Aerosol Optical Depth Retrieval on Particulate Observing Scanning Polarimeter (POSP) Data over Land using a new Look-up table (LUT) Search Method

Aerosol Optical Depth Retrieval on Particulate Observing Scanning Polarimeter (POSP) Data over Land using a new Look-up table (LUT) Search Method | 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 Aerosol Optical Depth Retrieval on Particulate Observing Scanning Polarimeter (POSP) Data over Land using a new Look-up table (LUT) Search Method Zhe Ji, Zhengqiang Li, Ying Zhang, Yan Ma, Zheng Shi, XiaoXi Yan, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4161991/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 12 Aug, 2024 Read the published version in Aerosol Science and Engineering → Version 1 posted 5 You are reading this latest preprint version Abstract Accurate estimation of Land Surface Reflectance (LSR) is the key to Aerosol Optical Depth (AOD) retrievals. However, it is noted that the band-specific LSRs retrieved using Look-Up Tables (LUTs) are typically pseudo-LSRs obtained by atmospheric corrections to the AOD predetermined in the LUTs that do not match the surface constraints established by the true LSRs alone. As a result, there is an uncertain error in modeling reflectance at the top of atmosphere (TOA) using pseudo-LSRs calculated by linear interpolation. This study proposed a new LUT search method to improve the AOD retrievals of the Particle Observing Scanning Polarimetry (POSP) sensor onboard the China GaoFen-5 (02) satellite. LSR atmospherically corrected using ERA5 reanalysis data and POSP AOD products for the year 2022 was adopted to create a new full-spectrum LSR self-consistent surface constraint. Results showed that the AOD of POSP in January 2023 retrieved using the new method agrees with the ground-truth AOD values from AErosol RObotic NETwork (AERONET) site observations with the correlation coefficient (R) at 0.703 and the root mean square error (RMSE) at 0.068. 76.77% of the values fell into the expected error (EE) envelope of range ± (0.05 + 0.15 AOD AERONET ), and 67.35% met the accuracy requirements of the Global Climate Observing System (GCOS). Aerosol optical depth POSP AEROENT Land Surface Reflectance Look-Up Tables Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Article Highlights New full-spectrum LSR self-consistent surface constraints are constructed based on the spectrum properties of the POSP sensor and surface orientation properties. The optimal LUT search method is used to avoid the error caused by the old linear interpolation method. LSRs are obtained by using atmospherically corrected POSP observations, avoiding errors caused by using LSR products from other LSR products. 1. Introduction Aerosols can affect the climate through direct and indirect radiative effects (King et al. 1999 ). Meanwhile, it represents significant atmospheric pollution that seriously endangers human health (Yang et al. 2019 ; Li et al. 2017 ; Ramanathan and Carmichael 2008 ). Successive reports from the Intergovernmental Panel on Climate Change (IPCC) have underscored the pivotal role of aerosols in global climate dynamics. However, compared to greenhouse gases, limited understanding of the spatial distribution and composition of aerosols has resulted in substantial uncertainties regarding their impact on radiative forcing (IPCC and others 2013; Mishchenko et al. 2007 ). Knowledge of the global distribution and composition of aerosol properties is essential for reducing uncertainties surrounding their contribution to climate change mitigation efforts (Popp et al. 2016 ; Chu et al. 2002 ). Aerosol sources can be mainly categorized into natural and anthropogenic sources (Pósfai et al. 1999 ). Anthropogenic sources, stemming from modern industrial activities, may include gases and heavy metal particles that pose great threats to human health. Prolonged exposure to particulate matters such as PM 2.5 , PM 10 , and others poses significant health hazards. More importantly, these health risks are not confined solely to anthropogenic sources (Yang et al. 2019 ; He and Huang 2018 ; Cohen et al. 2017 ; Pope et al. 2009 ). Aerosol atmospheric chemistry and changes in aerosol concentrations have garnered increasing attention as environmental concerns escalate. Satellite-based detection information comes from both surface and atmospheric contributions. The atmospheric component is influenced by the scattering and absorption of aerosol particles and gas molecules (Liou 2002 ), where aerosol particles play a significant role, characterized by their spectral distribution, complex refractive index, and vertical distribution (Dubovik et al. 2002 ). Leveraging extensive aerosol climatological data and cluster analysis of ground-based observations, an approximate aerosol model with spatial distribution can be established (Levy et al. 2007a ). Surface contribution can be determined through prior knowledge, referencing existing data (Hsu et al. 2004 ), or approximating relationships across different spectral bands (Kaufman et al. 1997 ). It is noteworthy that under the condition of low AOD loading, even minor errors, such as a 0.01 deviation in LSR estimation, may lead to significant uncertainties of 0.1 in AOD retrieval (Kaufman et al. 1997 ). Hence, achieving precise AOD retrievals necessitates accurate estimation of LSR. Aerosol retrieval algorithms have undergone significant development since the 1970s (Remer et al. 2020 ), resulting in numerous high-quality algorithms for sensors with varying characteristics. With AVHRR, the longest continuous satellite sensor to date, the NASA Deep Blue Science Team (NASA DST) developed the world's first global aerosol product (Hsu et al. 2017 ). The first sensors used for quantitative inversion of AOD over land were the Along Track Scanning Radiometer (ASTR), ASTR-2, and the Advanced Along Track Scanning Radiometer (AATSR). ASTR-2 and AATSR are collectively referred to as (A)ASTR. Songacheva et al.(2013) developed the earliest aerosol global product, ADV/ASV (ATSR Dual View/ATSR Single View). Swansea University and the University of Oxford have then released the ASTR SU and ASTR ORAC (Oxford-RAL aerosol and cloud) aerosol products (Thomas et al. 2009 ). The Multiangle Imaging Spectroradiometer (MISR) and Moderate-Resolution Imaging Spectroradiometer (MODIS), both on board the Terra satellite, have more angles (9) and bands (36) than the sensors described above. Diner et al., ( 2005 ) obtained aerosol products based on MISR observations using the Empirical Orthogonal Function (EOF) method for surface constrain. Kaufman et al., ( 1997 ) developed an early MODIS AOD retrieval algorithm over dark land targets based on the empirical relationship between the LSRs of the 2.12 µm and 0.47 µm. Levy et al. ( 2005 ) found that the empirical relationship did not apply to some surfaces. They introduced the Normalized Difference Vegetation Index (NDVI SWIR ) to estimate the LSR in the 0.47 µm, and improved the accuracy of the AOD retrievals (Levy et al. 2007b , 2013 ). However, both studies didn’t obtain high-precision AOD retrievals on bright surfaces. To address this issue, Hsu et al. ( 2004 ) developed the Deep Blue (DB) algorithm using the Minimum Reflectivity Technique (MRT), and retrieved high-precision AOD over bright surface areas (Sayer et al. 2013 ). In recent years, the MAIAC team has developed higher spatial resolution (1km) and high-precision aerosol products based on MODIS observations on Terra and Aqua (Lyapustin et al. 2011b , 2011a , 2021 ). The effectiveness of all the aforementioned algorithms hinges on the accurate estimation of LSR. In contrast to multi-angle sensors can provide more comprehensive angular information for LSR estimation, single-angle sensors rely on additional constraints due to the limited number of observations (Si et al. 2021 ). However, the relationship between specific bands is not always linear due to significant variations in feature composition among different surface types (Levy et al. 2013 ). It was found that the accuracy of LSR estimation can be increased by utilizing the spectral dependence between multiple bands with different characteristic spectral shapes (Shi et al. 2023 ). In recent years, China has made significant strides in aerosol detection by launching on-board sensors, which has effectively bridged the gap left by the discontinued POLDER series of sensors (Shi et al. 2023 ; Ge et al. 2022 ; Li et al. 2018 , 2022d , 2022c ). The development holds immense importance for the global scientific community. The POSP sensor is an important sensor among these advancements, which is a high-precision polarimetric scanner first mounted on the sun-synchronous orbiting satellite GF5-02 launched on September 7, 2021 (Li et al. 2022a ). The first POSP AOD algorithm was proposed during the POSP on-orbit period, and it utilized the long-term reconstructed land surface reflectance (derived from MODIS LSR product) to construct the surface constraints supporting AOD retrieval (Shi et al, 2023 ). While the reconstructed surface reflectance is as consistent as possible with the POSP spectral response function, potential inconsistencies remain in some cases. With its relatively longer presence in orbit, POSP has amassed substantial observations to facilitate precise estimation of LSR. Leveraging POSP's global observations spanning one year (January 2022 to December 2022), we proposed a new model for estimating LSR. The model utilizes the atmospherically corrected POSP LSR and ensures that the reflectance is fully self-consistent with the POSP spectral response. Therefore, accurate calculation of surface contribution becomes imperative before retrieval. However, reliance on pre-established LSR libraries may introduce inaccuracies. Under these challenges, this study proposed an aerosol retrieval method utilizing comprehensive band information to achieve a more precise LSR estimation. A long-term series of LSR datasets were obtained based on the existing POSP AOD product for atmospheric correction (Shi et al. 2023 ). A quantitative relationship between LSR across different channels was established using statistical analysis for surface constraints, and AOD was retrieved using the optimal search method after the linearization of Look-Up Tables (LUTs) and the construction of a cost function. The rest of the paper is organized as follows. Section 2 presents the materials and data preparation for this study. Section 3 provides a detailed description of the proposed surface reflectance estimation model for POSP and the POSP AOD retrieval algorithm. Section 4 illustrates the results, validation as well as accuracy assessment of the proposed estimation model, and conclusions and discussions are shown in Section 5. 2. Materials 2.1. POSP Data POSP is a new generation polarization sensor that shows good capabilities for exploring aerosol climate. It includes stokes vector [I, Q, U] observations of nine spectral bands ranging from 380 nm to 2250 nm. Detailed information on the POSP sensor is given in Table 1 . Since it is equipped with on-board radiometric and polarimetric calibration systems of exceptional accuracy, POSP achieves a radiometric calibration accuracy ( ΔI ) within 5% and a polarimetric calibration accuracy ( ΔDOLP ) within 0.005 of linear polarization (Li et al. 2022b ). This study focuses exclusively on AOD retrieval utilizing intensity data. POSP Level 1 (L1) data is archived in HDF5 format files, containing essential parameters such as observation geometry, and stokes vector observation data of individual pixels. Table 1 POSP Sensor Band Setting Band Number Central wavelength (nm) Spectral bandwidth(nm) SNR 1 380 ± 3 20 ± 3 333.04 2 410 ± 3 20 ± 3 967.61 3 443 ± 3 20 ± 3 1517.65 4 490 ± 3 20 ± 3 1599.73 5 670 ± 5 20 ± 3 2029.47 6 865 ± 5 40 ± 5 2927.03 7 1380 ± 5 40 ± 10 3585.42 8 1610 ± 15 60 ± 10 3914.96 9 2250 ± 15 80 ± 10 482.55 Calibration error ΔI < 5% ΔDOLP < 5% To obtain high-precision atmospheric corrections, it is recommended to use satellite observations of AOD data at the same time. This is because the aerosol composition of the atmosphere undergoes significant changes over time. Shi et al. ( 2023 ) have implemented an AOD retrieval algorithm with considerable accuracy by utilizing a small amount of POSP observations during the on-orbit test period. In this study, the past AOD products were used as a source of atmospherically-corrected data for atmospheric corrections to generate global LSRs for the year 2022. 