Time-series analysis of Leaf Area Index and Land Surface Temperature Association using Sentinel-2 and Landsat OLI data

Time-series analysis of Leaf Area Index and Land Surface Temperature Association using Sentinel-2 and Landsat OLI data | 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 Time-series analysis of Leaf Area Index and Land Surface Temperature Association using Sentinel-2 and Landsat OLI data Ali Yasin, Abebe Ali, Nurhussen Ahmed This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4672963/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 28 Sep, 2024 Read the published version in Environmental Systems Research → Version 1 posted 20 You are reading this latest preprint version Abstract Background: Understanding the complex relationship between vegetation dynamics and Land Surface Temperature (LST) is crucial for comprehending ecosystem functioning, climate change impacts, and sustainable land management. Hence, this study conducts a time-series analysis of Leaf Area Index (LAI) and LST derived from Sentinel-2 and Landsat Operational Land Imager (OLI) data. LAI data was generated using Sentinel-2 imagery processed with the SNAP toolbox, while Landsat OLI data was utilized for precise LST calculations. Mann-Kendall test was used to detect trends in the time series data. Results: The trends of LAI were statistically significant at P-values of 0.05 and 0.1 for annual and seasonal trends, respectively. The mean LST trends were statistically insignificant throughout the study period except for the summer season at a P-value of 0.07. The correlation between LAI and LST was weak (R 2 = 0.36) during crop-growing seasons, but moderate in winter (R 2 = 0.46) and autumn (R 2 = 0.41). Conclusion: The findings of this research clarify the complex relationships between variations in surface temperature and vegetation growth patterns, providing insight into the environmental mechanisms driving the dynamics of localized ecosystems. The study underscores the implications of these findings for informed decision-making in sustainable land management, biodiversity conservation, and climate change mitigation strategies. Leaf Area Index Land Surface Temperature Land Use Management Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 1. Background of the Study Understanding the dynamics of vegetation and Land Surface Temperature (LST) holds paramount implications for ecological, climate change assessment, and land resource management studies. Leaf Area Index (LAI), a critical biophysical variable measuring the total area of leaves relative to the land surface, plays a pivotal role in comprehending land surface processes associated with vegetation dynamics and climate modeling (Avdan & Jovanovska, 2016 ; Mwangi et al., 2018 ). It provides essential insights into the impacts of various environmental factors on vegetation (Wang et al., 2019 ). Similarly, LST, another vital variable linked to vegetation dynamics, is directly influenced by vegetation conditions (Guechi et al., 2021 ; Zhao-Liang et al., 2013). A comprehensive global vegetation analysis spanning 31 years (1982–2013) observed across all continents revealed a persistent browning trend on Earth since the 1990s (Pan et al., 2018 ). Nonetheless, other studies have indicated a contrasting greening trend, primarily driven by human land-use practices. For example, Park et al., ( 2019 ) demonstrated substantial contributions to the greening trend from China and India, with China accounting for 25% of the global increase in leaf area and India exhibiting an increase exceeding 35% since 2000. The ongoing browning trend in global vegetation since the 1990s emphasizes the impact of climate on vegetation. However, the concurrent greening trend, exemplified by significant leaf area increases in China and India, suggests that human land-use practices significantly influence vegetation (Pan et al., 2018 ). The relationship between LAI and LST has been extensively studied and established as inverse relationship (Hussain et al., 2023 ; Jin & Zhang, 2002 ; Mwangi et al., 2018 ; Rasul et al., 2020 ). Several researchers have concurred on this inverse relationship, attributing it to the cooling effect of vegetation on the land surface, where an increase in the number of plants (measured by LAI) corresponds to a decrease in LST (Nega et al., 2019 ). This cooling effect arises from the transpiration of water by plants, regulating the temperature of the surrounding environment (Schwaab et al., 2021 ). Additionally, this relationship is subject to the influence of other factors such as solar radiation, atmospheric conditions, and soil moisture (Liu et al., 2016 ). Generally, heightened solar radiation and reduced atmospheric moisture levels tend to elevate LST (Cheruy et al., 2017 ; Han et al., 2020 ; Jiang et al., 2023 ), whereas increased vegetation and soil moisture assist in lowering LST (Imran et al., 2021 ; Li et al., 2022 ; Liu et al., 2016 ). Previous studies have utilized various satellite datasets to investigate the connection between LAI and LST, including MODIS (Hussain et al., 2023 ; Miller et al., 2022 ; Mwangi et al., 2018 ; Rasul et al., 2020 ; Reygadas et al., 2020 ; Schwaab et al., 2021 ; Tesemma et al., 2015 ). Despite the growing importance of remote sensing data in environmental monitoring and land management (Franklin, 1983 ; Skidmore, 2002 ; Skidmore et al., 1997 ), there remains a critical gap in the understanding of the temporal dynamics and interrelationship between LAI and LST as derived from Sentinel-2 and Landsat Operational Land Imager (OLI) data. While both LAI and LST are crucial indicators of ecosystem health and vitality (Imran et al., 2022 ), the combined analysis and temporal association of these metrics using time-series data from these two prominent satellite platforms have been relatively underexplored. Recognizing the interconnected influence of vegetation dynamics and surface temperature on local ecosystems, there is a pressing need to elucidate the intricate associations and potential feedback mechanisms between these critical biophysical parameters. This research aims to bridge this gap by conducting a meticulous time-series analysis of LAI and LST derived from Sentinel-2 and Landsat OLI data. Through exploring the intricate connections between changes in LAI and LST, our research aims to offer valuable understanding of the fundamental ecological mechanisms at play and their significance for promoting sustainable land management techniques and strategies for mitigating the effects of climate change. Through this integrated approach, we aim to contribute to the refinement of remote sensing methodologies and the advancement of our understanding of local-scale environmental dynamics. The findings of this study highlight the significance of employing high-resolution satellite imagery to examine the connection between vegetation status and climate at a local scale, particularly in areas characterized by diverse landscape features that can impact the association between LAI and LST. It offers a comprehensive evaluation of their relationship and emphasizes the potential of high-resolution satellite imagery in understanding the complex interaction between vegetation and climate. The results contribute to the existing knowledge on this association and provide valuable insights into its potential consequences on vegetation dynamics and climate modeling at the local scale. Thus, the research has two main objectives: (a) assessing the time-series trend of LAI and LST derived from Sentinel-2 and Landsat OLI, and (b) evaluating the seasonal and annual correlation between LAI and LST. 2. Materials and Methods 2.1 Description of the Study Area The study was conducted in the lower Millie watershed, situated in Ambasel District within the South Wollo Zone of the Amhara Regional State, Ethiopia (Fig. 1 ). The area encompasses both tropical and subtropical agro-climatic regions. The Millie watershed is positioned between 11.32 0 –11.54 0 latitude and 39.52 0 – 39.68 0 longitude. It covers an area of around 19509 hectares. The elevation of the area ranging from 1427 to 3635 meters above sea level describes the study area possesses multiple agro-climatic zones. In addition, the region exhibits an average temperature varying between 15 to 20º C (Destaw, 2017 ). Data obtained from Kombelcha meteorological station suggests that the study area experiences an annual precipitation ranging of 800 to 1200 mm. The primary rainy season typically spans from June to August, while the short rains characterize the period between April and June. The region experiences its lowest minimum temperatures from August to November, ranging from 11 o C to 12 o C. Conversely, the highest maximum temperatures occur during May and June, varying between 22 o C and 30 o C. The livelihood of the study area is largely reliant on mixed agriculture, with a focus on crop-livestock production, predominantly influenced by rainfall. The primary vegetation found in the research area consists mainly of Eucalyptus trees and different types of long-lasting fruit plants (Desalegn et al., 2023 ). These are commonly used in agroforestry for sustaining livelihoods and creating income-generating opportunities, serving as a means of mitigating the challenges faced by small landholders. Agriculture serves as the dominant occupation, engaging a significant portion of the population. Key crops cultivated in the area include Wheat, Barley, Teff, and various pulse crops (Desalegn et al., 2023 ). 2.1. Data processing 2.2.1 Sentinel 2 Data and Preprocessing The estimation of LAI utilizing the Sentinel Application Platform (SNAP R2020a) toolbox involved the use of Sentinel-2 satellite images. More specifically, the dataset comprised over 36 cloud-free Level 1C data from 2016 to 2018 and Level 2A data from 2019 to 2022. The Level 2A data, accessible free of charge from the Copernicus SciHub website ( https://scihub.copernicus.eu/ ), is both geometrically and atmospherically corrected, providing a Bottom-Of-Atmosphere (BOA) corrected reflectance product (Wang et al., 2019 ). However, preprocessing required for the Level 1C images before biophysical retrieval (Sola et al., 2018 ). To convert the Level 1C data to Level 2A, the Sen2Cor atmospheric correction algorithm was employed, a method demonstrated in the work of Kganyago et al., ( 2020 ). This algorithm effectively addresses atmospheric distortions in Level-1C data, producing BOA reflectance images, with the option for terrain and cirrus correction (Clevers et al., 2017 ). Standardizing the analysis, all atmospherically corrected images were resampled to a 20-m pixel size, ensuring uniformity across all available bands of the Sentinel-2 images. Within the SNAP toolbox, three biophysical processors were available, namely S2_20m, S2_10m, and LANDSAT8 (Mourad et al., 2020 ). For this particular study, the S2_20m processor was utilized to estimate the LAI of the study area. In assessing the LAI trend from 2016 to 2022, a total of 27 Sentinel-2 images were employed. These images were acquired during the same seasons as Landsat imagery, with a tolerance of up to 10 days. 2.2.2 Landsat level 2 LST product and preprocessing The Landsat 8–9 Collection 2 Level 2 LST is computed from the Landsat 8–9 Collection 2 Level 1 Thermal Infrared Sensor (TIRS) Band 10, using Top of Atmosphere (TOA) Reflectance, TOA Brightness Temperature (BT), Advanced Space borne Thermal Emission and Reflection Radiometer (ASTER), Global Emissivity Dataset (GED) data, ASTER Normalized Difference Vegetation Index (NDVI) data, and atmospheric profiles of geopotential height, specific humidity, and air temperature extracted from reanalysis data (Sayler, 2022 ). The Digital Number (DN) values are then transformed to LST using a multiplication factor and an additive number, as shown in Eq. 1 (Sayler et al., 2023 ). The output unit for LST is in Kelvin, which is converted to Celsius by subtracting 273.15. To ensure consistency with the LAI pixel size, the LST data were resampled to a 20-meter pixel size. Cloud-free Landsat imagery was obtained from NASA's website, comprising Level 2 Landsat 8 and 9 OLI, to estimate the LST of the Millie watershed from 2016 to 2022. Over 37 Level 2 Landsat images were utilized over 7 consecutive years, including multiple images for each season (winter, spring, summer, and autumn). The Landsat satellite data has a spatial resolution of 30 meters, and the imagery can be downloaded free of charge from the USGS Earth Explorer ( https://earthexplorer.usgs.gov/ ). 