH, Malabayabas AJB, Pustika AB, Argosubekti N, Vithoonjit D, Hieu PTM, Pame ARP, Singleton GR, Stuart AM. Revisiting yield gaps and the scope for sustainable intensification for irrigated lowland rice in Southeast Asia. Agricultural Systems. 2022; 198: 103383. http://doi.org/10.1016/j.agsy.2022.103383.\u003c/li\u003e\n\u003cli\u003eSihi D, Dari B, Yan Z, Sharma DK, Pathak H, Sharma OP, Nain L. Assessment of Water Quality in Indo-Gangetic Plain of South-Eastern Asia under Organic vs. Conventional Rice Farming. Water. 2020;12(4): 960. http://doi.org/10.3390/w12040960.\u003c/li\u003e\n\u003cli\u003eBirkhofer K, Smith HG, Rundl\u0026ouml;f M. Environmental Impacts of Organic Farming. Encyclopedia of Life Sciences. Wiley. 2016: 1\u0026ndash;7. http://doi.org/10.1002/9780470015902.a0026341.\u003c/li\u003e\n\u003cli\u003eLee KS, Choe YC, Park SH. Measuring the environmental effects of organic farming: A meta-analysis of structural variables in empirical research. J. Environ. Manage. 2015; 162: 263\u0026ndash;274. http://doi.org/10.1016/j.jenvman.2015.07.021.\u003c/li\u003e\n\u003cli\u003eMas FS, Handal AJ, Rohrer RE, Viteri ET. Health and Safety in Organic Farming: A Qualitative Study. J. Agromedicine, 2018; 23(1): 92\u0026ndash;104. http://doi.org/10.1080/1059924X.2017.1382409.\u003c/li\u003e\n\u003cli\u003eCosta C, Lest\u0026oacute;n JG, Costa S, Coelho P, Silva S, Pingarilho M, Valdiglesias V, Mattei F, Dall\u0026rsquo;Armi V, Bonassi S, Laffon B, Snawder J, Teixeira JP. Is organic farming safer to farmers\u0026rsquo; health? A comparison between organic and traditional farming. Toxicol. Lett. 2014; 230(2): 166\u0026ndash;176. http://doi.org/10.1016/j.toxlet.2014.02.011.\u003c/li\u003e\n\u003cli\u003eMie A, Andersen HR, Gunnarsson S, Kahl J, Kesse-Guyot E, Rembiałkowska E, Quaglio G, Grandjean Human health implications of organic food and organic agriculture: a comprehensive review. Environ. Heal\u003cem\u003e.\u003c/em\u003e 2017; 16(1): 111. http://doi.org/10.1186/s12940-017-0315-4.\u003c/li\u003e\n\u003cli\u003eRahmann G, Ardakani MR, Barberi P, Boehm H, Canali S, et al. Organic Agriculture 3.0 is innovation with research. Org. Agric. 2017; 7(3): 169\u0026ndash;197. http://doi.org/10.1007/s13165-016-0171-5.\u003c/li\u003e\n\u003cli\u003eDevkota KP, Pasuquin E, Elmido-Mabilangan A, Dikitanan R, Singleton GR, Stuart AM, Vithoonjit D, Vidiyangkura L, Pustika AB, et al. Economic and environmental indicators of sustainable rice cultivation: A comparison across intensive irrigated rice cropping systems in six Asian countries. Ecological Indicators. 2019; 105:199\u0026ndash;214. https://doi.org/10.1016/j.ecolind.2019.05.029\u003c/li\u003e\n\u003cli\u003eRudiana E, Rustiadi E, Firdaus M, Dirgahayu D. Pengembangan Penggunaan Penginderaan Jauh untuk Estimasi Produksi Padi (Studi Kasus Kabupaten Bekasi). \u003cem\u003eJ. Ilmu Tanah dan Lingkung.\u003c/em\u003e 2019; 19(1): 6\u0026ndash;12. https://doi.org/10.29244/jitl.19.1.6-12.\u003c/li\u003e\n\u003cli\u003eHuete, AR, Liu HQ, Batchily K, van Leeuwen W. A comparison of vegetation indices over a global set of TM images for EOS-MODIS. Remote Sensing of Environment. 1997; 59(3): 440-451. https://doi.org/10.1016/S0034-4257(96)00112-5\u003c/li\u003e\n\u003cli\u003eJin Z, Azzari G, Lobell DB. Use time series NDVI and EVI to develop dynamic crop growth metrics for yield modeling. Ecological Indicators. 