2.2. AERONET AOD product The AERONET collects global aerosol data (Dubovik and King 2000 ; Holben et al. 1998 ). It has been used as baseline data because the products of AERONET have low uncertainties, approximately 0.01–0.02 (Borde and Verdebout 2003 ). AERONET provides datasets of AOD at three different quality levels. In this paper, Level 2.0 data are used to verify the accuracy of the retrieval algorithm, which has undergone screening for cloud identification, instrumental anomaly monitoring, and quality control. Level 1.5 data are used to verify the accuracy of the retrieved AOD for January 2023, which has undergone screening for cloud identification and instrument anomaly monitoring. As most satellite AOD products are at 550 nm, AERONET AOD data must be interpolated to obtain AOD at the same wavelength. The AOD versus observed wavelength empirical equation given by Ångström, ( 1929 ) is used for this purpose: $$\begin{array}{c}{\tau }_{\lambda }=\beta {\lambda }^{-\alpha }\#\left(1\right)\end{array}$$ Where \(\lambda\) is the specified wavelength in microns \({\tau }_{\lambda }\) the AOD at the \(\lambda\) wavelength, \(\alpha\) is the Ångström exponent (AE), which characterizes the aerosol particle size. The AOD at 550 nm can be calculated by selecting the AOD in a band similar to 550 nm. This study focuses on aerosol retrieval over land; therefore, AERONET stations within the ocean or close to the coastline were excluded. Based on the statistics, there are currently 1366 valid Level 2.0 stations worldwide in 2022. After excluding stations that do not meet the requirements mentioned above, the number of remaining stations is 944. The spatial distribution of these stations is illustrated in Fig. 1 . 2.3. Other auxiliary data 2.3.1. MODIS Land cover type product To rigorously assess the precision of the newly developed LRS estimation model across diverse land cover types, we leveraged the MODIS MCD12Q1 surface classification product (500m spatial resolution, 1-year temporal resolution) for the year 2022. To align with the dimensions of the satellite matching window (19.2×19.2km), we reprojected the official MCD12Q1 dataset and transformed it into equidistant latitude and longitude results (0.005°×0.005°). Subsequently, within a 40×40 window centered on the AERONET site, we tabulated the distribution of ground cover types. The surface cover type with the highest frequency of occurrence within this window was designated as the predominant surface cover type for the site. Due to constraints such as the distribution of AERONET sites and matching criteria, certain land cover classifications may have limited matching data. Consequently, our study focused on 12 primary land cover classifications, including evergreen broadleaf forest, evergreen needleleaf forest, deciduous broadleaf forest, barren land, croplands, woody savannas, grassland, savanna, mixed forests, open shrublands, urban areas, and water bodies. 2.3.2. ERA5 data the POSP sensor operates across multiple wavelength bands, each subject to distinct gas absorptions. Specifically, the 490 nm and 670 nm bands are predominantly influenced by ozone absorption, while the 1610 nm band is primarily affected by carbon dioxide absorption, and the 2250 nm band experiences slight water absorption. To ensure accurate surface reflectance values, it is imperative to account for the impact of gas absorption characteristics during the atmospheric correction process. ERA5, developed and maintained by the European Centre for Medium-Range Weather Forecasts (ECMWF), stands as a comprehensive global climate reanalysis dataset. Notably, ERA5 boasts a high spatial resolution of 0.25° (Dee et al. 2011 ). It is pertinent to emphasize that the absorption of gases has minimal impact on the different wavelengths of POSP sensors, thereby ensuring that reanalyzed data meet the stringent accuracy requirements for atmospheric corrections. 3. Methodologies Although MODIS LSR products were proven high accuracy (Liang et al. 2002 ). When applied to POSP, errors may accumulate due to factors such as spatial resolution, spectral response function, and observation geometry. To address the issue above, we use one full year of POSP observations in 2022 to establish a new surface constraint. Figure 2 shows the new surface constraints developed in this study. The model is built at each grid by dividing the globe into equal latitude and longitude grids (0.05°) and using the POSP data for the entire year of 2022. The surface constraint is selected for inversion based on the geographic location corresponding to the pixel. The LUTs for that part are linearized based on the given observation geometry. Finally, the AOD is obtained from the processing by using the optimal LUT search method on the linearized LUTs. Other parts of the retrieval processing can be found in supplementary information. 3.1. Improved LSR model In this study, we use the correlation coefficient (R), root mean square error (RMSE), and expectation error (EE, ± 0.025) to evaluate the accuracy of different surface constraints. By using the atmospherically corrected POSP LSR, we re-fit the empirical formula between NDVI and band ratio proposed by Shi et al., ( 2023 ), and re-find the blue band LSR estimated by this method. As shown in Fig. 3 (a), it is found to be less stable in part of the surface case. Since it is more cumbersome to calculate the blue band, which is not conducive to the subsequent construction of the cost function, this study proposes a simpler method to estimate the LSR. As shown in Fig. 3 (b), the results are found to have a more stable estimation accuracy after comparing all POSP observations matched with the AERONET site. The LSR of the blue band is estimated through linear fitting of other longer wavelength bands. This involves employing a high-order polynomial to fit LSR from the 670 nm, 865 nm, 1610 nm, and 2250 nm channels. Additionally, the statistical relationship between the scattering angle at the time of observation and the 410/443/490 nm channels is considered. After verifying the different fitting relationships it was found that polynomials can achieve higher accuracy with lower complexity. Finally, the empirical relational equation was constructed using a third-order polynomial and fitted using ridge regression, balancing algorithm efficiency, and accuracy considerations. $$\begin{array}{c}{\rho }_{{\lambda }_{blue}}={a}_{0}+\sum _{i=1}^{num}\left({a}_{3i-3}{\rho }_{{\lambda }_{i}}+{a}_{3i-2}{\rho }_{{\lambda }_{i}}^{2}+{a}_{3i-1}{\rho }_{{\lambda }_{i}}^{3}\right)+\sum _{i=1}^{num}\sum _{j=2}^{num}\left({b}_{ij}{\rho }_{{\lambda }_{i}}{\rho }_{j}\right)+\\ \sum _{i=1}^{num}\sum _{j=2}^{num}\sum _{k=3}^{num}\left({c}_{ijk}{\rho }_{{\lambda }_{i}}{\rho }_{{\lambda }_{j}}{\rho }_{{\lambda }_{k}}\right)+\dots \#\left(2\right)\end{array}$$ Where num is the number of variables involved in the fit, num is taken as 5, and \({a}_{0}\) is the bias term. \({\rho }_{{\lambda }_{i}}\) denotes \({\rho }_{670}\) , \({\rho }_{865}\) , \({\rho }_{1610}\) , \({\rho }_{2250}\) , and scattering angle ( \({\Theta }\) ), respectively. The construction of the model consists of three main steps: (a) Screening of POSP AOD products for 2022, taking into account the impact of aerosol model on atmospheric corrections at high AOD loadings, with atmospheric corrections only for AOD < 0.2, (b) atmospheric correction of all bands of satellite observations to obtain the corresponding LSRs, and (c) calculate the statistical relationships between \({\rho }_{410\backslash 443\backslash 490}\) and \({\rho }_{670}\) , \({\rho }_{865}\) , \({\rho }_{1610}\) , \({\rho }_{2250}\) , and scattering angle. This relationship is used in the POSP aerosol algorithm for surface constrain. 3.2. Linearization of LUTs The study still uses Shi et al., ( 2023 ) improved global aerosol model for retrieval, and a new LUT has been established after considering gas absorption. Pseudo-LSRs obtained from conventional atmospheric corrections using a given AOD value do not satisfy the surface constraints, because it only considers the dependence of the surface contribution of each band. As a result, there is an uncertain error in modeling TOA using this result followed by linear interpolation. To address this issue, we used an optimal LUTs search method that constructs a cost function to search for the AODs that satisfy the surface constraints, which avoids the errors caused by linear interpolation based on the inaccurate pseudo-LSR. To use optimization techniques, it is necessary to construct a forward model that has been linearized, in this study by linearizing the LUTs to construct a forward model of the reflectance at TOA. Within the LUTs, the atmospheric parameters are modified only in response to changes in AOD once the observation geometry has been determined. Polynomial fitting procedures are then employed, with the atmospheric parameters serving as dependent variables and AOD as the sole independent variable. $$\begin{array}{c}{parm}_{{\lambda }}={a}_{0}+{a}_{1}*AOD+{a}_{2}*{\text{A}\text{O}\text{D}}^{2}+\dots \#\left(3\right)\end{array}$$ Where \({parm}_{{\lambda }}\) is the atmospheric parameter at wavelength \({\lambda }\) . While higher-order polynomials could enhance fitting precision, experimentation in this study revealed that the incremental improvement in accuracy with increased polynomial order was outweighed by the computational overhead. Considering the retrieval efficiency, a third-order polynomial is used for the fitting, and a certain condition is chosen as a case study for the accuracy demonstration, the satellite observation of this condition, with the solar zenith angle of 25.95°, the observation zenith angle of 24.5°, the relative azimuth angle of 130.68°, and the AOD of AERNET ground-based observation is 1.1. The difference between the atmospheric correction using the results of the linear fit and the direct correction using LUTs interpolation is also shown in Fig. 4 , with the RMSE < 0.004 and Mean Bias (MB) < 0.004 suggesting that the two can be considered approximately equal. 3.3. Pixel matching strategy for validations AERONET site observations vary temporally but are spatially fixed, satellite observations vary spatially but are temporally fixed. The key to successfully validating satellite data is how to match the data mentioned above. After years of research, various matching strategies have been proposed for different satellite products (Virtanen et al. 2018 ; Sayer et al. 2013 ; Chu et al. 2002 ; Ichoku et al. 2002 ). AERONET sites usually provide observations every 15 minutes (Dubovik et al. 2002 ), and the POSP nadir point has a spatial resolution of 6.4km, considering the composition of atmospheric aerosols changes rapidly over time. We finally choose the matching rule that the mean value of AERONET AOD (at least 2 AOD records) within an hour of GF5–02 satellite transit is used to compare with the spatial mean value of POSP AOD in a 3 × 3 window (at least three AOD values available in the window) centered on the AERONET site. It's important to note that since surface constraints differ across different bands, the quality of output results is governed by a uniform accuracy index, leading to variations in the number of final matching results. 