2.2.3 LAI Retrieval The SNAP Toolbox's biophysical variable retrieval algorithm for Sentinel-2, as described by Weiss & Baret ( 2016 ), is built on the PROSAIL radiative transfer model for canopy architecture and the Artificial Neural Networks (ANN) algorithm. Leveraging instant top-of-canopy reflectance data from eight Sentinel-2 bands, along with viewing zenith, solar zenith, and relative azimuth angles, the SNAP biophysical processor parameterizes PROSAIL to simulate bottom-of-atmosphere reflectance (Xie et al., 2019 ). Subsequently, the ANN algorithm, trained on the simulated reflectance, is applied to the chosen Sentinel-2 bands to retrieve the biophysical variable, as explained by Weiss & Baret ( 2016 ). This algorithm is categorized as "nonspecific," indicating its capability to deliver reasonable biophysical variable performance for various types of vegetation (Kamenova & Dimitrov, 2021 ; Kganyago et al., 2020 ; Mourad et al., 2020 ; Xie et al., 2019 ). For reference, Table 1 outlines the required bands for LAI estimation using the SNAP toolbox. The accuracy of Leaf Area Index (LAI) derived from the SNAP toolbox was evaluated to ascertain its reliability in practical applications. The study findings revealed a strong correlation (R 2 = 0.74) between LAI values obtained through the SNAP toolbox and those acquired through direct field measurements. Figure 2 visually depicted the validation results, illustrating the consistency and coherence between estimated and ground-measured LAI values. The Root Mean Square Error (RMSE) of 0.53 indicated an acceptable deviation between SNAP-derived and actual field-measured LAI values, emphasizing the tool's capability to provide reliable estimates. The calculated bias of -0.31, though suggesting a slight underestimation, was within an acceptable range, reinforcing the credibility of SNAP toolbox-derived LAI values. Overall, the strong correlation, low RMSE, and minimal bias collectively underscored the reliability and accuracy of the SNAP toolbox-derived LAI values, affirming its practical utility in estimating and predicting Leaf Area Index in the study area. These findings contribute to the validation and endorsement of the SNAP toolbox's effectiveness in generating accurate LAI data for further time-series analysis. Table 1 Sentinel 2 spectral and spatial characteristics of the 8 selected bands Band Number Central WL (nm) Width (nm) Spatial Resolution (m) Band 3 560 35 10 Band 4 665 30 10 Band 5 705 15 20 Band 6 740 15 20 Band 7 783 20 20 Band 8a 865 20 20 Band 11 1610 90 20 Band 12 2190 180 20 2.3 Trends and Correlation Analyses In this study, local-scale changes in LAI, LST, and their interconnectedness were assessed using trend and regression analyses. MATLAB R2020a and R 4.0.3 version software were employed for all statistical operations. Trend Analysis The trend analysis for both annual and seasonal trends relied on the SNAP-derived LAI and Landsat Level 2 LST products. Estimations for annual and seasonal trends were conducted using Zhang's method and the Yue Pilon method found in the "zyp" package within R software (X. L. Wang & Swail, 2001 ; Yue et al., 2002). This approach is advantageous for its accurate confidence intervals and robustness against outliers. Moreover, the widely recognized Mann-Kendall test was used to detect trends in time series data, as commonly seen in environmental studies, hydrology, and climatology (Hamed, 2008 ). The Mann-Kendall test allows for the assessment of monotonic trends in data, where a significant test statistic leads to the rejection of the null hypothesis (Yue & Wang, 2004 ). While the Mann-Kendall test is effective in handling tied observations and outliers (F. Wang et al., 2020 ), it does have limitations, such as its inability to detect nonlinear trends or abrupt changes in trend direction (Blain, 2013 ). To mitigate these limitations, the Sen's slope estimator and the Theil-Sen estimator were combined with the Mann-Kendall test, providing a comprehensive analysis of trend behavior. Additionally, given the requirement of non-auto correlated data, the data underwent testing in R Studio using the plot method of autocorrelation via the acf function. Eq. 2 was employed in the Mann-Kendall test for the analysis of the time series. Let X1, X2, Xn represent the variables denoting n points of LAI and LST. The Mann-Kendall test (S) is then applied to these variables. The Mann-Kendall test furnishes insights regarding the trend direction, if any, within the time series. A negative test result implies a decrease in both LAI and LST, while a positive value suggests an increase. Conversely, a result of zero supports the null hypothesis of no trend. A positive S value suggests that the later observations in the series are expected to be higher than the earlier ones, while a negative S value suggests the opposite. The variance of S is calculated using Eq. 3. 3. Results 3.1 Seasonal and Annual Trends of LAI and LST (2016–2022) 3.1.1 Seasonal Trend of Mean LAI Figure 3 illustrates the seasonal trends and the trend during the period (trend P) determined for each season from Sentinel 2 images. Throughout the study duration (2016 to 2022), all seasons exhibited significant greening trends, with the exception of the spring season, which displayed a Sen Slope of -0.041. The rate of change in LAI demonstrated significant variation, ranging from 0.02 to 0.08 for the season-to-season trend and from 0.13 to 0.57 for the trend period. Of particular note, the autumn season demonstrated the highest trend (0.082) and trend P (0.576) with a Sen Slope of 0.15. In general, the average LAI for all seasons showcased a notable greening trend over the study period. Table 2 highlights the seasons with a statistically significant trend at a p-value of 0.05 in bold font. Table 2 Z-value, Sen's Slope, P-value, trend, and trendp of the seasonal and annual trends of LAI Seasons Z-value Sen’s Slope P-value trend trendP Winter 1.503 0.055 0.13 0.066 0.464 Spring 1.503 -0.041 0.13 0.020 0.137 Summer 1.503 0.052 0.13 0.052 0.364 Autumn 1.878 0.047 0.05 0.082 0.576 Annual 1.878 0.013 0.05 0.013 0.091 3.1.2 Annual Trend of Mean LAI from 2016 to 2022 The annual trend in mean LAI for the study area is depicted in Fig. 4 . The analysis revealed a substantial greening trend, represented by a Sen's slope of 0.084 and a P-value of 0.05 for the 2016–2022 period. While the year-to-year trend of LAI was 0.013, the trend for the entire period amounted to 0.091. Notably, the highest mean annual LAI in the study area was observed in 2021. 3.1.3 Seasonal Trend of Mean LST The trends in LST for the four seasons are visualized in Fig. 5 . Variations in trend and trendp of LST were observed across seasons. However, the study did not identify any statistically significant seasonal trend in mean LST during the study period (2016–2022), except for the summer season, which exhibited a seasonal trend of LST with a P-value of 0.07. During the spring season, a near-zero trend (trend = 0.1) was observed, while increasing trends were noted in the summer seasons of 2021 and 2022. The season-to-season trend of LST ranged from − 0.031 (autumn season) to 1.5 (summer season), while for the trend period, it varied from − 2.147 (autumn season) to 10.5 (summer season). Table 3 presents the statistical results of the mean LST trends for each season, alongside the annual mean LST trend. Table 3 Z-value, Sen's Slope, P-value, trend, and trendp of the seasonal and annual trends of LST from 2016 to 2022. Seasons Z-value Sen’s Slope P-value trend trendp Winter 0.00 0.265 1 0.265 1.853 Spring 0.00 0.10 1 0.100 0.700 Summer 1.802 1.5 0.07 1.500 10.500 Autumn -0.30 -0.307 0.7 -0.307 -2.147 Annual 1.201 0.466 0.2 0.466 3.260 3.1.4 Annual Trend of Mean LST from 2016 to 2022 The annual trend in the mean LST for the study area was assessed based on the mean annual LST values over the study period, as depicted in Fig. 6 . To ascertain the significance of the observed changes, the Mann-Kendall trend test was conducted at a P-value of 0.1. The study findings indicated an absence of a significant annual trend in mean LST. The year-to-year trend of LST was 0.466, while for the trend period, it was 3.260. Notably, the highest mean annual LST in the study area was observed in 2021, coinciding with the year that demonstrated the highest mean LAI in the study period. Overall, the average annual LST did not display statistically significant changes over the study period, with a P-value of 0.2. 3.2 Correlation 3.2.1 Annual Relationship between LST and LAI The assessment of the association between the LAI and LST was conducted at both annual and seasonal scales. To facilitate this analysis, all LAI and LST seasonal images for each year were stacked and averaged using cell statistics. Similarly, for the seasonal analysis, LAI and LST images for the same season across the seven years were stacked and averaged accordingly. A total of 201 points were used to evaluate the association between LAI and LST at both scales. Overall, the relationship between the two variables demonstrated a moderate correlation, with R-squared (R 2 ) values ranging from 0.35 to 0.49. This indicates that the vegetation characteristics of the study area respond to changes in LST, as depicted in Fig. 7 . Notably, the lowest R 2 value of 0.35 was recorded in 2021, while the highest value was observed in 2020 (R 2 = 0.49). 3.2.2 Seasonal Relationship between LAI and LST The study area exhibited significant spatial and temporal variations in the distribution of LAI and LST. Consequently, the seasonal association between LAI and LST was evaluated to discern the influence of LST on LAI across the winter, spring, summer, and autumn seasons. The results of the linear regression model fitting between LST and LAI are depicted in Fig. 8 . The highest correlation between LAI and LST was observed during the winter season (R 2 = 0.46), while the lowest R 2 value of 0.36 was recorded during the spring season. Overall, the correlation between LST and LAI across all seasons was moderate. Notably, during the crop-growing seasons, specifically summer and spring, the relationship was relatively weaker. However, during the dry season (winter) and autumn when the crops' leaves reached maturity, the correlation between LAI and LST was notably stronger. Table ‎1; R 2 , Adjusted R 2 and P-Value of annual and seasonal correlation between LAI and LST Seasons R 2 Adjusted R 2 P-Value < 2016 0.4119 0.4088 2.20E-16 2017 0.4012 0.3981 2.20E-16 2018 0.4104 0.4074 2.20E-16 2019 0.4763 0.4736 2.20E-16 2020 0.4934 0.4908 2.20E-16 2021 0.3452 0.3418 2.20E-16 2022 0.4594 0.4566 2.20E-16 Winter 0.4602 0.4575 2.20E-16 Spring 0.3580 0.3548 2.20E-16 Summer 0.3552 0.3520 2.20E-16 Autumn 0.4065 0.4036 2.20E-16 4. Discussions This study examined the temporal variation in LAI and LST by mapping LAI and LST in the Millie watershed from 2016 to 2022 using Sentinel-2 LAI and Landsat LST products. We investigated the temporal trends of mean LAI and LST in the study area at both seasonal and annual scales. Our analysis indicated a generally significant increasing trend in mean LAI during the study period, whereas LST exhibited an insignificant positive trend. 