2021; 121: 106987. https://doi.org/10.1016/j.ecolind.2020.106987\u003c/li\u003e\n\u003cli\u003eZhang Q, Liu K, Wu X. Rice Yield Estimation Using Multi-Temporal Remote Sensing Data and Machine Learning: A Case Study of Jiangsu, China. Agriculture. 2024; 14(4): 638. https://doi.org/10.3390/agriculture14040638\u003c/li\u003e\n\u003cli\u003eXie G, Niculescu S. Mapping Crop Types Using Sentinel-2 Data Machine Learning and Monitoring Crop Phenology with Sentinel-1 Backscatter Time Series in Pays de Brest, Brittany, France. Remote Sens\u003cem\u003e.\u003c/em\u003e 2022; 14(18): 1\u0026ndash;27. https://doi.org/10.3390/rs14184437.\u003c/li\u003e\n\u003cli\u003eKarlson M, Ostwald M, Bayala B, Bazie HR, Ouedraogo AS, Soro B, Sanou J, Reese H. The Potential of Sentinel-2 for Crop Production Estimation in a Smallholder Agroforestry Landscape, Burkina Faso. Front. Environ. Sci. 2020; 8: 1-13. https://doi.org/10.3389/fenvs.2020.00085.\u003c/li\u003e\n\u003cli\u003eParra M, Parra L, Mostaza-Colado D, Mauri P, Lloret J. Using Satellite Imagery and Vegetation Indices to Monitor and Quantify the Performance of Different Varieties of Camelina Sativa. GEOProcessing 2020: The Twelfth International Conference on Advanced Geographic Information Systems, Applications, and Services. 2020: 48\u0026ndash;53.\u003c/li\u003e\n\u003cli\u003eSon NT, Chen CF, Chen CR, Minh VQ, Trung NH. A comparative analysis of multitemporal MODIS EVI and NDVI data for large-scale rice yield estimation. Agricultural and Forest Meteorology. 2014; 197: 52\u0026ndash;64. https://doi.org/10.1016/j.agrformet.2014.06.007.\u003c/li\u003e\n\u003cli\u003eIslam M D, Di L, Qamer FM, Shrestha S, Guo L, Lin L, Mayer TJ, Phalke AR. Rapid rice yield estimation using integrated remote sensing and meteorological data and machine learning. Remote Sensing. 2023; 15(9), 2374. https://doi.org/10.3390/rs15092374.\u003c/li\u003e\n\u003cli\u003eWang F, Yao X, Xie L, Zheng J, Xu T. Rice Yield Estimation Based on Vegetation Index and Florescence Spectral Information from UAV Hyperspectral Remote Sensing\u003cstrong\u003e. \u003c/strong\u003eRemote Sensing. 2021; 13(17): 3390; https://doi.org/10.3390/rs13173390.\u003c/li\u003e\n\u003cli\u003eYanti D, Khoirunnisa S, Rusnam, Stiyanto E. Estimation of Rice Productivity Using The Normalized Difference Vegetation Index (NDVI) Algorithm (Case Study of Gunung Talang District, Solok Regency). Jurnal Keteknikan Pertanian. 2022; 10(3): 241\u0026ndash;253. https://doi.org/10.19028/jtep.010.3.240-252.\u003c/li\u003e\n\u003cli\u003eHafizh SA, Cahyono AB, Wibowo A. Penggunaan Algoritma Ndvi Dan Evi pada Citra Multispektral Untuk Analisa Pertumbuhan Padi (Studi Kasus : Kabupaten Indramayu, Jawa Barat). \u003cem\u003eGeoid\u003c/em\u003e. 2013; 9(1): 7. https://doi.org/10.12962/j24423998.v9i1.733.\u003c/li\u003e\n\u003cli\u003eSulistiono W, Sugihono C, Hidayat Y, Assagaf M, Abu HL, Brahmantiyo B, Wahab A. Physiology and early growth of introduced robusta coffee clones in wet climate drylands in Bacan, North Maluku. IOP Conf. Series: Earth and Environmental Science. 2021; 824: 012030. https://doi.org/10.1088/1755-1315/824/1/012030.\u003c/li\u003e\n\u003cli\u003eSoriano-Gonz\u0026aacute;lez J, Angelats E, Mart\u0026iacute;nez-Eixarch M, Alcaraz C. Monitoring rice crop and yield estimation with Sentinel-2 data. Field Crops Research. 2022; 281: 108507. https://doi.org/10.1016/j.fcr.2022.108507.\u003c/li\u003e\n\u003cli\u003eEuropean Space Agency. Sentinel-2 User Handbook. ESA Stand. Doc. 2015; 2(1): 64.