4. Results and validation 4.1. Verification of the improved LSR model Since the use of historical POSP AOD products introduces unavoidable errors that affect the accuracy of the retrievals, this study retrieves the pixels that overlap with the AERONET site and using high-precision AOD observations at the AERONET site instead of the POSP AOD products to assess the uncertainties introduced by the LSR estimation alone. Following the matching strategy above, we get a benchmark dataset to verify the improved LSR model. Ozone content data are sourced from AERONET site. We evaluated the feasibility of the reconstruction model described in Eq. (2). The reconstructed POSP LSRs at 410 nm, 443 nm, and 490 nm were compared with the results accurately corrected for atmospheric effects, as illustrated in Fig. 5 , 6 , and 7 . The estimated LSR errors mostly fell within the EE range, with regressions closely aligned with the 1:1 line. Upon comparing the estimated results of the three blue bands, it was observed that the 490 nm band exhibited the most favorable overall statistics. This outcome could be attributed to the spectral proximity of 490 nm to the other bands involved in the estimation. Further analysis of estimation accuracies across the 12 IGBP ground cover classification cases revealed that the estimation accuracy was lowest for barren areas, followed by mixed forests and evergreen needleleaf forests. 4.2. Validation of AOD retrieval algorithm To assess the feasibility of the new LUTs search method, we employed three blue bands for AOD retrieval. It's important to note that the surface constraints were established using the LSR obtained by atmospherically correcting the POSP observation with the AOD values from the AERONET site. This reduced the error in the LSR estimation caused by the uncertainty in the AOD product. Thus, we can get the accuracy of the AOD retrieval algorithm without considering the accuracy of the auxiliary data. Temporal and spatial matching of POSP with AERONET was conducted to obtain the verification dataset for testing. Except for the Expected Error (EE) envelope which has been commonly used for MODIS validation (Remer et al. 2005 ), We also adopted stricter requirements proposed by the Global Climate Observing System (GCOS) (the greater of 0.03 or 10%), which have been adopted in the Aerosol_cci study (Popp et al. 2016 ). Notably, the POSP AOD (67.62%) demonstrated a high percentage of validation results within the %EE range, and 60.05% validation results within the GCOS range. The matches for 410 nm, 443 nm, and 490 nm are 5218, 6632, and 9067, respectively. Most of the retrievals are obtained under low AOD load conditions. The validation results based on the 490 nm observation are shown in Fig. 8 (c), with R of 0.837 and RMSE of 0.061. The percentage of retrievals falling within the expected error (%EE) \((\pm 0.05 + 0.15{\text{A}\text{O}\text{D}}_{\text{A}\text{E}\text{R}\text{O}\text{N}\text{E}\text{T}})\) is 84.98%, and within the GCOS is 76.17%, indicating reliable results in the vast majority of cases. The POSP's low spatial resolution may result in some pixels having fine cloud amounts that the cloud detection algorithm cannot capture. Consequently, the apparent reflectance of these pixels is generally higher than that of the pixels under cloud-free conditions, leading to an overestimation of the AOD. However, the underestimation of AOD is primarily attributed to the partial bias of the established surface constraints, resulting in a significant discrepancy between the AOD retrievals and the ground-based observations during the optimization iteration process. Figure 8 (a) displays the AOD retrievals of 410 nm observations, with an R of 0.799 and an RMSE of 0.075. Similarly, Fig. 8 (b) shows the AOD retrievals of 443 nm observations, with R of 0.788 and RMSE of 0.076. The results highlight that the accuracy of the AOD retrievals is largely determined by the accuracy of the surface constraints. To use more blue bands brought about by the increase in the amount of information, this study through the use of a surface constraint model fitting the highest accuracy of the band for retrieval, has verified that the retrieval accuracy can be improved, but the cost of computation is significantly higher, taking into account the time of operation, and ultimately choose to use only 490nm for AOD retrieval. 4.3. Application for POSP AOD inversion The validation conducted above suggests that the new method is capable of producing high-precision AOD retrievals. Adapting this method for global AOD retrieval, necessitates LSR data meticulously corrected for atmospheric influences across diverse geographical locations. Hence, we opted for existing POSP AOD products for the whole of 2022 for robust atmospheric correction. Earth was partitioned into equal 0.05° intervals, and data from each band with AOD values below 0.2 were chosen for atmospheric correction to establish surface constraints. AODs for January 2023 were retrieved using observations solely from POSP at 490 nm. Figure 9 illustrates the global monthly mean AOD distribution for January 2023, derived from POSP. The study underscored the need for high-precision historical AOD data to support the proposed method. However, certain areas with persistent high AOD loadings present challenges, leading to substantial uncertainties in atmospheric correction. Consequently, retrievals for such regions were not provided in this study. The validation results revealed a strong correlation between the POSP AOD and AERONET AOD, with R of 0.703 and RMSE of 0.068. However, it should be noted that the POSP AOD showed significant underestimation at high AOD loads, and by calculating the bias of AOD at different loads, it was found that bias=-0.002 at AOD < 0.2, bias=-0.099 at 0.2 < = AOD = 0.7. These biases may stem from surface inhomogeneity due to the lower resolution of POSP and the presence of mixed pixels, which tend to overestimate the LSR in the blue band. To enhance the scrutiny of the new LSR estimation method's reliability in AOD retrieval, a verification dataset was meticulously curated by extracting POSP and AERONET observations within the timeframe of January 2023. Subsequently, a comparative analysis was conducted between the outcomes derived from employing LSR estimates at each site location in the retrieval process and those obtained after rigorous atmospheric correction. Table 2 displays the estimated LSRs \({\rho }_{R,\lambda }^{{\prime }}\) in comparison to the Atmospheric corrected LSRs \({\rho }_{R,\lambda }\) across various IGBP surface types. As retrievals were unavailable, the assessment encompassed solely eight distinct surface types. Notably, Open Shrublands exhibited the highest accuracy, whereas Barren demonstrated the poorest accuracy. It is noteworthy that the majority of vegetation cover and urban areas fell within the %EE exceeding 80% of the estimations. This observation strongly implies the commendable accuracy of the new POSP AOD retrieval algorithm, particularly in vegetated regions. Table 2 LSRs estimation at 490 nm accuracy for different surface types N R RMSE %EE Barren 544 -0.369 0.092 29.04 Croplands 596 0.528 0.027 76.01 Deciduous Broadleaf Forests 381 0.637 0.030 68.24 Grasslands 260 0.740 0.024 84.62 Open Shrublands 270 0.726 0.019 90.00 Savannas 347 0.457 0.020 82.71 Urban and Built up Lands 1681 0.495 0.022 83.22 Woody Savannas 139 0.505 0.020 80.58 5. Conclusion and discussions The traditional linear interpolation LUT-based approach usually uses the pseudo-LSR obtained from the atmospheric correction with the preset AOD in the LUT to simulate the reflectance at TOA. However, the pseudo-LSR does not meet the surface constraints established by true LSR alone. Thus, there is an uncertain amount of error when linearly interpolating the simulated reflectance at TOA. This paper also proposes a novel solution to this challenge and establishes a new surface constraint, thereby accommodating surface type variations. The new surface constraints are full-spectrum LSR self-consistent, combining the optimized LUTs search method results in a full-band LSR that satisfies the surface constraints and therefore uses more spectral information in the inversion process, a concept that can be extended to other AOD retrieval algorithms for similar POSP sensors. Aerosol retrieval is achieved by constructing LUTs using the 6SV radiative transfer code, linearizing them during retrieval, and applying the optimal LUTs search method. A comparison of LSRs estimated and LSRs atmospherically corrected revealed errors predominantly below 0.025 across diverse surface types, underscoring the method's high-precision aerosol retrieval capability. Employing satellite observations overlapping with AERONET sites in 2022 as experimental data, surface constraints were established, and AOD retrieval was conducted. Notably, the highest retrieval accuracy was observed at 490 nm, with R = 0.837, RMSE = 0.061, KAPPA = 0.42, and EE%=84.98. However, future research must address several areas for improvement. Firstly, retrieval accuracy for higher AOD cases is heavily influenced by the aerosol model, necessitating consideration of predefined aerosol models with spatial and temporal variations to mitigate errors due to lack of a priori knowledge. Secondly, the assumption of a Lambertian surface overlooks variations in LSR under different observational conditions, warranting consideration of the bi-directional reflectance distribution function (BRDF) to minimize errors in LSR estimation. Thirdly, areas with high AOD loads pose challenges due to uncertainties associated with historical AOD data, thus necessitating refinement of atmospheric correction procedures in such regions. Fourthly, it is noteworthy that under conditions of high AOD loads, POSP AOD demonstrates a notable tendency towards underestimation. This phenomenon may be ascribed to the lower spatial resolution of POSP, which in turn leads to an overestimation of LSR particularly in intricate mixed-pixel scenarios. Lastly, extending surface constraint establishment beyond 2022 and overcoming limitations in observation availability due to stringent screening conditions would enhance the method's applicability and robustness. Declarations Author Contributions All authors contributed to the study's conception and design. All authors read and approved the final manuscript. Zhe Ji: Conceptualization, Methodology, data collection, Material preparation, Analysis, and Writing Original Draft. Zhengqiang Li: Supervision, Funding acquisition. Ying Zhang and Zheng Shi: Conceptualization, Supervision, and Writing - Review & Editing. Yisong Xie: Supervision, Writing - Review & Editing. Xiaoxi Yan, Yan Man, and Yang Zheng: Writing - Review & Editing. Zhenting Chen: Funding acquisition. Acknowledgments The data used for this study is available at https://aeronet.gsfc.nasa.gov/ (AERONET website). We would like to thank all the sites staffs for the data used for this study. We thank the Level 1 data of POSP provided by the China Centre for Resources Satellite Data and Application (https://data.cresda.cn/). ERA5 data were generated using Copernicus Climate Change Service Information (https://cds.climate.copernicus.eu/). Neither the European Commission nor the ECMWF are responsible for any use that may be made of the Copernicus information or data in this publication. Funding This work was supported by the National Key R&D Program of China (2022YFC3704000), the National Natural Science Foundation of China (grant nos.42101365, 41925019), the Foreign Technical Cooperation and Scientific Research Program (E3KZ0301) and Li zhengqiang Expert Workstation of Yunnan Province(202205AF150031). Competing interests The authors have no competing interests to declare that are relevant to the content of this article. Ethical approval The authors confirm that the manuscript has been read and approved by all authors. Consent to participate All the authors have agreed to authorship, read, and approved the manuscript. Consent to publish All the authors mentioned in the manuscript approve the version to be published. References Ångström, A. (1929). On the Atmospheric Transmission of Sun Radiation and on Dust in the Air. Geografiska Annaler 11 (2):156–166. doi:10.1080/20014422.1929.11880498. Borde, R. and Verdebout, J. (2003). 