4.1 Seasonal and Annual Trends of LAI Our findings revealed a notable greening trend in the Millie watershed from 2016 to 2022, with positive Sen's slope values observed in the winter, summer, and autumn seasons. These results are consistent with prior research indicating a global increase in LAI during growing seasons (Rasul et al., 2020 ). We attribute this trend to a combination of factors, including natural reforestation, land-use changes, and increased agricultural activity in the region. However, the negative Sen's slope value observed during the spring season suggests that various environmental stressors or other factors may be limiting vegetation growth and productivity at that time. The mean LAI trend exhibited seasonal variation, ranging from 0.020 to 0.082 (Figs. 3 and 4 ). Nevertheless, the trendp (trend during the study period) was consistently robust, ranging from 0.137 to 0.576. These findings suggest that while short-term seasonal trends may exhibit some variability, the overall trend of increasing LAI throughout the study period remains substantial. As reported by SuDCA & Soberland (2015) and Agri Service Ethiopia (ASE) in 2011), agriculture stands as the predominant land use in the study area. Investigating seasonal trends becomes essential due to the fluctuating spatial patterns of seasonal crops influenced by factors such as rainfall and temperature. Our findings solidify the close connection between the trend of LAI and agricultural activities. Seasons marked by abundant rainfall, accompanied by a gradual expansion of cultivated lands, have resulted in higher mean LAI trends. The study area experiences two primary rainy seasons, namely summer and spring. According to Abegaz & Abera ( 2020 ), who investigated temperature and rainfall trends in northeastern Ethiopia, the seasonal rainfall trend in this region, particularly South Wollo, has shown a declining trend in the spring season, relatively stable trends in summer and autumn, and an increasing trend in the winter season. The declining trend in spring season rainfall leads to less cultivation, which negatively impacts LAI. Consequently, the declining trend of mean LAI in the spring season is attributed to inadequate land use management in agricultural activities (Agidew & Singh, 2017 ). The relative increase in mean LAI during the winter season is attributed to increased rainfall, which positively influences rain-fed agriculture (Chuanhua et al., 2023 ; Rasul et al., 2020 ). Hence, the increasing or decreasing trend of LAI can be predicted based on land use management activities, such as agricultural practices (Zhu et al., 2016 ). These results affirm the positive influence of rainfall amounts on LAI (Longhui et al., 2017 ), as most of the greening trends were observed during the growing seasons. 4.2 Seasonal and Annual Trends of LST Throughout the years 2016 to 2022, the average temperature (LST) in the study area did not display any significant seasonal or yearly changes, except for a notable trend observed during the summer season with a 93% confidence level (Figs. 5 and 6 ). A study by Abegaz & Abera ( 2020 ) in South Wollo, Ethiopia, indicated that the summer season's delayed onset, resulting in reduced rainfall and increased air and surface temperatures, could account for the rising trend of LST during summer and the declining LST trend during autumn in the study area. Decreased rainfall and soil moisture have a positive impact on LST by affecting crop cultivation and soil moisture (Jiang et al., 2023 ; Sun & Pinker, 2004 ; Weng et al., 2004 ). These findings support the notion that changes in rainfall amounts, soil moisture, and crop production inversely influence LST by modulating the transmission of electromagnetic radiation to the Earth's surface. 4.3 Seasonal and Annual Association between LAI and LST The relationship between mean LAI and mean LST was evaluated yearly to understand how vegetation responds to variations in LST, as depicted in Figs. 7 and 8 . Our analysis revealed a moderate inverse correlation between mean LAI and LST from 2016 to 2022, with the coefficient of determination (R 2 ) ranging from 0.35 to 0.49. This finding contrasts with the results of Rasul et al. ( 2020 ), who found no significant relationship between LAI and LST in Africa. While the observed moderate negative correlation suggests some sensitivity of the study area's photosynthetic vegetation to LST during the study period, the precise cause behind this relationship requires further investigation. It is possible that variations in vegetation type, land use practices, and climatic conditions contribute to this correlation (Guha & Govil, 2020 ). Understanding the relationship between LST and LAI is essential for sustainable land management and conservation, particularly as changes in LST can influence vegetation dynamics. During the study period, the correlation between LAI and LST in the Millie watershed was assessed seasonally, revealing moderate correlations between the two variables during the summer and spring seasons, with an R 2 of 0.36 (Fig. 8 ). Notably, the study area's growing season aligns with increased mean LAI which is agreed with previous study by (Nazeri et al., 2021 ). The period from March to June is known for the highest temperatures in Ethiopia, with incremental temperature changes during the summer, crucial for crop growth aided by moderate rainfall. Consequently, crop leaf development intensifies during these seasons, contributing to a cooling effect on LST. However, the R 2 is lower during the colder months when crop cultivation is less optimal. These results underscore the significance of various factors, including crop cultivation, precipitation availability, land use practices, and mean maximum temperature, in modulating the seasonal association between LAI and LST. 5. Conclusion This study highlighted a significant greening trend over the study period, attributed to natural reforestation, land-use changes, and agricultural activities. While the LST showed relatively stable patterns, an increase during delayed summer onset was noted. The moderate inverse correlation between LAI and LST underscored vegetation's sensitivity to temperature changes, emphasizing the need for vigilant environmental monitoring and management. These findings stress the importance of understanding the complex interplay between environmental factors, guiding the development of targeted strategies for sustainable land management and conservation in the region. While this study provides valuable insights into the temporal trends and link between LAI and LST in the Millie watershed, there is still a need for further research to better understand the complex relationships between environmental factors and vegetation productivity. One potential avenue for future research is to investigate the impact of land-use changes and management practices on the observed trends in LAI and LST. Declarations Acknowledgements We are thankful to the members of the department of Geography and Environmental Studies at Wollo University who assisted with field data collection. We also extend our gratitude to the reviewers and the academic editor for their valuable and insightful feedback. Authors' contributions AY, AM, and NA designing methodology, AY collecting field data. AY, and AM conceiving the study, and designing the research framework, AY performing statistical analysis and prepared the manuscript, AM, and NA providing supervision throughout the study, AY writing original drift, NA, and AM Validation, AM, and NA reviewing and editing. All authors read and approved the final manuscript. Funding Not applicable Availability of data and materials The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request. Ethics approval and consent to participate Not applicable Consent for publication Not applicable Competing interests The authors declare that they have no competing interests. References Abegaz, W. B., & Abera, E. A. (2020). Temperature and Rainfall Trends in North Eastern Ethiopia . 25 (3), 97–103. https://doi.org/10.19080/IJESNR.2020.25.556163 Agidew, A., & Singh, K. N. (2017). 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Greening of the Earth and its drivers. Nature Climate Change , 23. https://doi.org/10.1038/nclimate3004 Additional Declarations No competing interests reported. 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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-4672963","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":332650769,"identity":"ffea6971-78ec-4624-b084-ff6c80072639","order_by":0,"name":"Ali Yasin","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA7UlEQVRIiWNgGAWjYDACdjD5j8e+mfkAkCEhQ1gLM5g8IGfA3pYA0sJDtBZjA54zBiAWYS38zczHJD623UncLpHz+dWNGgseBvbDRzfg0yJxmC3ZcGbbs8SdM3K3WeccAzqMJy3tBl5rDvMYPuZtY05suJG7zTiHDahFgscMrxb5w/wfDkO05DwzzvlHhBaDwzyMQFsOGxucOcP8OLeNCC2Gh9mMDWecS5OTbG8zY87tk+BhI+QXuePNzyQ+lNnw8DMzP/6c861Ojp/98DH83gcBRjYwxSYBJgkqB4M/YJL5A3GqR8EoGAWjYKQBAAiySMKqMDh8AAAAAElFTkSuQmCC","orcid":"","institution":"Jigjiga University","correspondingAuthor":true,"prefix":"","firstName":"Ali","middleName":"","lastName":"Yasin","suffix":""},{"id":332650770,"identity":"1890e927-1f12-4e19-b707-08fdeb5a2269","order_by":1,"name":"Abebe Ali","email":"","orcid":"","institution":"Wollo University","correspondingAuthor":false,"prefix":"","firstName":"Abebe","middleName":"","lastName":"Ali","suffix":""},{"id":332650771,"identity":"24e85b28-76ca-45cb-b3d8-289a5ff6ba8d","order_by":2,"name":"Nurhussen Ahmed","email":"","orcid":"","institution":"Wollo University","correspondingAuthor":false,"prefix":"","firstName":"Nurhussen","middleName":"","lastName":"Ahmed","suffix":""}],"badges":[],"createdAt":"2024-07-02 09:11:10","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4672963/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4672963/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s40068-024-00371-6","type":"published","date":"2024-09-28T15:56:54+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":62108378,"identity":"e4ca0816-4acd-465c-bfdb-2d31caa18076","added_by":"auto","created_at":"2024-08-09 11:16:00","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":431504,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eMap of Millie watershed which displays a true color combination of the Sentinel-2 satellite image (bands 4, 3, and 2).\u003c/em\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-4672963/v1/d29375915d5fbdeb9d6a8e36.png"},{"id":62108374,"identity":"51eb646e-044d-4704-83bf-9e4314c38ad1","added_by":"auto","created_at":"2024-08-09 11:16:00","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":82931,"visible":true,"origin":"","legend":"\u003cp\u003eValidation of SNAP toolbox and MVLR model derived LAI using ground measured LAI\u003c/p\u003e\n\u003cp\u003eTable 1: Sentinel 2 spectral and spatial characteristics of the 8 selected bands\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-4672963/v1/9d03819213d4cec07d3ffd94.png"},{"id":62108371,"identity":"f7459a06-5a13-44f3-852a-e8a55b98d42d","added_by":"auto","created_at":"2024-08-09 11:16:00","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":93416,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eSeasonal Trend of LAI from 2016 to 2022; Sen’s slope shows the trend direction of LAI in the study period. All seasons had a positive trend except the spring season, which had a Sen Slope value of -0.041.