Accurate retrieval of the spatiotemporal distribution of atmospheric aerosols is essential for studying aerosol-radiation-cloud interactions, air-quality forecasting, and climate‑change assessment. Although data-driven methods have significantly advanced aerosol retrieval, existing models often neglect the influence of aerosol type on retrieval accuracy. To address this gap, this study presents an improved data- driven aerosol retrieval framework that explicitly incorporates aerosol type information into model training. Aerosol classification is performed using the K-means unsupervised clustering algorithm to optimize training samples, thereby enhancing model adaptability and retrieval accuracy. The refined samples are then used to train an Extremely Randomized Trees (ERT) model, achieving an optimal balance between accuracy and computational efficiency. Validation results demonstrate strong performance, with a correlation coefficient of 0.93, a root mean square error (RMSE) of 0.072, and over 89 % of results falling within the expected error range [(EE: ± (0.05 + 20 % × in-situ observations)], better than that of the traditional model. The findings demonstrate that integrating aerosol- type information into data- driven retrievals substantially improves accuracy and applicability for aerosol remote sensing. Future research should focus on refining aerosol classification techniques and integrating multi-source remote sensing data to enhance model robustness and global applicability further.

Although multispectral satellite remote sensing (RS) is able to derive relatedly accurate aerosols on a large geographical scale, which is crucial for the study of aerosol-related climate and environment changes, some issues remain in current retrieval algorithms. For example, both radiance transfer and machine learning algorithms fail to consider the aerosol types because it is challenging to use only multispectral information to quantify aerosol sources accurately. This may cause a large uncertainty in RS aerosol retrieval, especially in areas with complex and varying aerosol components. Moreover, there are multiple parameters that can reflect aerosol’s physical and chemical properties, but most developed algorithms only accurately obtain one once, such as aerosol optical depth (AOD), which may lead to a large inconsistency when applying them from different algorithms. To address these issues, we propose a multiparameter aerosol simultaneous retrieval algorithm by combining multispectral satellite and atmospheric simulation using a space-time transformer (STTF) model. Sample-based ten-fold validation suggests that our STTF model can effectively retrieve multiple aerosol parameters in terms of 550-nm AOD with R (root mean square error, RMSE) of 0.89 (0.10), Ångström exponent (AE) with R (RMSE) of 0.89 (0.26), and single scattering albedo (SSA) with R (RMSE) of 0.89 (0.10). The time- and spatial-based validation further underscores the model’s ability to predict different aerosol parameters over areas or periods without ground-based measurements. Moreover, the model also shows better AOD retrievals than the operational aerosol products (i.e., MCD19A2 and MOD04_3K) over the word and has significant improvement in areas with high aerosol loadings.

Cloud detection methods for Landsat satellites have been developed to characterize surface backgrounds and various cloud characteristics, employing different processing techniques to improve the accuracy of cloud detection. While these algorithms have their own strengths and limitations, it is crucial to improve their accuracy and stability through algorithm fusion. To address the issue of low detection accuracy for highly reflective surfaces and thin broken clouds, this study examines the impact of complex surfaces and seasonal variations, and proposes a cloud detection fusion algorithm that combines multiple algorithms. First, the accuracy feedback indicators for each algorithm were calculated by combining GlobeLand30 and Landsat 8 Cloud Cover Assessment Validation Data. A foundational fusion algorithm model was constructed based on the accuracy feedback indicators. Subsequently, considering the influence of complex surfaces and seasonal variations, the normalized vegetation index was introduced into the basic fusion algorithm. The algorithm weights were dynamically updated in real-time using logistic regression. The detection accuracy of the fusion algorithm was evaluated using Landsat 8 Biome data and Landsat 9 images. The fusion algorithm exhibited a high consistency of 0.95 for real cloud coverage. Its F1 score was 0.96 for water and 0.92 for barren surfaces. The results demonstrate that the fusion algorithm can identify thin and broken clouds above both dark and bright surfaces with high accuracy. The fusion algorithm outperformed other similar algorithms, as it could overcome the influence of complex surfaces and seasonal variations.