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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-4161991","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":285191497,"identity":"e90754a6-2a24-4250-8ed0-11d0b14ee80b","order_by":0,"name":"Zhe 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1","display":"","copyAsset":false,"role":"figure","size":12479630,"visible":true,"origin":"","legend":"\u003cp\u003eThe distribution of AERONET sites used in the dataset (land sites are represented by pink circles, coastal/ocean sites are represented by blue circles\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-4161991/v1/40724780d4b3c4fc3ee8740b.png"},{"id":53940359,"identity":"65f29bbd-a447-4da3-b2ce-93ac83aeb945","added_by":"auto","created_at":"2024-04-02 13:07:07","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":725704,"visible":true,"origin":"","legend":"\u003cp\u003eThe overall flowchart of the improved POSP AOD retrieval algorithm\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-4161991/v1/e8ef4b5485670c2e693f1f16.png"},{"id":53940365,"identity":"55953f7e-c662-442d-8350-bfb6a15ddbfc","added_by":"auto","created_at":"2024-04-02 13:07:08","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":10463733,"visible":true,"origin":"","legend":"\u003cp\u003eExpected difference between atmospherically corrected surface reflectance and simulated surface reflectance at (1) 410nm, (2) 443nm, and (3) 490nm, the dotted line is the expected error (EE) of ±0.025\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-4161991/v1/6471d710847b3e3d972bdfc9.png"},{"id":53940711,"identity":"b60f0ec0-933d-44a4-b9c9-c5d17844b589","added_by":"auto","created_at":"2024-04-02 13:15:08","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":6406503,"visible":true,"origin":"","legend":"\u003cp\u003eThe plot of fitting errors of linearized LUTs at different bands\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-4161991/v1/fdfbfe4cab6a700240b8832d.png"},{"id":53940358,"identity":"778a2626-ac39-44cd-9b4d-1e3c4a1d35ab","added_by":"auto","created_at":"2024-04-02 13:07:07","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":7601689,"visible":true,"origin":"","legend":"\u003cp\u003eExpected difference between atmospherically corrected surface reflectance and simulated surface reflectance at 410nm over (a) Barren, (b) Croplands (c) Deciduous Broadleaf Forests, (d) Evergreen Broadleaf Forests, (e) Grasslands, (f) Mixed Forests, (g) Open Shrublands, (h) Savannas, (i) Urban and Built-up Lands, (j) Water Bodies, (k) Woody Savannas\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-4161991/v1/019d131c4baf83889eb1e635.png"},{"id":53940360,"identity":"bc3cf74b-2567-4e9a-9593-4cda23c2fd6f","added_by":"auto","created_at":"2024-04-02 13:07:08","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":7509979,"visible":true,"origin":"","legend":"\u003cp\u003eThe same as \u003cstrong\u003eFig. 5\u003c/strong\u003e but surface reflectance at 443nm\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-4161991/v1/e5563cf849f2130ea5b9130a.png"},{"id":53940369,"identity":"4827ff72-c0e0-4ae1-b6a0-e672cd70d564","added_by":"auto","created_at":"2024-04-02 13:07:09","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":6734404,"visible":true,"origin":"","legend":"\u003cp\u003eThe same as \u003cstrong\u003eFig. 5\u003c/strong\u003ebut surface reflectance at 490nm\u003c/p\u003e","description":"","filename":"floatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-4161991/v1/55fe76bf343856e07eabba9d.png"},{"id":53940364,"identity":"85cd1282-ffbf-4bca-9430-b6de1a6c8957","added_by":"auto","created_at":"2024-04-02 13:07:08","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":3559458,"visible":true,"origin":"","legend":"\u003cp\u003eValidation of the POSP AOD against AERONET AOD data in verification dataset (January 2022 to December 2022), where N—number of match collocations, R—Pearson correlation coefficient, RMSE—root mean square error, and %EE—data fraction within EE bounds. The one-to-one agreement is represented by the black dotted line. The red line represents the linear regression fit, and the black dashed lines are EE lines. The magenta points are means for specific ranges of AERONET and satellite AOD, and the magenta lines are the mean ±2σ of retrievals in a certain range. The grey region with a border of the dotted line indicates the EE envelope of ±(0.05 + 0.15AOD\u003csub\u003eAERONET\u003c/sub\u003e)\u003c/p\u003e","description":"","filename":"floatimage8.png","url":"https://assets-eu.researchsquare.com/files/rs-4161991/v1/0b97d72060de955c2e120546.png"},{"id":53940366,"identity":"1b6df62f-adea-49f4-b53e-cd2dc12b1b96","added_by":"auto","created_at":"2024-04-02 13:07:08","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":4824937,"visible":true,"origin":"","legend":"\u003cp\u003eGlobal spatial distribution of POSP AOD in January 2023\u003c/p\u003e","description":"","filename":"floatimage9.png","url":"https://assets-eu.researchsquare.com/files/rs-4161991/v1/0765ffdb922d2d5fa2aca258.png"},{"id":53940368,"identity":"fe3fe6f0-fa63-4d8b-94ba-7d359c3dbbb1","added_by":"auto","created_at":"2024-04-02 13:07:09","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":1101652,"visible":true,"origin":"","legend":"\u003cp\u003eValidation of the POSP AOD against AERONET AOD data in January 2023, where N—number of match collocations, where N—number of match collocations, R—Pearson correlation coefficient, RMSE—root mean square error, and %EE—data fraction within EE bounds. The one-to-one agreement is represented by the black dotted line. The red line represents the linear regression fit, and the black dashed lines are EE lines. The magenta points are means for specific ranges of AERONET and satellite AOD, and the magenta lines are the mean ±2σ of retrievals in a certain range. The grey region with a border of the dotted line indicates the EE envelope of ±(0.05 + 0.15AOD\u003csub\u003eAERONET\u003c/sub\u003e).\u003c/p\u003e","description":"","filename":"floatimage10.png","url":"https://assets-eu.researchsquare.com/files/rs-4161991/v1/bc85aefbd4c1cf5f6a4036e4.png"},{"id":63071139,"identity":"4b29492e-03fc-482c-96f8-dddbd7a9f6e0","added_by":"auto","created_at":"2024-08-22 20:04:07","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":63958389,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4161991/v1/9d5f82d6-7a75-4141-96ba-01e335f17062.pdf"},{"id":53940361,"identity":"ccc1726d-c368-43d3-9211-254bfd91baa8","added_by":"auto","created_at":"2024-04-02 13:07:08","extension":"docx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":22255,"visible":true,"origin":"","legend":"","description":"","filename":"SISubmit.docx","url":"https://assets-eu.researchsquare.com/files/rs-4161991/v1/84bc1c45d088785812b44da8.docx"}],"financialInterests":"","formattedTitle":"Aerosol Optical Depth Retrieval on Particulate Observing Scanning Polarimeter (POSP) Data over Land using a new Look-up table (LUT) Search Method","fulltext":[{"header":"Article Highlights","content":"\u003col\u003e\n \u003cli\u003eNew full-spectrum LSR self-consistent surface constraints are constructed based on the spectrum properties of the POSP sensor and surface orientation properties.\u003c/li\u003e\n \u003cli\u003eThe optimal LUT search method is used to avoid the error caused by the old linear interpolation method.\u003c/li\u003e\n \u003cli\u003eLSRs are obtained by using atmospherically corrected POSP observations, avoiding errors caused by using LSR products from other LSR products.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"1. Introduction","content":"\u003cp\u003eAerosols can affect the climate through direct and indirect radiative effects (King et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e1999\u003c/span\u003e). Meanwhile, it represents significant atmospheric pollution that seriously endangers human health (Yang et al. \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Li et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Ramanathan and Carmichael \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). Successive reports from the Intergovernmental Panel on Climate Change (IPCC) have underscored the pivotal role of aerosols in global climate dynamics. However, compared to greenhouse gases, limited understanding of the spatial distribution and composition of aerosols has resulted in substantial uncertainties regarding their impact on radiative forcing (IPCC and others 2013; Mishchenko et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). Knowledge of the global distribution and composition of aerosol properties is essential for reducing uncertainties surrounding their contribution to climate change mitigation efforts (Popp et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Chu et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). Aerosol sources can be mainly categorized into natural and anthropogenic sources (P\u0026oacute;sfai et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e1999\u003c/span\u003e). Anthropogenic sources, stemming from modern industrial activities, may include gases and heavy metal particles that pose great threats to human health. Prolonged exposure to particulate matters such as PM\u003csub\u003e2.5\u003c/sub\u003e, PM\u003csub\u003e10\u003c/sub\u003e, and others poses significant health hazards. More importantly, these health risks are not confined solely to anthropogenic sources (Yang et al. \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; He and Huang \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Cohen et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Pope et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Aerosol atmospheric chemistry and changes in aerosol concentrations have garnered increasing attention as environmental concerns escalate.\u003c/p\u003e \u003cp\u003eSatellite-based detection information comes from both surface and atmospheric contributions. The atmospheric component is influenced by the scattering and absorption of aerosol particles and gas molecules (Liou \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2002\u003c/span\u003e), where aerosol particles play a significant role, characterized by their spectral distribution, complex refractive index, and vertical distribution (Dubovik et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). Leveraging extensive aerosol climatological data and cluster analysis of ground-based observations, an approximate aerosol model with spatial distribution can be established (Levy et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2007a\u003c/span\u003e). Surface contribution can be determined through prior knowledge, referencing existing data (Hsu et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2004\u003c/span\u003e), or approximating relationships across different spectral bands (Kaufman et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e1997\u003c/span\u003e). It is noteworthy that under the condition of low AOD loading, even minor errors, such as a 0.01 deviation in LSR estimation, may lead to significant uncertainties of 0.1 in AOD retrieval (Kaufman et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e1997\u003c/span\u003e). Hence, achieving precise AOD retrievals necessitates accurate estimation of LSR.