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-4672963/v1/e499869cf75e911e756809bc.png"},{"id":62108379,"identity":"313bad7e-cd23-4dfd-9575-2dc4e90711a1","added_by":"auto","created_at":"2024-08-09 11:16:00","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":56131,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eAnnual Trend of Mean LAI from 2016 to 2022: a positive \u003c/em\u003etrend with a 0.084 Sen's slope and a 0.05 P-value.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-4672963/v1/0994737b784e5c9dd3bc234e.png"},{"id":62108957,"identity":"8c8d5a63-fb5f-4e60-8942-b2fbc80e72b0","added_by":"auto","created_at":"2024-08-09 11:23:59","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":86814,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eSeasonal Trends of mean LST from 2016 to 2022; Sen’s slope shows the trend direction of LST in the study period. All seasons had an insignificant positive and negative trend except the summer season, which had a P value of 0.07.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-4672963/v1/37d9e404f5c186f7e9963739.png"},{"id":62108373,"identity":"5158a783-197e-49bc-9e7f-b57aac593699","added_by":"auto","created_at":"2024-08-09 11:16:00","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":50358,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eAnnual Trend of Mean LST from 2016 to 2022\u003c/em\u003e: an insignificant positive \u003cem\u003etrend with a 0.466 Sen's slope.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-4672963/v1/1dbb0c3da63d66679e927950.png"},{"id":62108377,"identity":"a4f7d913-d0b1-456a-a7fa-2f5e19cce57f","added_by":"auto","created_at":"2024-08-09 11:16:00","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":308211,"visible":true,"origin":"","legend":"\u003cp\u003eAnnual Association between LAI and LST from 2016 to 2022\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-4672963/v1/ab35c79317ad53e35c65bf8c.png"},{"id":62108958,"identity":"9c31513d-4119-454f-9219-a50f3fa75e2e","added_by":"auto","created_at":"2024-08-09 11:24:00","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":187726,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eSeasonal Association between LAI and LST from 2016 to 2022;\u003c/em\u003e\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-4672963/v1/948516fdb4fa9a90de2b42d1.png"},{"id":65627054,"identity":"d2fe95fa-02ba-4c99-9506-b6f9e536b9a1","added_by":"auto","created_at":"2024-09-30 16:10:00","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2021072,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4672963/v1/3b580b56-8e56-4eef-bca6-3c2c1027ad59.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Time-series analysis of Leaf Area Index and Land Surface Temperature Association using Sentinel-2 and Landsat OLI data","fulltext":[{"header":"1. Background of the Study","content":"\u003cp\u003eUnderstanding the dynamics of vegetation and Land Surface Temperature (LST) holds paramount implications for ecological, climate change assessment, and land resource management studies. Leaf Area Index (LAI), a critical biophysical variable measuring the total area of leaves relative to the land surface, plays a pivotal role in comprehending land surface processes associated with vegetation dynamics and climate modeling (Avdan \u0026amp; Jovanovska, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Mwangi et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). It provides essential insights into the impacts of various environmental factors on vegetation (Wang et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Similarly, LST, another vital variable linked to vegetation dynamics, is directly influenced by vegetation conditions (Guechi et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Zhao-Liang et al., 2013).\u003c/p\u003e \u003cp\u003eA comprehensive global vegetation analysis spanning 31 years (1982\u0026ndash;2013) observed across all continents revealed a persistent browning trend on Earth since the 1990s (Pan et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Nonetheless, other studies have indicated a contrasting greening trend, primarily driven by human land-use practices. For example, Park et al., (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) demonstrated substantial contributions to the greening trend from China and India, with China accounting for 25% of the global increase in leaf area and India exhibiting an increase exceeding 35% since 2000. The ongoing browning trend in global vegetation since the 1990s emphasizes the impact of climate on vegetation. However, the concurrent greening trend, exemplified by significant leaf area increases in China and India, suggests that human land-use practices significantly influence vegetation (Pan et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe relationship between LAI and LST has been extensively studied and established as inverse relationship (Hussain et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Jin \u0026amp; Zhang, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Mwangi et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Rasul et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Several researchers have concurred on this inverse relationship, attributing it to the cooling effect of vegetation on the land surface, where an increase in the number of plants (measured by LAI) corresponds to a decrease in LST (Nega et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). This cooling effect arises from the transpiration of water by plants, regulating the temperature of the surrounding environment (Schwaab et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Additionally, this relationship is subject to the influence of other factors such as solar radiation, atmospheric conditions, and soil moisture (Liu et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Generally, heightened solar radiation and reduced atmospheric moisture levels tend to elevate LST (Cheruy et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Han et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Jiang et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), whereas increased vegetation and soil moisture assist in lowering LST (Imran et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Li et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Liu et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e \u003cp\u003ePrevious studies have utilized various satellite datasets to investigate the connection between LAI and LST, including MODIS (Hussain et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Miller et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Mwangi et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Rasul et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Reygadas et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Schwaab et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Tesemma et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Despite the growing importance of remote sensing data in environmental monitoring and land management (Franklin, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e1983\u003c/span\u003e; Skidmore, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Skidmore et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e1997\u003c/span\u003e), there remains a critical gap in the understanding of the temporal dynamics and interrelationship between LAI and LST as derived from Sentinel-2 and Landsat Operational Land Imager (OLI) data. While both LAI and LST are crucial indicators of ecosystem health and vitality (Imran et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), the combined analysis and temporal association of these metrics using time-series data from these two prominent satellite platforms have been relatively underexplored. Recognizing the interconnected influence of vegetation dynamics and surface temperature on local ecosystems, there is a pressing need to elucidate the intricate associations and potential feedback mechanisms between these critical biophysical parameters.\u003c/p\u003e \u003cp\u003eThis research aims to bridge this gap by conducting a meticulous time-series analysis of LAI and LST derived from Sentinel-2 and Landsat OLI data. Through exploring the intricate connections between changes in LAI and LST, our research aims to offer valuable understanding of the fundamental ecological mechanisms at play and their significance for promoting sustainable land management techniques and strategies for mitigating the effects of climate change. Through this integrated approach, we aim to contribute to the refinement of remote sensing methodologies and the advancement of our understanding of local-scale environmental dynamics.\u003c/p\u003e \u003cp\u003eThe findings of this study highlight the significance of employing high-resolution satellite imagery to examine the connection between vegetation status and climate at a local scale, particularly in areas characterized by diverse landscape features that can impact the association between LAI and LST. It offers a comprehensive evaluation of their relationship and emphasizes the potential of high-resolution satellite imagery in understanding the complex interaction between vegetation and climate. The results contribute to the existing knowledge on this association and provide valuable insights into its potential consequences on vegetation dynamics and climate modeling at the local scale. Thus, the research has two main objectives: (a) assessing the time-series trend of LAI and LST derived from Sentinel-2 and Landsat OLI, and (b) evaluating the seasonal and annual correlation between LAI and LST.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n \u003ch2\u003e2.1 Description of the Study Area\u003c/h2\u003e\n \u003cp\u003eThe study was conducted in the lower Millie watershed, situated in Ambasel District within the South Wollo Zone of the Amhara Regional State, Ethiopia (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). The area encompasses both tropical and subtropical agro-climatic regions. The Millie watershed is positioned between 11.32\u003csup\u003e0\u003c/sup\u003e–11.54\u003csup\u003e0\u003c/sup\u003e latitude and 39.52\u003csup\u003e0\u003c/sup\u003e – 39.68\u003csup\u003e0\u003c/sup\u003e longitude. It covers an area of around 19509 hectares. The elevation of the area ranging from 1427 to 3635 meters above sea level describes the study area possesses multiple agro-climatic zones. In addition, the region exhibits an average temperature varying between 15 to 20º C (Destaw, \u003cspan class=\"CitationRef\"\u003e2017\u003c/span\u003e). Data obtained from Kombelcha meteorological station suggests that the study area experiences an annual precipitation ranging of 800 to 1200 mm.