Ambient fine particulate matter (PM2.5) has significant adverse effects on human health, thereby urgent hunger for accurate monitoring of ground-level PM2.5, especially its space distribution. Since satellites can observe the Earth on a large spatial scale, remote sensing technology can be applied to estimate PM2.5concentrations at the national level. Based on it and machine learning (ML) methods, numerous studies mapped high-accuracy, wholesale and continuous PM2.5. However, different models and data in these studies made their results incomparable, and more samples were needed to be provided. Here, a large-column and long-term sample dataset (ML-CNPM2.5) applied for ML-based models was constructed with 5,076,608 data records and 24 features from 2014 to 2023 in China. Multiple approaches were used to guarantee the quantity and quality of the sample dataset. Due to its comprehensiveness and objectivity, the ML-CNPM2.5can be used to train and validate different models, thereby further improving the accuracy of PM2.5estimating. Using the ML-CNPM2.5, eight basic ML-based models were also constructed as the baseline for judging other derivative models. These models can estimate daily full-coverage PM2.5and most performed well, with 10-fold cross-validation RMSE of 16.94-11.21μg/m3and R2of 0.71-0.89, which is consistent with previous studies and can effectively capture spatial trends of PM2.5in a period suffered from high pollution. Overall, our ML-CNPM2.5can be applied to effectively construct, validate, and compare various ML-based models for PM2.5estimation, helping to develop new algorithms with higher accuracy and robustness.

Atmospheric aerosols originate from diverse sources and exert a notable influence on the radiation budget, atmospheric environment, and human health. However, current aerosol inversion models still have limitations in dealing with multiple types of variables and intricate scenarios, for which a two-stage hybrid model named convolutional neural network-random forest (CNNRF) is proposed in this study. Convolution is employed to extract continuous spectral signals. This study takes into account the synergistic impact of spatiotemporal, meteorological, and surface information. Ensemble learning is then applied to adeptly handle diverse-independent input variables. In this article, eight regions were selected globally for modeling and testing based on different scenario types. Additionally, an aerosol hotspot region (India) was chosen for independent experiments. Accuracy was validated at the site scale using a 10-fold cross-validation (10-CV) approach and cross-comparison with the MCD19A2 product, and convolutional neural network (CNN) and random forest (RF) model results. The sample-based CV of the CNNRF model demonstrates high and consistent accuracy, with a Pearson correlation coefficient ($R$) value of 0.958, mean absolute error (MAE) of 0.048, and a within expected error (EE) envelope of 87.15%. For the Indian region, the MAE and EE are 0.05 and 95.8%, respectively. In summary, the proposed hybrid model demonstrates robust generalization capabilities, enabling accurate and stable aerosols estimation on a global scale.

Thin clouds detection and the difficulty in distinguishing between clouds and bright surface features have consistently presented challenges in optical remote sensing cloud detection tasks. Convolutional neural networks (CNNs) have made significant progress, however, CNNs perform weakly in capturing global information interactions due to the inherent limitation of network structure. To address these issues, we propose a hybrid CNN-Transformer network with differential feature enhancement (DFE) for cloud detection (CNN-TransNet). CNN-TransNet adopts a dual-branch encoder consisting of CNN-Transformer module and DFE module. CNN-TransNet combines the strengths of both Transformer and CNN to enhance finer details and build long-range dependencies. CNN is considered as a high-resolution feature extractor for capturing low-level features. The transformer module encodes image sequences by patch embedding to extract high-level features and relationships. DFE branch utilizes differential features and attention mechanism to further obtain effective information for distinguishing between clouds and non-clouds. The decoder upsamples features of the encoder and concatenates multiscale features from the CNN layers. Experimental results demonstrate that the proposed method achieves excellent performance on Landsat-8 and Sentinel-2 images, with a high cloud pixel precision of 92.94% and 93.04%. Moreover, it effectively reduces thin cloud omissions and the misclassifications of bright surface features.

Land surface temperature (LST) is a vital parameter for studying global ecological, climatic, and environmental changes. Although various LST retrieval algorithms have been proposed, including split-window (SW), dual-window (DW), three-channel (TC), and dual-angle (DA) algorithms, few studies have compared these algorithms using the same satellite observations. The Sea and Land Surface Temperature Radiometer (SLSTR) onboard Sentinel-3A provides a unique opportunity to conduct this comparison owing to its dual-angle viewing capability and multiple thermal infrared (TIR) and mid-infrared (MIR) channels. Here, we implemented two SW algorithms, one DW algorithm, two TC algorithms and one DA algorithm for the SLSTR data. The LST retrievals from these six algorithms were validated, along with the SLSTR operational LST product based on an emissivity-implicit SW algorithm. Temperature-based and radiance-based validation methods were used to evaluate different LST retrievals across different land cover types. The results indicated that the proposed SW algorithm had the highest accuracy, followed by the Pérez-Planells SW and the official algorithms. The overall root-mean-square errors (RMSEs) of these three SW algorithms were 1.42 K, 1.79 K and 2.05 K, respectively. The three algorithms involving the MIR channel (one DW and two TC algorithms) were more suitable for nighttime LST retrieval and had similar performances to the three SW algorithms, with a nighttime RMSE of approximately 1.36 K. The LST retrieval accuracy of the DA algorithm had the highest uncertainty and was closely related to the angular variation in surface emissivity and brightness temperature. The findings of this study contribute to a better understanding of the different LST retrieval algorithms and facilitate potential improvements in the official LST retrieval algorithm for SLSTR.