\u003c/p\u003e \u003cp\u003eAerosol retrieval algorithms have undergone significant development since the 1970s (Remer et al. \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), resulting in numerous high-quality algorithms for sensors with varying characteristics. With AVHRR, the longest continuous satellite sensor to date, the NASA Deep Blue Science Team (NASA DST) developed the world's first global aerosol product (Hsu et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). The first sensors used for quantitative inversion of AOD over land were the Along Track Scanning Radiometer (ASTR), ASTR-2, and the Advanced Along Track Scanning Radiometer (AATSR). ASTR-2 and AATSR are collectively referred to as (A)ASTR. Songacheva et al.(2013) developed the earliest aerosol global product, ADV/ASV (ATSR Dual View/ATSR Single View). Swansea University and the University of Oxford have then released the ASTR SU and ASTR ORAC (Oxford-RAL aerosol and cloud) aerosol products (Thomas et al. \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). The Multiangle Imaging Spectroradiometer (MISR) and Moderate-Resolution Imaging Spectroradiometer (MODIS), both on board the Terra satellite, have more angles (9) and bands (36) than the sensors described above. Diner et al., (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2005\u003c/span\u003e) obtained aerosol products based on MISR observations using the Empirical Orthogonal Function (EOF) method for surface constrain. Kaufman et al., (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e1997\u003c/span\u003e) developed an early MODIS AOD retrieval algorithm over dark land targets based on the empirical relationship between the LSRs of the 2.12 \u0026micro;m and 0.47 \u0026micro;m. Levy et al. (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2005\u003c/span\u003e) found that the empirical relationship did not apply to some surfaces. They introduced the Normalized Difference Vegetation Index (NDVI\u003csub\u003eSWIR\u003c/sub\u003e) to estimate the LSR in the 0.47 \u0026micro;m, and improved the accuracy of the AOD retrievals (Levy et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2007b\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). However, both studies didn\u0026rsquo;t obtain high-precision AOD retrievals on bright surfaces. To address this issue, Hsu et al. (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2004\u003c/span\u003e) developed the Deep Blue (DB) algorithm using the Minimum Reflectivity Technique (MRT), and retrieved high-precision AOD over bright surface areas (Sayer et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). In recent years, the MAIAC team has developed higher spatial resolution (1km) and high-precision aerosol products based on MODIS observations on Terra and Aqua (Lyapustin et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2011b\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2011a\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe effectiveness of all the aforementioned algorithms hinges on the accurate estimation of LSR. In contrast to multi-angle sensors can provide more comprehensive angular information for LSR estimation, single-angle sensors rely on additional constraints due to the limited number of observations (Si et al. \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). However, the relationship between specific bands is not always linear due to significant variations in feature composition among different surface types (Levy et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). It was found that the accuracy of LSR estimation can be increased by utilizing the spectral dependence between multiple bands with different characteristic spectral shapes (Shi et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn recent years, China has made significant strides in aerosol detection by launching on-board sensors, which has effectively bridged the gap left by the discontinued POLDER series of sensors (Shi et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Ge et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Li et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2018\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2022d\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2022c\u003c/span\u003e). The development holds immense importance for the global scientific community. The POSP sensor is an important sensor among these advancements, which is a high-precision polarimetric scanner first mounted on the sun-synchronous orbiting satellite GF5-02 launched on September 7, 2021 (Li et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2022a\u003c/span\u003e). The first POSP AOD algorithm was proposed during the POSP on-orbit period, and it utilized the long-term reconstructed land surface reflectance (derived from MODIS LSR product) to construct the surface constraints supporting AOD retrieval (Shi et al, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). While the reconstructed surface reflectance is as consistent as possible with the POSP spectral response function, potential inconsistencies remain in some cases. With its relatively longer presence in orbit, POSP has amassed substantial observations to facilitate precise estimation of LSR. Leveraging POSP's global observations spanning one year (January 2022 to December 2022), we proposed a new model for estimating LSR. The model utilizes the atmospherically corrected POSP LSR and ensures that the reflectance is fully self-consistent with the POSP spectral response. Therefore, accurate calculation of surface contribution becomes imperative before retrieval. However, reliance on pre-established LSR libraries may introduce inaccuracies. Under these challenges, this study proposed an aerosol retrieval method utilizing comprehensive band information to achieve a more precise LSR estimation. A long-term series of LSR datasets were obtained based on the existing POSP AOD product for atmospheric correction (Shi et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). A quantitative relationship between LSR across different channels was established using statistical analysis for surface constraints, and AOD was retrieved using the optimal search method after the linearization of Look-Up Tables (LUTs) and the construction of a cost function.\u003c/p\u003e \u003cp\u003eThe rest of the paper is organized as follows. Section 2 presents the materials and data preparation for this study. Section 3 provides a detailed description of the proposed surface reflectance estimation model for POSP and the POSP AOD retrieval algorithm. Section 4 illustrates the results, validation as well as accuracy assessment of the proposed estimation model, and conclusions and discussions are shown in Section 5.\u003c/p\u003e"},{"header":"2. Materials","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. POSP Data\u003c/h2\u003e \u003cp\u003ePOSP is a new generation polarization sensor that shows good capabilities for exploring aerosol climate. It includes stokes vector [I, Q, U] observations of nine spectral bands ranging from 380 nm to 2250 nm. Detailed information on the POSP sensor is given in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Since it is equipped with on-board radiometric and polarimetric calibration systems of exceptional accuracy, POSP achieves a radiometric calibration accuracy (\u003cem\u003eΔI\u003c/em\u003e) within 5% and a polarimetric calibration accuracy (\u003cem\u003eΔDOLP\u003c/em\u003e) within 0.005 of linear polarization (Li et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2022b\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThis study focuses exclusively on AOD retrieval utilizing intensity data. POSP Level 1 (L1) data is archived in HDF5 format files, containing essential parameters such as observation geometry, and stokes vector observation data of individual pixels.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePOSP Sensor Band Setting\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" 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=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBand Number\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCentral wavelength (nm)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eSpectral bandwidth(nm)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSNR\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e380\u0026thinsp;\u0026plusmn;\u0026thinsp;3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e20\u0026thinsp;\u0026plusmn;\u0026thinsp;3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e333.04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e410\u0026thinsp;\u0026plusmn;\u0026thinsp;3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e20\u0026thinsp;\u0026plusmn;\u0026thinsp;3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e967.61\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e443\u0026thinsp;\u0026plusmn;\u0026thinsp;3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e20\u0026thinsp;\u0026plusmn;\u0026thinsp;3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1517.65\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e490\u0026thinsp;\u0026plusmn;\u0026thinsp;3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e20\u0026thinsp;\u0026plusmn;\u0026thinsp;3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1599.73\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e670\u0026thinsp;\u0026plusmn;\u0026thinsp;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e20\u0026thinsp;\u0026plusmn;\u0026thinsp;3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2029.47\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e865\u0026thinsp;\u0026plusmn;\u0026thinsp;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e40\u0026thinsp;\u0026plusmn;\u0026thinsp;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2927.03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1380\u0026thinsp;\u0026plusmn;\u0026thinsp;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e40\u0026thinsp;\u0026plusmn;\u0026thinsp;10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3585.42\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1610\u0026thinsp;\u0026plusmn;\u0026thinsp;15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e60\u0026thinsp;\u0026plusmn;\u0026thinsp;10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3914.96\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2250\u0026thinsp;\u0026plusmn;\u0026thinsp;15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e80\u0026thinsp;\u0026plusmn;\u0026thinsp;10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e482.55\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCalibration error\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e\u003cem\u003eΔI\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e\u003cem\u003eΔDOLP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;5%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTo obtain high-precision atmospheric corrections, it is recommended to use satellite observations of AOD data at the same time. This is because the aerosol composition of the atmosphere undergoes significant changes over time. Shi et al. (\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) have implemented an AOD retrieval algorithm with considerable accuracy by utilizing a small amount of POSP observations during the on-orbit test period. In this study, the past AOD products were used as a source of atmospherically-corrected data for atmospheric corrections to generate global LSRs for the year 2022.