\u003c/p\u003e\n \u003cp\u003eThe primary rainy season typically spans from June to August, while the short rains characterize the period between April and June. The region experiences its lowest minimum temperatures from August to November, ranging from 11\u003csup\u003eo\u003c/sup\u003e C to 12\u003csup\u003eo\u003c/sup\u003e C. Conversely, the highest maximum temperatures occur during May and June, varying between 22\u003csup\u003eo\u003c/sup\u003e C and 30\u003csup\u003eo\u003c/sup\u003e C. The livelihood of the study area is largely reliant on mixed agriculture, with a focus on crop-livestock production, predominantly influenced by rainfall. The primary vegetation found in the research area consists mainly of Eucalyptus trees and different types of long-lasting fruit plants (Desalegn et al., \u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e). These are commonly used in agroforestry for sustaining livelihoods and creating income-generating opportunities, serving as a means of mitigating the challenges faced by small landholders. Agriculture serves as the dominant occupation, engaging a significant portion of the population. Key crops cultivated in the area include Wheat, Barley, Teff, and various pulse crops (Desalegn et al., \u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\n \u003ch2\u003e2.1. Data processing\u003c/h2\u003e\n \u003cdiv id=\"Sec5\" class=\"Section3\"\u003e\n \u003ch2\u003e2.2.1 Sentinel 2 Data and Preprocessing\u003c/h2\u003e\n \u003cp\u003eThe estimation of LAI utilizing the Sentinel Application Platform (SNAP R2020a) toolbox involved the use of Sentinel-2 satellite images. More specifically, the dataset comprised over 36 cloud-free Level 1C data from 2016 to 2018 and Level 2A data from 2019 to 2022. The Level 2A data, accessible free of charge from the Copernicus SciHub website (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://scihub.copernicus.eu/\u003c/span\u003e\u003c/span\u003e), is both geometrically and atmospherically corrected, providing a Bottom-Of-Atmosphere (BOA) corrected reflectance product (Wang et al., \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e). However, preprocessing required for the Level 1C images before biophysical retrieval (Sola et al., \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eTo convert the Level 1C data to Level 2A, the Sen2Cor atmospheric correction algorithm was employed, a method demonstrated in the work of Kganyago et al., (\u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e). This algorithm effectively addresses atmospheric distortions in Level-1C data, producing BOA reflectance images, with the option for terrain and cirrus correction (Clevers et al., \u003cspan class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eStandardizing the analysis, all atmospherically corrected images were resampled to a 20-m pixel size, ensuring uniformity across all available bands of the Sentinel-2 images. Within the SNAP toolbox, three biophysical processors were available, namely S2_20m, S2_10m, and LANDSAT8 (Mourad et al., \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e). For this particular study, the S2_20m processor was utilized to estimate the LAI of the study area. In assessing the LAI trend from 2016 to 2022, a total of 27 Sentinel-2 images were employed. These images were acquired during the same seasons as Landsat imagery, with a tolerance of up to 10 days.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e\n \u003ch2\u003e2.2.2 Landsat level 2 LST product and preprocessing\u003c/h2\u003e\n \u003cp\u003eThe Landsat 8–9 Collection 2 Level 2 LST is computed from the Landsat 8–9 Collection 2 Level 1 Thermal Infrared Sensor (TIRS) Band 10, using Top of Atmosphere (TOA) Reflectance, TOA Brightness Temperature (BT), Advanced Space borne Thermal Emission and Reflection Radiometer (ASTER), Global Emissivity Dataset (GED) data, ASTER Normalized Difference Vegetation Index (NDVI) data, and atmospheric profiles of geopotential height, specific humidity, and air temperature extracted from reanalysis data (Sayler, \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e). The Digital Number (DN) values are then transformed to LST using a multiplication factor and an additive number, as shown in Eq. 1 (Sayler et al., \u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e). The output unit for LST is in Kelvin, which is converted to Celsius by subtracting 273.15. To ensure consistency with the LAI pixel size, the LST data were resampled to a 20-meter pixel size.\u003c/p\u003e\n \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u0026nbsp;\u003c/span\u003e \u003cimg src=\"data:image/png;base64,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\"\u003e\u003c/p\u003e\n \u003cp\u003eCloud-free Landsat imagery was obtained from NASA's website, comprising Level 2 Landsat 8 and 9 OLI, to estimate the LST of the Millie watershed from 2016 to 2022. Over 37 Level 2 Landsat images were utilized over 7 consecutive years, including multiple images for each season (winter, spring, summer, and autumn). The Landsat satellite data has a spatial resolution of 30 meters, and the imagery can be downloaded free of charge from the USGS Earth Explorer (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://earthexplorer.usgs.gov/\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e\n \u003ch2\u003e2.2.3 LAI Retrieval\u003c/h2\u003e\n \u003cp\u003eThe SNAP Toolbox's biophysical variable retrieval algorithm for Sentinel-2, as described by Weiss \u0026amp; Baret (\u003cspan class=\"CitationRef\"\u003e2016\u003c/span\u003e), is built on the PROSAIL radiative transfer model for canopy architecture and the Artificial Neural Networks (ANN) algorithm. Leveraging instant top-of-canopy reflectance data from eight Sentinel-2 bands, along with viewing zenith, solar zenith, and relative azimuth angles, the SNAP biophysical processor parameterizes PROSAIL to simulate bottom-of-atmosphere reflectance (Xie et al., \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e). Subsequently, the ANN algorithm, trained on the simulated reflectance, is applied to the chosen Sentinel-2 bands to retrieve the biophysical variable, as explained by Weiss \u0026amp; Baret (\u003cspan class=\"CitationRef\"\u003e2016\u003c/span\u003e). This algorithm is categorized as \"nonspecific,\" indicating its capability to deliver reasonable biophysical variable performance for various types of vegetation (Kamenova \u0026amp; Dimitrov, \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e; Kganyago et al., \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e; Mourad et al., \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e; Xie et al., \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e). For reference, Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e outlines the required bands for LAI estimation using the SNAP toolbox.\u003c/p\u003e\n \u003cp\u003eThe accuracy of Leaf Area Index (LAI) derived from the SNAP toolbox was evaluated to ascertain its reliability in practical applications. The study findings revealed a strong correlation (R\u003csup\u003e2\u003c/sup\u003e = 0.74) between LAI values obtained through the SNAP toolbox and those acquired through direct field measurements. Figure \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e visually depicted the validation results, illustrating the consistency and coherence between estimated and ground-measured LAI values. The Root Mean Square Error (RMSE) of 0.53 indicated an acceptable deviation between SNAP-derived and actual field-measured LAI values, emphasizing the tool's capability to provide reliable estimates. The calculated bias of -0.31, though suggesting a slight underestimation, was within an acceptable range, reinforcing the credibility of SNAP toolbox-derived LAI values. Overall, the strong correlation, low RMSE, and minimal bias collectively underscored the reliability and accuracy of the SNAP toolbox-derived LAI values, affirming its practical utility in estimating and predicting Leaf Area Index in the study area. These findings contribute to the validation and endorsement of the SNAP toolbox's effectiveness in generating accurate LAI data for further time-series analysis.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eSentinel 2 spectral and spatial characteristics of the 8 selected bands\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"4\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eBand Number\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCentral WL (nm)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eWidth (nm)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSpatial Resolution (m)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBand 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e560\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBand 4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e665\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBand 5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e705\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBand 6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e740\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBand 7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e783\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBand 8a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e865\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBand 11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1610\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBand 12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2190\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e180\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n \u003ch2\u003e2.3 Trends and Correlation Analyses\u003c/h2\u003e\n \u003cp\u003eIn this study, local-scale changes in LAI, LST, and their interconnectedness were assessed using trend and regression analyses. MATLAB R2020a and R 4.0.3 version software were employed for all statistical operations.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eTrend Analysis\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eThe trend analysis for both annual and seasonal trends relied on the SNAP-derived LAI and Landsat Level 2 LST products. Estimations for annual and seasonal trends were conducted using Zhang's method and the Yue Pilon method found in the \"zyp\" package within R software (X. L. Wang \u0026amp; Swail, \u003cspan class=\"CitationRef\"\u003e2001\u003c/span\u003e; Yue et al., 2002). This approach is advantageous for its accurate confidence intervals and robustness against outliers. Moreover, the widely recognized Mann-Kendall test was used to detect trends in time series data, as commonly seen in environmental studies, hydrology, and climatology (Hamed, \u003cspan class=\"CitationRef\"\u003e2008\u003c/span\u003e). The Mann-Kendall test allows for the assessment of monotonic trends in data, where a significant test statistic leads to the rejection of the null hypothesis (Yue \u0026amp; Wang, \u003cspan class=\"CitationRef\"\u003e2004\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eWhile the Mann-Kendall test is effective in handling tied observations and outliers (F. Wang et al., \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e), it does have limitations, such as its inability to detect nonlinear trends or abrupt changes in trend direction (Blain, \u003cspan class=\"CitationRef\"\u003e2013\u003c/span\u003e). To mitigate these limitations, the Sen's slope estimator and the Theil-Sen estimator were combined with the Mann-Kendall test, providing a comprehensive analysis of trend behavior. Additionally, given the requirement of non-auto correlated data, the data underwent testing in R Studio using the plot method of autocorrelation via the acf function. Eq.\u0026nbsp;2 was employed in the Mann-Kendall test for the analysis of the time series.