Land surface temperature (LST) is an important parameter that characterizes the energy balance of the land surface, and it is widely used in various research fields. This paper proposes an operational split-window (SW) algorithm for use with the Chinese Fengyun-3 (FY-3) series satellite data, with the purpose of generating long-term global LST products. The algorithm primarily involves three steps. First, the brightness temperatures of the FY-3 Visible and Infra-Red Radiometer (VIRR) were recalibrated using historical recalibration coefficients to improve the accuracy of the absolute radiometric calibration. Second, daily dynamic emissivity maps were estimated using the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) global emissivity dataset (GED) and vegetation/snow cover products based on the vegetation cover method. Finally, the coefficients of the SW algorithm were simulated using MODTRAN 5 combined with the SeeBor V5.0 atmospheric profile library and ASTER spectral library, and then the coefficients were stratified by the view zenith angle and atmospheric water vapor content to improve the fitting accuracy. The proposed SW algorithm was integrated into the MUlti-source data SYnergized Quantitative (MUSYQ) remote sensing production system to then generate FY-3 VIRR LST products. Ten land surface sites from the HiWATER and SURFRAD networks and nine water surface sites from the National Data Buoy Center (NDBC) were used to evaluate the accuracy of the FY-3 VIRR LST products. The results demonstrated that the accuracy of the historical recalibration coefficients of the FY-3A/B VIRR is higher than that of the operational calibration coefficients for LST retrieval. The evaluation results revealed that the FY-3A VIRR LST products (2009-2013) had a bias of 0.13 K and an RMSE of 2.77 K, and the FY-3B VIRR LST products (2011-2020) had a bias of -0.07 K and an RMSE of 2.83 K. These results demonstrate that the proposed operational SW algorithm has reasonable accuracy and can be used to produce global LST products from the FY-3 VIRR data.

Cloud and cloud shadow detection is a crucial procedure in remote sensing image processing. While the threshold method is a common method for its easy implementation, it compromises on accuracy for complex surface conditions. To address such limitations, a cloud detection algorithm based on land type has been proposed. As an extension of this, a novel cloud shadow detection algorithm based on an a Priori Land type and Reflectance Database Support (denoted as PLRDS) is proposed. This algorithm uses GlobeLand30 data and Landsat-8 images as support information for land cover and reflectance data, respectively. By utilizing this support information, optimal bands and thresholds are determined for cloud shadow detection in each land type. The threshold determination is discussed in terms of absolute and grey areas. To evaluate the PLRDS algorithm, experiments were conducted on Landsat-8 data for each land type and on complex surfaces. The results show good consistency compared to false-color images. In addition, a quantitative validation was conducted with the producer’s accuracy of cloud shadow and clear land (PAS and PAC), user’s accuracy of cloud shadow and clear land (UAS and UAC), and overall accuracy (OA). The OA of the proposed algorithm is 0.9719. Among all the land types, bareland and artificial surfaces achieved the lowest and highest OA, which reached 0.9867 and 0.9483, respectively. These results indicate the accuracy and good performance of the PLRDS cloud shadow detection algorithm.

High-resolution land surface temperature (LST) retrieval is a hot research topic in recent ten years, and the development of various high-resolution satellite sensors provides a data basis for this study. The Visual and Infrared Multispectral Imager (VIMI) on Gaofen5-02 (GF5-02) satellite provides 40m spatial resolution thermal infrared data ranging from 8µm to 12.5µm, including four thermal infrared channels. In this paper, we developed a split-window algorithm for retrieving LST from VIMI data. First, the two thermal infrared channels 11 and 12 of VIMI are cross-calibrated using MODIS bands 31 and 32, and then high-resolution LST was derived using the generalized split-window algorithm. The GF5-02 LST was cross-validated with the MODIS MOD21 LST products, the preliminary results indicate that GF5-02 LST shows a reasonable accuracy, with a mean bias of 0.05 K and a mean RMSE of 3.29 K.