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. AERONET AOD product\u003c/h2\u003e \u003cp\u003eThe AERONET collects global aerosol data (Dubovik and King \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; Holben et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e1998\u003c/span\u003e). It has been used as baseline data because the products of AERONET have low uncertainties, approximately 0.01\u0026ndash;0.02 (Borde and Verdebout \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). AERONET provides datasets of AOD at three different quality levels. In this paper, Level 2.0 data are used to verify the accuracy of the retrieval algorithm, which has undergone screening for cloud identification, instrumental anomaly monitoring, and quality control. Level 1.5 data are used to verify the accuracy of the retrieved AOD for January 2023, which has undergone screening for cloud identification and instrument anomaly monitoring.\u003c/p\u003e \u003cp\u003eAs most satellite AOD products are at 550 nm, AERONET AOD data must be interpolated to obtain AOD at the same wavelength. The AOD versus observed wavelength empirical equation given by \u0026Aring;ngstr\u0026ouml;m, (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1929\u003c/span\u003e) is used for this purpose:\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\begin{array}{c}{\\tau }_{\\lambda }=\\beta {\\lambda }^{-\\alpha }\\#\\left(1\\right)\\end{array}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\lambda\\)\u003c/span\u003e\u003c/span\u003e is the specified wavelength in microns \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\tau }_{\\lambda }\\)\u003c/span\u003e\u003c/span\u003e the AOD at the \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\lambda\\)\u003c/span\u003e\u003c/span\u003e wavelength, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\alpha\\)\u003c/span\u003e\u003c/span\u003e is the \u0026Aring;ngstr\u0026ouml;m exponent (AE), which characterizes the aerosol particle size. The AOD at 550 nm can be calculated by selecting the AOD in a band similar to 550 nm.\u003c/p\u003e \u003cp\u003eThis study focuses on aerosol retrieval over land; therefore, AERONET stations within the ocean or close to the coastline were excluded. Based on the statistics, there are currently 1366 valid Level 2.0 stations worldwide in 2022. After excluding stations that do not meet the requirements mentioned above, the number of remaining stations is 944. The spatial distribution of these stations is illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3. Other auxiliary data\u003c/h2\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003ch2\u003e2.3.1. MODIS Land cover type product\u003c/h2\u003e \u003cp\u003eTo rigorously assess the precision of the newly developed LRS estimation model across diverse land cover types, we leveraged the MODIS MCD12Q1 surface classification product (500m spatial resolution, 1-year temporal resolution) for the year 2022. To align with the dimensions of the satellite matching window (19.2\u0026times;19.2km), we reprojected the official MCD12Q1 dataset and transformed it into equidistant latitude and longitude results (0.005\u0026deg;\u0026times;0.005\u0026deg;). Subsequently, within a 40\u0026times;40 window centered on the AERONET site, we tabulated the distribution of ground cover types. The surface cover type with the highest frequency of occurrence within this window was designated as the predominant surface cover type for the site.\u003c/p\u003e \u003cp\u003eDue to constraints such as the distribution of AERONET sites and matching criteria, certain land cover classifications may have limited matching data. Consequently, our study focused on 12 primary land cover classifications, including evergreen broadleaf forest, evergreen needleleaf forest, deciduous broadleaf forest, barren land, croplands, woody savannas, grassland, savanna, mixed forests, open shrublands, urban areas, and water bodies.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e \u003ch2\u003e2.3.2. ERA5 data\u003c/h2\u003e \u003cp\u003ethe POSP sensor operates across multiple wavelength bands, each subject to distinct gas absorptions. Specifically, the 490 nm and 670 nm bands are predominantly influenced by ozone absorption, while the 1610 nm band is primarily affected by carbon dioxide absorption, and the 2250 nm band experiences slight water absorption. To ensure accurate surface reflectance values, it is imperative to account for the impact of gas absorption characteristics during the atmospheric correction process.\u003c/p\u003e \u003cp\u003eERA5, developed and maintained by the European Centre for Medium-Range Weather Forecasts (ECMWF), stands as a comprehensive global climate reanalysis dataset. Notably, ERA5 boasts a high spatial resolution of 0.25\u0026deg; (Dee et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). It is pertinent to emphasize that the absorption of gases has minimal impact on the different wavelengths of POSP sensors, thereby ensuring that reanalyzed data meet the stringent accuracy requirements for atmospheric corrections.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"3. Methodologies","content":"\u003cp\u003eAlthough MODIS LSR products were proven high accuracy (Liang et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). When applied to POSP, errors may accumulate due to factors such as spatial resolution, spectral response function, and observation geometry. To address the issue above, we use one full year of POSP observations in 2022 to establish a new surface constraint.\u003c/p\u003e \u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows the new surface constraints developed in this study. The model is built at each grid by dividing the globe into equal latitude and longitude grids (0.05\u0026deg;) and using the POSP data for the entire year of 2022. The surface constraint is selected for inversion based on the geographic location corresponding to the pixel. The LUTs for that part are linearized based on the given observation geometry. Finally, the AOD is obtained from the processing by using the optimal LUT search method on the linearized LUTs. Other parts of the retrieval processing can be found in supplementary information.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Improved LSR model\u003c/h2\u003e \u003cp\u003eIn this study, we use the correlation coefficient (R), root mean square error (RMSE), and expectation error (EE, \u0026plusmn;\u0026thinsp;0.025) to evaluate the accuracy of different surface constraints. By using the atmospherically corrected POSP LSR, we re-fit the empirical formula between NDVI and band ratio proposed by Shi et al., (\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), and re-find the blue band LSR estimated by this method. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e (a), it is found to be less stable in part of the surface case. Since it is more cumbersome to calculate the blue band, which is not conducive to the subsequent construction of the cost function, this study proposes a simpler method to estimate the LSR. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e (b), the results are found to have a more stable estimation accuracy after comparing all POSP observations matched with the AERONET site.\u003c/p\u003e \u003cp\u003eThe LSR of the blue band is estimated through linear fitting of other longer wavelength bands. This involves employing a high-order polynomial to fit LSR from the 670 nm, 865 nm, 1610 nm, and 2250 nm channels. Additionally, the statistical relationship between the scattering angle at the time of observation and the 410/443/490 nm channels is considered. After verifying the different fitting relationships it was found that polynomials can achieve higher accuracy with lower complexity. Finally, the empirical relational equation was constructed using a third-order polynomial and fitted using ridge regression, balancing algorithm efficiency, and accuracy considerations.\u003cdiv id=\"Equb\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e\n$$\\begin{array}{c}{\\rho }_{{\\lambda }_{blue}}={a}_{0}+\\sum _{i=1}^{num}\\left({a}_{3i-3}{\\rho }_{{\\lambda }_{i}}+{a}_{3i-2}{\\rho }_{{\\lambda }_{i}}^{2}+{a}_{3i-1}{\\rho }_{{\\lambda }_{i}}^{3}\\right)+\\sum _{i=1}^{num}\\sum _{j=2}^{num}\\left({b}_{ij}{\\rho }_{{\\lambda }_{i}}{\\rho }_{j}\\right)+\\\\ \\sum _{i=1}^{num}\\sum _{j=2}^{num}\\sum _{k=3}^{num}\\left({c}_{ijk}{\\rho }_{{\\lambda }_{i}}{\\rho }_{{\\lambda }_{j}}{\\rho }_{{\\lambda }_{k}}\\right)+\\dots \\#\\left(2\\right)\\end{array}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWhere num is the number of variables involved in the fit, num is taken as 5, and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({a}_{0}\\)\u003c/span\u003e\u003c/span\u003e is the bias term. \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\rho }_{{\\lambda }_{i}}\\)\u003c/span\u003e\u003c/span\u003edenotes \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\rho }_{670}\\)\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\rho }_{865}\\)\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\rho }_{1610}\\)\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\rho }_{2250}\\)\u003c/span\u003e\u003c/span\u003e, and scattering angle (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\Theta }\\)\u003c/span\u003e\u003c/span\u003e), respectively.\u003c/p\u003e \u003cp\u003eThe construction of the model consists of three main steps: (a) Screening of POSP AOD products for 2022, taking into account the impact of aerosol model on atmospheric corrections at high AOD loadings, with atmospheric corrections only for AOD\u0026thinsp;\u0026lt;\u0026thinsp;0.2, (b) atmospheric correction of all bands of satellite observations to obtain the corresponding LSRs, and (c) calculate the statistical relationships between \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\rho }_{410\\backslash 443\\backslash 490}\\)\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\rho }_{670}\\)\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\rho }_{865}\\)\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\rho }_{1610}\\)\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\rho }_{2250}\\)\u003c/span\u003e\u003c/span\u003e, and scattering angle. This relationship is used in the POSP aerosol algorithm for surface constrain.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.2. Linearization of LUTs\u003c/h2\u003e \u003cp\u003eThe study still uses Shi et al., (\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) improved global aerosol model for retrieval, and a new LUT has been established after considering gas absorption. Pseudo-LSRs obtained from conventional atmospheric corrections using a given AOD value do not satisfy the surface constraints, because it only considers the dependence of the surface contribution of each band. As a result, there is an uncertain error in modeling TOA using this result followed by linear interpolation. To address this issue, we used an optimal LUTs search method that constructs a cost function to search for the AODs that satisfy the surface constraints, which avoids the errors caused by linear interpolation based on the inaccurate pseudo-LSR.\u003c/p\u003e \u003cp\u003eTo use optimization techniques, it is necessary to construct a forward model that has been linearized, in this study by linearizing the LUTs to construct a forward model of the reflectance at TOA. Within the LUTs, the atmospheric parameters are modified only in response to changes in AOD once the observation geometry has been determined. Polynomial fitting procedures are then employed, with the atmospheric parameters serving as dependent variables and AOD as the sole independent variable.