\u003c/p\u003e\n \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u0026nbsp;\u003c/span\u003e \u003cem\u003e\u003cimg src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAqgAAAA3CAYAAADAF7jjAAAAAXNSR0IArs4c6QAAAARnQU1BAACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAAqDSURBVHhe7d1JaFRbHsfxYyMoOEJcOCA8Y6tPEFScEJyHBmeRdgoOC43i84mKacSF00KQNk7QLkzMQrCNxkU7a7eioj6cMA6Lth3S9sJpoeKEuEvnd7yn3k1ZValKKqlTqe8HitStulXn3CrBX/3POfe2qK5hAAAAAE/8IfgLAAAAeIGACgAAAK8QUAEAAOAVAioAAAC8QkAFAACAVwioAAAA8AoBFQAAAF4hoAIAAMArOR1Qv3z5YiZMmGB69uxpXr9+HTwKAACATMrpgNq2bVtz/PhxM378+OARAAAAZFqzDKj37t0zQ4cONdeuXbN/d+7cGTzT+DLZNgAAQHPQLAPqwIEDzfLly01lZaXZv3+/HcpvKplsuyk9ffrUVFRUBFt+KysrYwoHAABZpFkGVIWRW7dumTlz5piXL1/aoXw337RFixaRm7bTHSBjtd3c6BgPHz5sjzEbLFmyxJSUlBBSAQDIEt4F1CNHjtQKkanc3HD63bt3zfTp002XLl3s/YKCAhsUL168aKqrqyM3bYcDZGO13dwo7C1btizYyg7qr/oNAAD8l3JAVcVR/9krlHXq1MmGOlWm0jXcO2/ePFNeXh5sGbuA6fPnz7WCZfTtyZMnZsiQIcErvofEQYMG2X49evTI9jGelStXmtLSUrNw4UIzderUJm07G2mO7adPn2wAzybqr/qt/gMA4LMtO0rMxNkr7U33c1JNyErJokWL7K0muNnbli1bqvU2p06dCvZID71fXl6efe+aAFhdEwSDZ2KrrKysLi4uDrYaJpNtp5v6lZ+fX/3q1avgkYbRd9+Q71r9qQn+9t9OY4s+dvVb/QcAwFeb/7q/uvwf/7T3L169XT3hz79EtnNJShVUVxVcs2aNHRrXbd26daawsNBWDdNJ1cwLFy6YmqBo7ty5Y4YPH56w+tW5c+e0zSfNZNvppu+nqqoqLRVP9/1369YteCR16k/01IrGEn3s6rf6r+MAAMBHc2dMNPNm/sneHz9yiPn5jz+Zt+8+2O1cklJAbdeunWnfvr3Zs2dP8Mj3c4kOGzbMPpduWhF/48YNk5+fb969e2cmTpwYNygqhGzevDnYarhMtu2rN2/e2L8K5NnI9dsdBwAAvunbu0dw73ed8joG93JHSgFVYVTV0zNnzpjFixdHqoZaJd1YFbFevXqZBw8e2PmgLig21bzOTLadClUEdc7V8IItPaYrZGn79OnTwZ7fF4Jp7rDb190099c9rv3Dc43dMeusBPocoml/7afXuxDv2nFnStC/F+2TjjMn1NWeToEV69hF/ddxAACQDd6+/xCpqOaUYKg/JZpzqTmadc0l1Hw/NRHvlsp8QLWj9vQ6tZ3uOa+JZLLtZGiu5axZs2w/S0tLI/3TdmFhYa05mOp/zQ8Mu11eXm639X2K9qsJeHa7qKjIzr3Ve+smen2i+azaT9+p+uDeM0yvd++VDonaiz520X3137fvDwCAWDQf9d+P/xts5ZZ6nWZKw9+ao3n//n0zc+bMuBWxgwcP1lrxHn3T88lShVaXJa0JJGbKlCl2nmhTyWTbyRg3bpz5+vWrPePA0qVLI/1TJVFTLzQFQd+RpmZs2LDBTJ482T6v4+rQoUNk6FtnIOjatau9yICmLOh1t2/ftu+fDO138+ZNOx9Z/0aiXb58+Yf3cpXVeDc9H0+i9sLHDgBAtvlbWYX5uddPMYf8c0HSAVVDpeHhUgUChR2FVIWBpqAA1qpVK7Nv377gkaaTybbrou9Cp8vS9Ivwj4VLly7ZwBn28OFDu4++MwXWESNGRELcsWPH7DEqwCq8uu9VUx2Sof26d+8ecwhdQ/EvXrz44b0a8iMmUXuxjh0AgGxw5Pi/7N+cHNoPJB1QHz9+HNz7XZ8+fWpV4KI1pDoWTaFKAWzFihWNNt81nky2XZfdu3fb/qlq2rp1a3t5VdFj4eqn+r1jxw67il2VRZ2ZYOTIkZHArTmr169fj1xkQBTyNLfVHbNWwbds2TLuIiM3D1RBN1r0e6VDvPaijz0sLy+vQWchAACgMSmc/ufp/8yvS7Ljao2NJemAqsrb6tWrI1W1WBW4aOkc4teQs06mH2vouLFlsu1EFMS0YC1c2db3JKr4vn//3oZJd1olhcPRo0fb596+fWuPywVG7Td48GAzZswYu63XHD161PTt2zeyEEkVywEDBtj70RQW9UNl/fr15tu3b7ZvFRUV9n10/9y5c/YHTbokai/WsYsL1vF+UAEAkEkunG75y/erNWrbVVNzTVIBVf/Jjxo1ypw/f95W4VT97N27t+nfv3+TDHlrVbqGazMx9zOTbddFQez58+dm/vz59jtRdXT79u32Oc0nVbBs06ZN5AfEyZMnTXFxsZk9e7atvIanZkRXOBXmPn78aCvnLpjrOVVpw0Pq+nxUxVRA1GekEKuV8qpe6r7aVju6ilM6zpWbTHuxjl3UbwXueD+oAADIFF0xquzvJ8xvtx9EriL1260HOTvM36JapUyPqTqoxTUKJqlQgFEIW7t27Q/DynpOi7sU7jSsHS+w1LftsK1bt9rTNcVqw/Vj0qRJ9qTy8STb30T0WahCqmF9hTcthNJ0AAW2VCrZeu2hQ4dS+kzcvomOsSmo/QULFnhXCQcAALXVaxV/U1HlVlU/hbxkXL161d5EoXTjxo0/hFPRY1qVr/ObxtOQtsPcavhY1A9VpMPX8o8lmf4moqC9bdu2yGehgDZ37lwbnFOl1+piDW7YPxZ9djqvqiqnZ8+etVeOKigoCJ7NDPVJ/SacAgDgP28DqqqGqS5MOnHiROSKVlqAVd+Twje0bdF7qP3w/NBYFOIUhDVEH71vWVlZvfofTfNKV61aZS804BaoabhdUwLqM0VDwfbKlSvBVmya46ppIArWWsSU6WH1kpKSegVyAADQ9LwNqKksTNJ8SQ3fKgy5BTCaZ6mQmWzADGto26J2N23alHDFuMKn5njOmDHDlJaWNto8V/VFx6TQ6BaoqV0Nvdfn81HYVIVUAToWPa+qqdpRMKxPG+mkfqqCm+mQDAAAkuNlQFVw2rVrl5k2bVqk4pfopuCh/cO0UEarxlXFDO9bV1U1HW07qo5q4Y6rpoZfp20Nk+vcoAq3OsG+hPfVY6rK6rKdGqL2iY5bl7jNBuqnvgcAAJAdvAuoGuYuKioKtlLTo0cPG+hcmEu1YpaOth0XPhNVD7WqfOzYsaZjx45m7969Npxqf1d9PHDggF2pX1VVRfUPAADkDO8Cqoa53TB0qjcFOwU8VU8V/rRS3YW96H105SUNq2so31VU09G2o9M2udMqhUNneH8Ns+u8pB8+fLBV0vDro8XqLwAAQHPk/WmmsomCo07nVFhYaOef6gwAVD4BAABS4+0iqWykCuizZ89Mv379al0yFAAAAMmjggoAAACvUEEFAACAVwioAAAA8AoBFQAAAF4hoAIAAMArBFQAAAB4hYAKAAAArxBQAQAA4BUCKgAAALxCQAUAAIBXCKgAAADwCgEVAAAAXiGgAgAAwCsEVAAAAHiFgAoAAACvEFABAADgFQIqAAAAvEJABQAAgFcIqAAAAPAKARUAAAAeMeb/iggx2XQno8kAAAAASUVORK5CYII=\"\u003e\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003eLet X1, X2, Xn represent the variables denoting n points of LAI and LST. The Mann-Kendall test (S) is then applied to these variables.\u003c/p\u003e\n \u003cp\u003eThe Mann-Kendall test furnishes insights regarding the trend direction, if any, within the time series. A negative test result implies a decrease in both LAI and LST, while a positive value suggests an increase. Conversely, a result of zero supports the null hypothesis of no trend. A positive S value suggests that the later observations in the series are expected to be higher than the earlier ones, while a negative S value suggests the opposite. The variance of S is calculated using Eq.\u0026nbsp;3.\u003c/p\u003e\n \u003cp\u003e\u003cimg 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\"\u003e\u003cbr\u003e\u003c/p\u003e\n\u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Seasonal and Annual Trends of LAI and LST (2016\u0026ndash;2022)\u003c/h2\u003e \u003cdiv id=\"Sec11\" class=\"Section3\"\u003e \u003ch2\u003e3.1.1 Seasonal Trend of Mean LAI\u003c/h2\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e illustrates the seasonal trends and the trend during the period (trend P) determined for each season from Sentinel 2 images. Throughout the study duration (2016 to 2022), all seasons exhibited significant greening trends, with the exception of the spring season, which displayed a Sen Slope of -0.041. The rate of change in LAI demonstrated significant variation, ranging from 0.02 to 0.08 for the season-to-season trend and from 0.13 to 0.57 for the trend period.\u003c/p\u003e \u003cp\u003eOf particular note, the autumn season demonstrated the highest trend (0.082) and trend P (0.576) with a Sen Slope of 0.15. In general, the average LAI for all seasons showcased a notable greening trend over the study period. Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e highlights the seasons with a statistically significant trend at a p-value of 0.05 in bold font.\u003c/p\u003e \u003cp\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\u003eZ-value, Sen's Slope, P-value, trend, and trendp of the seasonal and annual trends of LAI\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSeasons\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eZ-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSen\u0026rsquo;s Slope\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003etrend\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003etrendP\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eWinter\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.503\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.055\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.066\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.464\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSpring\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.503\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.041\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.137\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSummer\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.503\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.052\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.052\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.364\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAutumn\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e1.878\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.047\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.05\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.082\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.576\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAnnual\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e1.878\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.013\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.05\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.013\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.091\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section3\"\u003e \u003ch2\u003e3.1.2 Annual Trend of Mean LAI from 2016 to 2022\u003c/h2\u003e \u003cp\u003eThe annual trend in mean LAI for the study area is depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. The analysis revealed a substantial greening trend, represented by a Sen's slope of 0.084 and a P-value of 0.05 for the 2016\u0026ndash;2022 period. While the year-to-year trend of LAI was 0.013, the trend for the entire period amounted to 0.091. Notably, the highest mean annual LAI in the study area was observed in 2021.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section3\"\u003e \u003ch2\u003e3.1.3 Seasonal Trend of Mean LST\u003c/h2\u003e \u003cp\u003eThe trends in LST for the four seasons are visualized in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e. Variations in trend and trendp of LST were observed across seasons. However, the study did not identify any statistically significant seasonal trend in mean LST during the study period (2016\u0026ndash;2022), except for the summer season, which exhibited a seasonal trend of LST with a P-value of 0.07.\u003c/p\u003e \u003cp\u003eDuring the spring season, a near-zero trend (trend\u0026thinsp;=\u0026thinsp;0.1) was observed, while increasing trends were noted in the summer seasons of 2021 and 2022. The season-to-season trend of LST ranged from \u0026minus;\u0026thinsp;0.031 (autumn season) to 1.5 (summer season), while for the trend period, it varied from \u0026minus;\u0026thinsp;2.147 (autumn season) to 10.5 (summer season). Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e presents the statistical results of the mean LST trends for each season, alongside the annual mean LST trend.\u003c/p\u003e \u003cp\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\u003eZ-value, Sen's Slope, P-value, trend, and trendp of the seasonal and annual trends of LST from 2016 to 2022.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\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=\"left\" 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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSeasons\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eZ-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSen\u0026rsquo;s Slope\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003etrend\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003etrendp\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWinter\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.265\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.265\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.853\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSpring\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.700\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSummer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.802\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e10.500\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAutumn\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.307\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.307\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-2.147\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnnual\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.201\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.466\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.466\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.260\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section3\"\u003e \u003ch2\u003e3.1.4 Annual Trend of Mean LST from 2016 to 2022\u003c/h2\u003e \u003cp\u003eThe annual trend in the mean LST for the study area was assessed based on the mean annual LST values over the study period, as depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e. To ascertain the significance of the observed changes, the Mann-Kendall trend test was conducted at a P-value of 0.1.