High-precision cloud detection for remote sensing image is of great significance for applications in agriculture, environment, meteorology and other fields. Cloud detection in bright surface environments and thin clouds identification have always been a challenge in cloud detection research. Aiming at this problem, a cloud detection method for remote sensing images based on difference features and semantic segmentation network is proposed in this paper. Cloudy and cloudless images of the same area are used to obtain difference features, and cloudless images are used as the surface reference. The multi-band cloud detection network based on U-network (U-net) fully learns the difference feature between clouds and the surface, and combines the feature information of the shallow and deep layers to accurately identify the cloud and the surface. Experiments were conducted on the Landsat 8 validation dataset. The results show that the proposed method achieves good performance. It improves the detection accuracy and reduces the misclassification over the bright surface underlying compared with the method based on top of atmosphere reflectance (TOA).

Aerosol satellite remote sensing retrieval is of great importance in the study of climatic and environmental effects. However, due to varied factors affecting the signals reaching satellite sensors, accurate aerosol retrieval remains challenging. Focusing on limitations in the availability of aerosol data from real scenes, we simulated real scenes to obtain sample data to achieve aerosol retrieval using a deep belief network (DBN). The 6S atmospheric radiative transfer model was used to simulate various possible parameters of earth–atmosphere, sun, and sensor in real scenes. A large amount of simulated data was generated and used as sample datasets of DBN training to obtain the aerosol inversion model. Moderate Resolution Imaging Spectroradiometer (MODIS) data were used to perform aerosol optical thickness (AOT) retrieval experiments. Global-scale aerosol retrieval experiments were conducted based on the following three representative regions: 1) Beijing–Tianjin–Hebei region in China; 2) Midwestern and Southern United States; and 3) Central and Western Europe. The retrieval results were verified using Aerosol Robotic Network (AERONET) datasets in comparison with MCD19A2 aerosol products. Five indicators, including mean absolute error (MAE) and within expected error (${f} _{=\text {EE}}$), were used for the evaluation. The evaluation indicators of the proposed method, in which MAEs were 0.0626, 0.0366, and 0.0487, and${f} _{=\text {EE}}$’s were 86.28%, 85.21%, and 80.71%, performed better than MCD19A2 in three typical regions. The most significant advantage of the proposed method is that high-precision retrieval of spatially continuous AOT can be achieved using single-temporal satellite imagery data, which is not possible realizing in current aerosol retrieval methods.

As the successors of the HuanjingJianzai-1 (HJ-1) series satellites in the Chinese Environmental Protection and Disaster Monitoring Satellite Constellation, the first two of HJ-2 A/B satellites have been successfully launched on the September 27 of 2020. The Polarized Scanning Atmospheric Corrector (PSAC) sensors, onboard the HJ-2 A/B satellites, are served as the synchronously atmospheric correction instrument requiring high speed and accurate aerosol optical depth (AOD) algorithm. For this purpose, we proposed a neural network based AOD retrieval model (named the AODNet), which takes full advantage of the multispectral measurements of PSAC for AOD retrieval with a high speed. The training of AODNet is conducted by the simulated observation data (currently applicable for the China region) from the forward calculation using the radiative transfer model. In this way, the land surface reflectance (LSR) is no need for our well trained model. It is expected to be one of the effective ways to solve the ill-pose problem in the decoupling of the atmosphere and surface information in AOD retrieval. Either of Sun-sky radiometer Observation NETwork (SONET) AOD or AErosol RObotic NETwork (AERONET) AOD was used to validate the AODNet AOD. The correlation coefficient is higher than 0.85 and more than 60% of the AODNet AOD can fall into the expected error envelope of ±(0.05+20%). The cross-comparison shows that the AODNet has better accuracy than MODIS Dark Target (DT) and Deep Blue (DB) algorithm. The air pollution episode is well characterized by the AODNet AOD using PSAC data.