\u003cdiv id=\"Equc\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equc\" name=\"EquationSource\"\u003e\n$$\\begin{array}{c}{parm}_{{\\lambda }}={a}_{0}+{a}_{1}*AOD+{a}_{2}*{\\text{A}\\text{O}\\text{D}}^{2}+\\dots \\#\\left(3\\right)\\end{array}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({parm}_{{\\lambda }}\\)\u003c/span\u003e\u003c/span\u003e is the atmospheric parameter at wavelength \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\lambda }\\)\u003c/span\u003e\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eWhile higher-order polynomials could enhance fitting precision, experimentation in this study revealed that the incremental improvement in accuracy with increased polynomial order was outweighed by the computational overhead. Considering the retrieval efficiency, a third-order polynomial is used for the fitting, and a certain condition is chosen as a case study for the accuracy demonstration, the satellite observation of this condition, with the solar zenith angle of 25.95\u0026deg;, the observation zenith angle of 24.5\u0026deg;, the relative azimuth angle of 130.68\u0026deg;, and the AOD of AERNET ground-based observation is 1.1. The difference between the atmospheric correction using the results of the linear fit and the direct correction using LUTs interpolation is also shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, with the RMSE\u0026thinsp;\u0026lt;\u0026thinsp;0.004 and Mean Bias (MB)\u0026thinsp;\u0026lt;\u0026thinsp;0.004 suggesting that the two can be considered approximately equal.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.3. Pixel matching strategy for validations\u003c/h2\u003e \u003cp\u003eAERONET site observations vary temporally but are spatially fixed, satellite observations vary spatially but are temporally fixed. The key to successfully validating satellite data is how to match the data mentioned above. After years of research, various matching strategies have been proposed for different satellite products (Virtanen et al. \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Sayer et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Chu et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Ichoku et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). AERONET sites usually provide observations every 15 minutes (Dubovik et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2002\u003c/span\u003e), and the POSP nadir point has a spatial resolution of 6.4km, considering the composition of atmospheric aerosols changes rapidly over time. We finally choose the matching rule that the mean value of AERONET AOD (at least 2 AOD records) within an hour of GF5\u0026ndash;02 satellite transit is used to compare with the spatial mean value of POSP AOD in a 3 \u0026times; 3 window (at least three AOD values available in the window) centered on the AERONET site. It's important to note that since surface constraints differ across different bands, the quality of output results is governed by a uniform accuracy index, leading to variations in the number of final matching results.\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Results and validation","content":"\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e4.1. Verification of the improved LSR model\u003c/h2\u003e \u003cp\u003eSince the use of historical POSP AOD products introduces unavoidable errors that affect the accuracy of the retrievals, this study retrieves the pixels that overlap with the AERONET site and using high-precision AOD observations at the AERONET site instead of the POSP AOD products to assess the uncertainties introduced by the LSR estimation alone. Following the matching strategy above, we get a benchmark dataset to verify the improved LSR model. Ozone content data are sourced from AERONET site.\u003c/p\u003e \u003cp\u003eWe evaluated the feasibility of the reconstruction model described in Eq.\u0026nbsp;(2). The reconstructed POSP LSRs at 410 nm, 443 nm, and 490 nm were compared with the results accurately corrected for atmospheric effects, as illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e, and \u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e. The estimated LSR errors mostly fell within the EE range, with regressions closely aligned with the 1:1 line. Upon comparing the estimated results of the three blue bands, it was observed that the 490 nm band exhibited the most favorable overall statistics. This outcome could be attributed to the spectral proximity of 490 nm to the other bands involved in the estimation.\u003c/p\u003e \u003cp\u003eFurther analysis of estimation accuracies across the 12 IGBP ground cover classification cases revealed that the estimation accuracy was lowest for barren areas, followed by mixed forests and evergreen needleleaf forests.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e4.2. Validation of AOD retrieval algorithm\u003c/h2\u003e \u003cp\u003eTo assess the feasibility of the new LUTs search method, we employed three blue bands for AOD retrieval. It's important to note that the surface constraints were established using the LSR obtained by atmospherically correcting the POSP observation with the AOD values from the AERONET site. This reduced the error in the LSR estimation caused by the uncertainty in the AOD product. Thus, we can get the accuracy of the AOD retrieval algorithm without considering the accuracy of the auxiliary data.\u003c/p\u003e \u003cp\u003eTemporal and spatial matching of POSP with AERONET was conducted to obtain the verification dataset for testing. Except for the Expected Error (EE) envelope which has been commonly used for MODIS validation (Remer et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2005\u003c/span\u003e), We also adopted stricter requirements proposed by the Global Climate Observing System (GCOS) (the greater of 0.03 or 10%), which have been adopted in the Aerosol_cci study (Popp et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Notably, the POSP AOD (67.62%) demonstrated a high percentage of validation results within the %EE range, and 60.05% validation results within the GCOS range.\u003c/p\u003e \u003cp\u003eThe matches for 410 nm, 443 nm, and 490 nm are 5218, 6632, and 9067, respectively. Most of the retrievals are obtained under low AOD load conditions. The validation results based on the 490 nm observation are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e (c), with R of 0.837 and RMSE of 0.061. The percentage of retrievals falling within the expected error (%EE) \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\((\\pm 0.05 + 0.15{\\text{A}\\text{O}\\text{D}}_{\\text{A}\\text{E}\\text{R}\\text{O}\\text{N}\\text{E}\\text{T}})\\)\u003c/span\u003e\u003c/span\u003e is 84.98%, and within the GCOS is 76.17%, indicating reliable results in the vast majority of cases.\u003c/p\u003e \u003cp\u003eThe POSP's low spatial resolution may result in some pixels having fine cloud amounts that the cloud detection algorithm cannot capture. Consequently, the apparent reflectance of these pixels is generally higher than that of the pixels under cloud-free conditions, leading to an overestimation of the AOD. However, the underestimation of AOD is primarily attributed to the partial bias of the established surface constraints, resulting in a significant discrepancy between the AOD retrievals and the ground-based observations during the optimization iteration process. Figure\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e (a) displays the AOD retrievals of 410 nm observations, with an R of 0.799 and an RMSE of 0.075. Similarly, Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e (b) shows the AOD retrievals of 443 nm observations, with R of 0.788 and RMSE of 0.076.\u003c/p\u003e \u003cp\u003eThe results highlight that the accuracy of the AOD retrievals is largely determined by the accuracy of the surface constraints. To use more blue bands brought about by the increase in the amount of information, this study through the use of a surface constraint model fitting the highest accuracy of the band for retrieval, has verified that the retrieval accuracy can be improved, but the cost of computation is significantly higher, taking into account the time of operation, and ultimately choose to use only 490nm for AOD retrieval.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e4.3. Application for POSP AOD inversion\u003c/h2\u003e \u003cp\u003eThe validation conducted above suggests that the new method is capable of producing high-precision AOD retrievals. Adapting this method for global AOD retrieval, necessitates LSR data meticulously corrected for atmospheric influences across diverse geographical locations. Hence, we opted for existing POSP AOD products for the whole of 2022 for robust atmospheric correction. Earth was partitioned into equal 0.05\u0026deg; intervals, and data from each band with AOD values below 0.2 were chosen for atmospheric correction to establish surface constraints.\u003c/p\u003e \u003cp\u003eAODs for January 2023 were retrieved using observations solely from POSP at 490 nm. Figure\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e illustrates the global monthly mean AOD distribution for January 2023, derived from POSP. The study underscored the need for high-precision historical AOD data to support the proposed method. However, certain areas with persistent high AOD loadings present challenges, leading to substantial uncertainties in atmospheric correction. Consequently, retrievals for such regions were not provided in this study.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe validation results revealed a strong correlation between the POSP AOD and AERONET AOD, with R of 0.703 and RMSE of 0.068. However, it should be noted that the POSP AOD showed significant underestimation at high AOD loads, and by calculating the bias of AOD at different loads, it was found that bias=-0.002 at AOD\u0026thinsp;\u0026lt;\u0026thinsp;0.2, bias=-0.099 at 0.2\u0026thinsp;\u0026lt;\u0026thinsp;=\u0026thinsp;AOD\u0026thinsp;\u0026lt;\u0026thinsp;0.77, and bias=-0.21 at AOD\u0026thinsp;\u0026gt;\u0026thinsp;=\u0026thinsp;0.7. These biases may stem from surface inhomogeneity due to the lower resolution of POSP and the presence of mixed pixels, which tend to overestimate the LSR in the blue band.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTo enhance the scrutiny of the new LSR estimation method's reliability in AOD retrieval, a verification dataset was meticulously curated by extracting POSP and AERONET observations within the timeframe of January 2023. Subsequently, a comparative analysis was conducted between the outcomes derived from employing LSR estimates at each site location in the retrieval process and those obtained after rigorous atmospheric correction.\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e displays the estimated LSRs \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\rho }_{R,\\lambda }^{{\\prime }}\\)\u003c/span\u003e\u003c/span\u003e in comparison to the Atmospheric corrected LSRs \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\rho }_{R,\\lambda }\\)\u003c/span\u003e\u003c/span\u003e across various IGBP surface types. As retrievals were unavailable, the assessment encompassed solely eight distinct surface types. Notably, Open Shrublands exhibited the highest accuracy, whereas Barren demonstrated the poorest accuracy. It is noteworthy that the majority of vegetation cover and urban areas fell within the %EE exceeding 80% of the estimations. This observation strongly implies the commendable accuracy of the new POSP AOD retrieval algorithm, particularly in vegetated regions.