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe study findings indicated an absence of a significant annual trend in mean LST. The year-to-year trend of LST was 0.466, while for the trend period, it was 3.260. Notably, the highest mean annual LST in the study area was observed in 2021, coinciding with the year that demonstrated the highest mean LAI in the study period. Overall, the average annual LST did not display statistically significant changes over the study period, with a P-value of 0.2.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Correlation\u003c/h2\u003e \u003cdiv id=\"Sec16\" class=\"Section3\"\u003e \u003ch2\u003e3.2.1 Annual Relationship between LST and LAI\u003c/h2\u003e \u003cp\u003eThe assessment of the association between the LAI and LST was conducted at both annual and seasonal scales. To facilitate this analysis, all LAI and LST seasonal images for each year were stacked and averaged using cell statistics. Similarly, for the seasonal analysis, LAI and LST images for the same season across the seven years were stacked and averaged accordingly. A total of 201 points were used to evaluate the association between LAI and LST at both scales.\u003c/p\u003e \u003cp\u003eOverall, the relationship between the two variables demonstrated a moderate correlation, with R-squared (R\u003csup\u003e2\u003c/sup\u003e) values ranging from 0.35 to 0.49. This indicates that the vegetation characteristics of the study area respond to changes in LST, as depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e. Notably, the lowest R\u003csup\u003e2\u003c/sup\u003e value of 0.35 was recorded in 2021, while the highest value was observed in 2020 (R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.49).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section3\"\u003e \u003ch2\u003e3.2.2 Seasonal Relationship between LAI and LST\u003c/h2\u003e \u003cp\u003eThe study area exhibited significant spatial and temporal variations in the distribution of LAI and LST. Consequently, the seasonal association between LAI and LST was evaluated to discern the influence of LST on LAI across the winter, spring, summer, and autumn seasons. The results of the linear regression model fitting between LST and LAI are depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eThe highest correlation between LAI and LST was observed during the winter season (R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.46), while the lowest R\u003csup\u003e2\u003c/sup\u003e value of 0.36 was recorded during the spring season. Overall, the correlation between LST and LAI across all seasons was moderate. Notably, during the crop-growing seasons, specifically summer and spring, the relationship was relatively weaker. However, during the dry season (winter) and autumn when the crops' leaves reached maturity, the correlation between LAI and LST was notably stronger.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eTable \u0026lrm;1; R\u003c/em\u003e \u003csup\u003e \u003cem\u003e2\u003c/em\u003e \u003c/sup\u003e, \u003cem\u003eAdjusted R\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e \u003cem\u003eand P-Value of annual and seasonal correlation between LAI and LST\u003c/em\u003e\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Taba\" border=\"1\"\u003e \u003ccolgroup cols=\"4\"\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=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSeasons\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAdjusted R\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP-Value \u0026lt;\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.4119\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.4088\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.20E-16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.4012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.3981\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.20E-16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.4104\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.4074\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.20E-16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.4763\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.4736\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.20E-16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.4934\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.4908\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.20E-16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.3452\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.3418\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.20E-16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.4594\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.4566\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.20E-16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWinter\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.4602\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.4575\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.20E-16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSpring\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.3580\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.3548\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.20E-16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSummer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.3552\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.3520\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.20E-16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAutumn\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.4065\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.4036\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.20E-16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"4. Discussions","content":"\u003cp\u003eThis study examined the temporal variation in LAI and LST by mapping LAI and LST in the Millie watershed from 2016 to 2022 using Sentinel-2 LAI and Landsat LST products. We investigated the temporal trends of mean LAI and LST in the study area at both seasonal and annual scales. Our analysis indicated a generally significant increasing trend in mean LAI during the study period, whereas LST exhibited an insignificant positive trend.\u003c/p\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Seasonal and Annual Trends of LAI\u003c/h2\u003e \u003cp\u003eOur findings revealed a notable greening trend in the Millie watershed from 2016 to 2022, with positive Sen's slope values observed in the winter, summer, and autumn seasons. These results are consistent with prior research indicating a global increase in LAI during growing seasons (Rasul et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). We attribute this trend to a combination of factors, including natural reforestation, land-use changes, and increased agricultural activity in the region. However, the negative Sen's slope value observed during the spring season suggests that various environmental stressors or other factors may be limiting vegetation growth and productivity at that time.\u003c/p\u003e \u003cp\u003eThe mean LAI trend exhibited seasonal variation, ranging from 0.020 to 0.082 (Figs.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e and \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Nevertheless, the trendp (trend during the study period) was consistently robust, ranging from 0.137 to 0.576. These findings suggest that while short-term seasonal trends may exhibit some variability, the overall trend of increasing LAI throughout the study period remains substantial.\u003c/p\u003e \u003cp\u003eAs reported by SuDCA \u0026amp; Soberland (2015) and Agri Service Ethiopia (ASE) in 2011), agriculture stands as the predominant land use in the study area. Investigating seasonal trends becomes essential due to the fluctuating spatial patterns of seasonal crops influenced by factors such as rainfall and temperature. Our findings solidify the close connection between the trend of LAI and agricultural activities. Seasons marked by abundant rainfall, accompanied by a gradual expansion of cultivated lands, have resulted in higher mean LAI trends. The study area experiences two primary rainy seasons, namely summer and spring.\u003c/p\u003e \u003cp\u003eAccording to Abegaz \u0026amp; Abera (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), who investigated temperature and rainfall trends in northeastern Ethiopia, the seasonal rainfall trend in this region, particularly South Wollo, has shown a declining trend in the spring season, relatively stable trends in summer and autumn, and an increasing trend in the winter season. The declining trend in spring season rainfall leads to less cultivation, which negatively impacts LAI. Consequently, the declining trend of mean LAI in the spring season is attributed to inadequate land use management in agricultural activities (Agidew \u0026amp; Singh, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). The relative increase in mean LAI during the winter season is attributed to increased rainfall, which positively influences rain-fed agriculture (Chuanhua et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Rasul et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Hence, the increasing or decreasing trend of LAI can be predicted based on land use management activities, such as agricultural practices (Zhu et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). These results affirm the positive influence of rainfall amounts on LAI (Longhui et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), as most of the greening trends were observed during the growing seasons.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Seasonal and Annual Trends of LST\u003c/h2\u003e \u003cp\u003eThroughout the years 2016 to 2022, the average temperature (LST) in the study area did not display any significant seasonal or yearly changes, except for a notable trend observed during the summer season with a 93% confidence level (Figs.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e and \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). A study by Abegaz \u0026amp; Abera (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) in South Wollo, Ethiopia, indicated that the summer season's delayed onset, resulting in reduced rainfall and increased air and surface temperatures, could account for the rising trend of LST during summer and the declining LST trend during autumn in the study area. Decreased rainfall and soil moisture have a positive impact on LST by affecting crop cultivation and soil moisture (Jiang et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Sun \u0026amp; Pinker, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Weng et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). These findings support the notion that changes in rainfall amounts, soil moisture, and crop production inversely influence LST by modulating the transmission of electromagnetic radiation to the Earth's surface.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003e4.3 Seasonal and Annual Association between LAI and LST\u003c/h2\u003e \u003cp\u003eThe relationship between mean LAI and mean LST was evaluated yearly to understand how vegetation responds to variations in LST, as depicted in Figs.