\u003c/li\u003e\n\u003cli\u003eRouse JW, Haas JRH, Schell JA, Deering D. Monitoring Vegetation Systems in The Great Plains with ERTS. in 3rd Earth Resources Technology Satellite (ERTS) Symposium. 1974: 309-317. [Online]. Available: https://ntrs.nasa.gov/citations/19740022592\u003c/li\u003e\n\u003cli\u003ePanek E, Gozdowski D. Analysis of relationship between cereal yield and NDVI for selected regions of Central Europe based on MODIS satellite data. Remote Sens. Appl. Soc. Environ. 2020; 17(August 2019):100286. https://doi.org/10.1016/j.rsase.2019.100286.\u003c/li\u003e\n\u003cli\u003eHuang J, Wang H, Dai Q, Han D. Analysis of NDVI data for crop identification and yield estimation. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 2014; 7(11): 4374-4384. https://doi.org/10.1109/JSTARS.2014.2334332.\u003c/li\u003e\n\u003cli\u003eAslan M F, Sabanci K, Aslan B. Artificial intelligence techniques in crop yield estimation based on Sentinel-2 data: A comprehensive survey. Sustainability. 2024; 16(18): 8277. https://doi.org/10.3390/su16188277.\u003c/li\u003e\n\u003cli\u003eHuete A, Didan K, Miura T, Rodriguez E, Gao X, Ferreira L. Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sens. Environ. 2002; 83(1-2): 195\u0026ndash;213. https://doi.org/10.1016/S0034-4257(02)00096-2.\u003c/li\u003e\n\u003cli\u003eThapa S, Rudd JC, Xue W, Bhandari M, Reddy SK, Jessup KE, et al. Use of NDVI for characterizing winter wheat response to water stress in a semi-arid environment. J. Crop Improv. 2019; 33(5): 633\u0026ndash;648. 2019. https://doi.org/10.1080/15427528.2019.1648348.\u003c/li\u003e\n\u003cli\u003eSchober P, Schwarte LA. Correlation coefficients: Appropriate use and interpretation. Anesth. Analg. 2018;126(5): 1763-1768. https://doi.org/10.1213/ANE.0000000000002864.\u003c/li\u003e\n\u003cli\u003eJensen JR. Introductory Digital Image Processing: A Remote Sensing Perspective, 4th Editio. Glenview, Illinois: Pearson Education, Inc. 2015.\u003c/li\u003e\n\u003cli\u003eCandra ED, Hartono, Wicaksono P. Above Ground Carbon Stock Estimates of Mangrove Forest Using Worldview-2 Imagery in Teluk Benoa, Bali. IOP Conf. Ser. Earth Environ. Sci. 2016; 47 (2016) 012014. https://doi.org/10.1088/1755-1315/47/1/012014.\u003c/li\u003e\n\u003cli\u003eKhan S, Mazhar T, Shahzad T, Khan MA, Guizani S, Hamam H, Future of sustainable farming: exploring opportunities and overcoming barriers in drone-IoT integration, Discover Sustainability. 2024; 5:470. https://doi.org/10.1007/s43621-024-00736-y.\u003c/li\u003e\n\u003cli\u003eFranch B, Bautista AS, Fita D, Rubio C, Tarraz\u0026oacute;-Serrano D, S\u0026aacute;nchez A, Skakun S, Vermote E, Becker-Reshef I, Uris A. Within-field rice yield estimation based on sentinel-2 satellite data. Remote Sensing. 2021; 13(20), 4095. https://doi.org/10.3390/rs13204095.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"discover-sustainability","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"disu","sideBox":"Learn more about [Discover Sustainability](https://www.springer.com/43621)","snPcode":"","submissionUrl":"","title":"Discover Sustainability","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Crop Yield, Estimation, EVI, NDVI, Rice","lastPublishedDoi":"10.21203/rs.3.rs-6150129/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6150129/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eOrganic farming has emerged as an alternative agricultural production practice, accompanied by a growing market for