\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\u003eLSRs estimation at 490 nm accuracy for different surface types\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" 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=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRMSE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e%EE\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBarren\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e544\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.369\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.092\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e29.04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCroplands\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e596\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.528\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.027\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e76.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDeciduous Broadleaf Forests\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e381\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.637\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.030\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e68.24\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGrasslands\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e260\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.740\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e84.62\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOpen Shrublands\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e270\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.726\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e90.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSavannas\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e347\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.457\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e82.71\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUrban and Built up Lands\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1681\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.495\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e83.22\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWoody Savannas\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e139\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.505\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e80.58\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"},{"header":"5. Conclusion and discussions","content":"\u003cp\u003eThe traditional linear interpolation LUT-based approach usually uses the pseudo-LSR obtained from the atmospheric correction with the preset AOD in the LUT to simulate the reflectance at TOA. However, the pseudo-LSR does not meet the surface constraints established by true LSR alone. Thus, there is an uncertain amount of error when linearly interpolating the simulated reflectance at TOA. This paper also proposes a novel solution to this challenge and establishes a new surface constraint, thereby accommodating surface type variations. The new surface constraints are full-spectrum LSR self-consistent, combining the optimized LUTs search method results in a full-band LSR that satisfies the surface constraints and therefore uses more spectral information in the inversion process, a concept that can be extended to other AOD retrieval algorithms for similar POSP sensors.\u003c/p\u003e \u003cp\u003eAerosol retrieval is achieved by constructing LUTs using the 6SV radiative transfer code, linearizing them during retrieval, and applying the optimal LUTs search method. A comparison of LSRs estimated and LSRs atmospherically corrected revealed errors predominantly below 0.025 across diverse surface types, underscoring the method's high-precision aerosol retrieval capability. Employing satellite observations overlapping with AERONET sites in 2022 as experimental data, surface constraints were established, and AOD retrieval was conducted. Notably, the highest retrieval accuracy was observed at 490 nm, with R\u0026thinsp;=\u0026thinsp;0.837, RMSE\u0026thinsp;=\u0026thinsp;0.061, KAPPA\u0026thinsp;=\u0026thinsp;0.42, and EE%=84.98.\u003c/p\u003e \u003cp\u003eHowever, future research must address several areas for improvement. Firstly, retrieval accuracy for higher AOD cases is heavily influenced by the aerosol model, necessitating consideration of predefined aerosol models with spatial and temporal variations to mitigate errors due to lack of a priori knowledge. Secondly, the assumption of a Lambertian surface overlooks variations in LSR under different observational conditions, warranting consideration of the bi-directional reflectance distribution function (BRDF) to minimize errors in LSR estimation. Thirdly, areas with high AOD loads pose challenges due to uncertainties associated with historical AOD data, thus necessitating refinement of atmospheric correction procedures in such regions. Fourthly, it is noteworthy that under conditions of high AOD loads, POSP AOD demonstrates a notable tendency towards underestimation. This phenomenon may be ascribed to the lower spatial resolution of POSP, which in turn leads to an overestimation of LSR particularly in intricate mixed-pixel scenarios. Lastly, extending surface constraint establishment beyond 2022 and overcoming limitations in observation availability due to stringent screening conditions would enhance the method's applicability and robustness.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors contributed to the study\u0026apos;s conception and design. All authors read and approved the final manuscript. Zhe Ji: Conceptualization, Methodology,\u0026nbsp;data collection,\u0026nbsp;Material preparation, Analysis, and Writing Original Draft. Zhengqiang Li: Supervision, Funding acquisition. Ying Zhang and Zheng Shi: Conceptualization, Supervision, and Writing - Review \u0026amp; Editing. Yisong Xie: Supervision, Writing - Review \u0026amp; Editing. Xiaoxi Yan, Yan Man, and Yang Zheng: Writing - Review \u0026amp; Editing. Zhenting Chen: Funding acquisition.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e The data used for this study is available at https://aeronet.gsfc.nasa.gov/ (AERONET website). We would like to thank all the sites staffs for the data used for this study. \u0026nbsp;We thank the Level 1 data of POSP provided by the China Centre for Resources Satellite Data and Application (https://data.cresda.cn/). ERA5 data were generated using Copernicus Climate Change Service Information (https://cds.climate.copernicus.eu/). Neither the European Commission nor the ECMWF are responsible for any use that may be made of the Copernicus information or data in this publication.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e This work was supported by the National Key R\u0026amp;D Program of China (2022YFC3704000), the National Natural Science Foundation of China (grant nos.42101365, 41925019), the Foreign Technical Cooperation and Scientific Research Program (E3KZ0301) and Li zhengqiang Expert Workstation of Yunnan Province(202205AF150031).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have no competing interests to declare that are relevant to the content of this article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical approval\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe authors confirm that the manuscript has been read and approved by all authors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to participate\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAll the authors have agreed to authorship, read, and approved the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to publish\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAll the authors mentioned in the manuscript approve the version to be published.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003e\u0026Aring;ngstr\u0026ouml;m, A. 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Environmental Pollution 248:526\u0026ndash;535.\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":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"aerosol-science-and-engineering","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"asen","sideBox":"Learn more about [Aerosol Science and Engineering](https://link.springer.com/journal/41810)","snPcode":"41810","submissionUrl":"https://www.editorialmanager.com/asen/default2.aspx","title":"Aerosol Science and Engineering","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Aerosol optical depth, POSP, AEROENT, Land Surface Reflectance, Look-Up Tables","lastPublishedDoi":"10.21203/rs.3.rs-4161991/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4161991/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAccurate estimation of Land Surface Reflectance (LSR) is the key to Aerosol Optical Depth (AOD) retrievals. However, it is noted that the band-specific LSRs retrieved using Look-Up Tables (LUTs) are typically pseudo-LSRs obtained by atmospheric corrections to the AOD predetermined in the LUTs that do not match the surface constraints established by the true LSRs alone. As a result, there is an uncertain error in modeling reflectance at the top of atmosphere (TOA) using pseudo-LSRs calculated by linear interpolation. This study proposed a new LUT search method to improve the AOD retrievals of the Particle Observing Scanning Polarimetry (POSP) sensor onboard the China GaoFen-5 (02) satellite. LSR atmospherically corrected using ERA5 reanalysis data and POSP AOD products for the year 2022 was adopted to create a new full-spectrum LSR self-consistent surface constraint. Results showed that the AOD of POSP in January 2023 retrieved using the new method agrees with the ground-truth AOD values from AErosol RObotic NETwork (AERONET) site observations with the correlation coefficient (R) at 0.703 and the root mean square error (RMSE) at 0.068. 76.77% of the values fell into the expected error (EE) envelope of range \u0026plusmn; (0.05\u0026thinsp;+\u0026thinsp;0.15 AOD\u003csub\u003eAERONET\u003c/sub\u003e), and 67.35% met the accuracy requirements of the Global Climate Observing System (GCOS).\u003c/p\u003e","manuscriptTitle":"Aerosol Optical Depth Retrieval on Particulate Observing Scanning Polarimeter (POSP) Data over Land using a new Look-up table (LUT) Search Method","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-04-02 13:07:02","doi":"10.21203/rs.3.rs-4161991/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"","date":"2024-03-29T02:43:49+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-03-29T01:55:17+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"Aerosol Science and Engineering","date":"2024-03-28T09:27:07+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-03-27T01:17:43+00:00","index":"","fulltext":""},{"type":"submitted","content":"Aerosol Science and Engineering","date":"2024-03-25T05:02:53+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"aerosol-science-and-engineering","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"asen","sideBox":"Learn more about [Aerosol Science and Engineering](https://link.springer.com/journal/41810)","snPcode":"41810","submissionUrl":"https://www.editorialmanager.com/asen/default2.aspx","title":"Aerosol Science and Engineering","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"374008f7-0896-45cc-be09-7922a059bf25","owner":[],"postedDate":"April 2nd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-08-22T19:31:02+00:00","versionOfRecord":{"articleIdentity":"rs-4161991","link":"https://doi.org/10.1007/s41810-024-00236-6","journal":{"identity":"aerosol-science-and-engineering","isVorOnly":false,"title":"Aerosol Science and Engineering"},"publishedOn":"2024-08-12 15:58:08","publishedOnDateReadable":"August 12th, 2024"},"versionCreatedAt":"2024-04-02 13:07:02","video":"","vorDoi":"10.1007/s41810-024-00236-6","vorDoiUrl":"https://doi.org/10.1007/s41810-024-00236-6","workflowStages":[]},"version":"v1","identity":"rs-4161991","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4161991","identity":"rs-4161991","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}