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e and \u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e. Our analysis revealed a moderate inverse correlation between mean LAI and LST from 2016 to 2022, with the coefficient of determination (R\u003csup\u003e2\u003c/sup\u003e) ranging from 0.35 to 0.49. This finding contrasts with the results of Rasul et al. (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), who found no significant relationship between LAI and LST in Africa. While the observed moderate negative correlation suggests some sensitivity of the study area's photosynthetic vegetation to LST during the study period, the precise cause behind this relationship requires further investigation. It is possible that variations in vegetation type, land use practices, and climatic conditions contribute to this correlation (Guha \u0026amp; Govil, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Understanding the relationship between LST and LAI is essential for sustainable land management and conservation, particularly as changes in LST can influence vegetation dynamics.\u003c/p\u003e \u003cp\u003eDuring the study period, the correlation between LAI and LST in the Millie watershed was assessed seasonally, revealing moderate correlations between the two variables during the summer and spring seasons, with an R\u003csup\u003e2\u003c/sup\u003e of 0.36 (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e). Notably, the study area's growing season aligns with increased mean LAI which is agreed with previous study by (Nazeri et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The period from March to June is known for the highest temperatures in Ethiopia, with incremental temperature changes during the summer, crucial for crop growth aided by moderate rainfall. Consequently, crop leaf development intensifies during these seasons, contributing to a cooling effect on LST. However, the R\u003csup\u003e2\u003c/sup\u003e is lower during the colder months when crop cultivation is less optimal. These results underscore the significance of various factors, including crop cultivation, precipitation availability, land use practices, and mean maximum temperature, in modulating the seasonal association between LAI and LST.\u003c/p\u003e \u003c/div\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eThis study highlighted a significant greening trend over the study period, attributed to natural reforestation, land-use changes, and agricultural activities. While the LST showed relatively stable patterns, an increase during delayed summer onset was noted. The moderate inverse correlation between LAI and LST underscored vegetation's sensitivity to temperature changes, emphasizing the need for vigilant environmental monitoring and management. These findings stress the importance of understanding the complex interplay between environmental factors, guiding the development of targeted strategies for sustainable land management and conservation in the region. While this study provides valuable insights into the temporal trends and link between LAI and LST in the Millie watershed, there is still a need for further research to better understand the complex relationships between environmental factors and vegetation productivity. One potential avenue for future research is to investigate the impact of land-use changes and management practices on the observed trends in LAI and LST.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe are thankful to the members of the department of Geography and Environmental Studies at Wollo University who assisted with field data collection. We also extend our gratitude to the reviewers and the academic editor for their valuable and insightful feedback.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAY, AM, and NA designing methodology, AY collecting field data. AY, and AM conceiving the study, and designing the research framework, AY performing statistical analysis and prepared the manuscript, AM, and NA providing supervision throughout the study, AY writing original drift, NA, and AM Validation, AM, and NA reviewing and editing. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eAbegaz, W. B., \u0026amp; Abera, E. A. (2020). \u003cem\u003eTemperature and Rainfall Trends in North Eastern Ethiopia\u003c/em\u003e. \u003cem\u003e25\u003c/em\u003e(3), 97\u0026ndash;103. https://doi.org/10.19080/IJESNR.2020.25.556163\u003c/li\u003e\n \u003cli\u003eAgidew, A., \u0026amp; Singh, K. N. (2017). The implications of land use and land cover changes for rural household food insecurity in the Northeastern highlands of Ethiopia : the case of the Teleyayen sub ‑ watershed. \u003cem\u003eAgriculture \u0026amp; Food Security\u003c/em\u003e, 1\u0026ndash;14. https://doi.org/10.1186/s40066-017-0134-4\u003c/li\u003e\n \u003cli\u003eAgri Service Ethiopia (ASE). (2011). \u003cem\u003eagri-drum-july-september-update.pdf\u003c/em\u003e.\u003c/li\u003e\n \u003cli\u003eAvdan, U., \u0026amp; Jovanovska, G. (2016). \u003cem\u003eAlgorithm for Automated Mapping of Land Surface Temperature Using LANDSAT 8 Satellite Data\u003c/em\u003e. \u003cem\u003e2016\u003c/em\u003e.\u003c/li\u003e\n \u003cli\u003eBlain, G. C. (2013). Teste de Mann-Kendall: A necessidade de considerar a intera\u0026ccedil;\u0026atilde;o entre correla\u0026ccedil;\u0026atilde;o serial e tend\u0026ecirc;ncia. \u003cem\u003eActa Scientiarum - Agronomy\u003c/em\u003e, \u003cem\u003e35\u003c/em\u003e(4), 393\u0026ndash;402. https://doi.org/10.4025/actasciagron.v35i4.16006\u003c/li\u003e\n \u003cli\u003eCheruy, F., Dufresne, J. L., A\u0026iuml;t Mesbah, S., Grandpeix, J. Y., \u0026amp; Wang, F. (2017). 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Greening of the Earth and its drivers. \u003cem\u003eNature Climate Change\u003c/em\u003e, 23. https://doi.org/10.1038/nclimate3004\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":"environmental-systems-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ensr","sideBox":"Learn more about [Environmental Systems Research](http://environmentalsystemsresearch.springeropen.com)","snPcode":"40068","submissionUrl":"https://submission.nature.com/new-submission/40068/3","title":"Environmental Systems Research","twitterHandle":"@SpringerOpen","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Leaf Area Index, Land Surface Temperature, Land Use Management","lastPublishedDoi":"10.21203/rs.3.rs-4672963/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4672963/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003e Understanding the complex relationship between vegetation dynamics and Land Surface Temperature (LST) is crucial for comprehending ecosystem functioning, climate change impacts, and sustainable land management. Hence, this study conducts a time-series analysis of Leaf Area Index (LAI) and LST derived from Sentinel-2 and Landsat Operational Land Imager (OLI) data. LAI data was generated using Sentinel-2 imagery processed with the SNAP toolbox, while Landsat OLI data was utilized for precise LST calculations. Mann-Kendall test was used to detect trends in the time series data.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e The trends of LAI were statistically significant at P-values of 0.05 and 0.1 for annual and seasonal trends, respectively. The mean LST trends were statistically insignificant throughout the study period except for the summer season at a P-value of 0.07. The correlation between LAI and LST was weak (R\u003csup\u003e2 \u003c/sup\u003e= 0.36) during crop-growing seasons, but moderate in winter (R\u003csup\u003e2 \u003c/sup\u003e= 0.46) and autumn (R\u003csup\u003e2 \u003c/sup\u003e= 0.41).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion:\u003c/strong\u003e The findings of this research clarify the complex relationships between variations in surface temperature and vegetation growth patterns, providing insight into the environmental mechanisms driving the dynamics of localized ecosystems. The study underscores the implications of these findings for informed decision-making in sustainable land management, biodiversity conservation, and climate change mitigation strategies.\u003c/p\u003e","manuscriptTitle":"Time-series analysis of Leaf Area Index and Land Surface Temperature Association using Sentinel-2 and Landsat OLI data","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-08-09 11:15:55","doi":"10.21203/rs.3.rs-4672963/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-07-28T02:45:26+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-07-27T19:00:09+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-07-27T16:18:00+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"246514967554530229064437542132055570426","date":"2024-07-24T06:45:22+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-07-23T06:57:48+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"93609104627177216471047696725084257787","date":"2024-07-22T03:20:22+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-07-21T10:49:15+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"245125275800091529223195888144415785564","date":"2024-07-20T03:17:13+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"315512316446302883162414684946405475771","date":"2024-07-19T05:18:26+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-07-19T02:28:28+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"230335088559215173073910192039058247101","date":"2024-07-18T23:34:26+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"68647304481645467158827806750684523200","date":"2024-07-17T19:22:48+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"325046547498892319257925542652996890796","date":"2024-07-17T10:34:09+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"219641855223483802423073958139788897936","date":"2024-07-17T06:13:49+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"91239726727893094885963718635271089960","date":"2024-07-17T05:28:43+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"272086890042453393140618115081636296711","date":"2024-07-16T23:49:33+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-07-16T20:43:36+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-07-16T20:40:34+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-07-16T12:22:59+00:00","index":"","fulltext":""},{"type":"submitted","content":"Environmental Systems Research","date":"2024-07-02T09:09:50+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"environmental-systems-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ensr","sideBox":"Learn more about [Environmental Systems Research](http://environmentalsystemsresearch.springeropen.com)","snPcode":"40068","submissionUrl":"https://submission.nature.com/new-submission/40068/3","title":"Environmental Systems Research","twitterHandle":"@SpringerOpen","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"389b706c-8a29-4ac6-913f-874d3d80bb50","owner":[],"postedDate":"August 9th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-09-30T15:59:05+00:00","versionOfRecord":{"articleIdentity":"rs-4672963","link":"https://doi.org/10.1186/s40068-024-00371-6","journal":{"identity":"environmental-systems-research","isVorOnly":false,"title":"Environmental Systems Research"},"publishedOn":"2024-09-28 15:56:54","publishedOnDateReadable":"September 28th, 2024"},"versionCreatedAt":"2024-08-09 11:15:55","video":"","vorDoi":"10.1186/s40068-024-00371-6","vorDoiUrl":"https://doi.org/10.1186/s40068-024-00371-6","workflowStages":[]},"version":"v1","identity":"rs-4672963","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4672963","identity":"rs-4672963","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}