organic products. Advanced remote sensing technology, exemplified by Sentinel-2 imagery as an optical satellite, can efficiently determine the rice planting season and project total rice production. It also enables the monitoring of plant health, irrigation, and fertiliser management, and environmental quality in organic cultivation areas. This study explored two vegetation indices, namely NDVI (Normalized Difference Vegetation Index) and EVI (Enhanced Vegetation Index). The study was conducted on 30 agricultural parcels in Candipuro District, Lumajang Regency, East Java, Indonesia. NDVI and EVI values were derived from Sentinel-2 imagery corresponding to each harvesting date; therefore, two harvesting periods were used in this study. Statistical indices\u0026mdash;including the Spearman-rho test, correlation analysis, linear regression, coefficient of determination, and RMSE (Root Mean Square Error)\u0026mdash;were used to compare rice yield estimates from satellite imagery with actual measurement results. The findings conclude that the EVI index provided the most accurate prediction, with R\u0026sup2; \u0026asymp; 0.7 and RMSE\u0026thinsp;=\u0026thinsp;0.03 for the second period. Both NDVI and EVI demonstrated promising outcomes for pre-harvest rice yield estimation, with EVI showing slightly superior performance, although data support remains limited.\u003c/p\u003e","manuscriptTitle":"Rice Yield Estimation Using Vegetation Indexes (NDVI and EVI) Derived from Sentinel-2 Imagery for Sustainable Agriculture","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-13 19:01:04","doi":"10.21203/rs.3.rs-6150129/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-05-19T11:36:58+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-05-12T05:30:01+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-05-09T08:36:35+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"5162441486834690738524020938351485961","date":"2025-05-09T01:11:16+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"28894593000109989970587330332077363356","date":"2025-05-08T09:26:40+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-05-08T08:37:01+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-05-06T12:18:07+00:00","index":"","fulltext":""},{"type":"submitted","content":"Discover Sustainability","date":"2025-04-26T07:02:19+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"discover-sustainability","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"disu","sideBox":"Learn more about [Discover Sustainability](https://www.springer.com/43621)","snPcode":"","submissionUrl":"","title":"Discover Sustainability","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"0f64142a-d2b9-430c-a310-bb82e8c111cf","owner":[],"postedDate":"May 13th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-10-13T16:00:25+00:00","versionOfRecord":{"articleIdentity":"rs-6150129","link":"https://doi.org/10.1007/s43621-025-01743-3","journal":{"identity":"discover-sustainability","isVorOnly":false,"title":"Discover Sustainability"},"publishedOn":"2025-10-08 15:57:23","publishedOnDateReadable":"October 8th, 2025"},"versionCreatedAt":"2025-05-13 19:01:04","video":"","vorDoi":"10.1007/s43621-025-01743-3","vorDoiUrl":"https://doi.org/10.1007/s43621-025-01743-3","workflowStages":[]},"version":"v1","identity":"rs-6150129","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6150129","identity":"rs-6150129","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}