PM2.5is hazardous to human health, and high-quality data are thus needed on a routine basis. An attempt is made here to improve the accuracy of near-surface PM2.5estimates using the newly released aerosol product derived from the Visible Infrared Imaging Radiometer Suite (VIIRS) satellite with the Deep Blue retrieval algorithm. A high-quality PM2.5data set is generated at a spatial resolution of 6 km from 2013 to 2018 by applying the space-time extremely randomized trees (STET) model, which also aims to extend the Earth Observing System (EOS) long-term PM2.5data records in China. The PM2.5estimates are highly consistent with ground-based measurements, with an out-of-sample cross-validation coefficient of determination (CV-R2) of 0.88, a root-mean-square error (RMSE) of$16.52~\mu \text{g}/\text{m}^{3}$, and a mean absolute error of$10~\mu \text{g}/\text{m}^{3}$at the national scale. Spatiotemporal PM2.5variations at monthly scales are also well captured (e.g.,$R^{2} =0.91$–0.94, RMSE = 5.8–$11.6~\mu \text{g}/\text{m}^{3})$. PM2.5varied greatly at regional and seasonal scales across China. Benefiting from emission reduction and air pollution controls, PM2.5pollution has reduced dramatically in China with an average of$- 5.6~\mu \text{g}/\text{m}^{3}$/yr−1during 2013–2018. Significant regional reductions are also seen, in particular, in the Beijing–Tianjin–Hebei region ($- 6.6~\mu \text{g}/\text{m}^{3}$/yr−1,$p < 0.001$), and the Deltas of Yangtze River ($- 6.3~\mu \text{g}/\text{m}^{3}$/yr−1,$p < 0.001$) and Pearl River Delta ($- 4.5~\mu \text{g}/\text{m}^{3}$/yr−1,$p < 0.001$). Our study improved the accuracy of near-surface PM2.5estimates in terms of their spatiotemporal variations at a relatively long-term record, which is important for future air pollution and health studies in China.

The collaborative use of Landsat and Sentinel-2 could substantially improve the temporal observation frequency at the medium spatial resolution, which was very important for the studies demanding dense temporal observations. The purpose of this study was to develop the global cross-sensor transformation functions for the well-established Landsat-8 and Sentinel-2 top of atmosphere (TOA) and surface reflectance (SR) products integrated within Google Earth Engine (GEE). Comparison results indicated the significant radiometric differences between Landsat-8 and Sentinel-2, resulting in band-wise root mean square error (RMSE) ranging from 0.0091 to 0.0357 and 0.0168 to 0.0348 for TOA and SR, respectively, and the linear relationships were developed accordingly. Furthermore, via using 12 validation sites across the globe, this study confirmed that the proposed correction models could substantially IMPROVE the time-series agreement between Landsat-8 and Sentinel-2, resulting in a 0.63%–27.83% and 7.16%–21.36% reduction in RMSE for TOA and SR, respectively. The findings of this study were highly useful for the collaborative utilization of Landsat-8 and Sentinel-2 data within GEE for the applications requiring high temporal observation frequency at medium spatial resolution.

In this study, a split-window(SW) algorithm is tested to retrieve the land surface temperature (LST) from nighttime mid-infrared MODIS data. At first, the SW algorithm's coefficients were derived using MODTRAN simulations with the TIGR atmospheric profile database. Then, the input emissivities of the SW algorithm were directly calculated using the MODIS MYD11B1 products. At last, the LST was retrieved using the Aqua MODIS bands 22 and 23 data. The retrieved LSTs were compared with the MODIS MYD11A1 products and validated using ground measurements collected from four ground sites in northwest China. The validation results indicate that the developed SW algorithm provides better accuracy than the MYD11A1 LST products, with a mean bias of −0.76K and a mean root-mean-square error (RMSE) of 1.3K. Experiments show that the split-window algorithm is also applicable in the mid-infrared channel at night, and can produce accurate land surface temperature results.

GF-6 is a low-orbit optical remote sensing satellite and it's the first high-resolution satellite for precision agricultural observations in China. The quantitative application and analysis of remote sensing images rely on the support of accurate surface reflectance data. Therefore, a fast and accurate atmospheric correction process is of great significance for the high-precision inversion of remote sensing parameters. In this paper, MOD09A1 is used to provide support for surface reflectance data to obtain aerosol parameters, and then achieve atmospheric correction of GF-6/WFV based on the 6S model. We selected China Beijing as the research area and obtained remote sensing data and AERONET ground measurement data, then carried out atmospheric correction experiments and accuracy verification. The verification results show that the aerosol inversion results and the measured data have small errors (MAE=0.029, RMSE=0.047) and high correlation (R=0.875); the image contrast is improved after atmospheric correction, and the surface reflection spectrum information is better restored.

In this study, two collection 6 (C6) Moderate Resolution Imaging Spectroradiometer (MODIS) level-2 land surface temperature (LST) products (MYD11_L2 and MYD21_L2) from the Aqua satellite were evaluated using temperature-based (T-based) and radiance-based (R-based) validation methods over barren surfaces in Northwestern China. The ground measurements collected at four barren surface sites from June 2012 to September 2018 during the Heihe Watershed Allied Telemetry Experimental Research (HiWATER) experiment were used to perform the T-based evaluation. Ten sand dune sites were selected in six large deserts in Northwestern China to carry out an R-based validation from 2012 to 2018. The T-based validation results indicate that the C6 MYD21 LST product has a better accuracy than the C6 MYD11 product during both daytime and nighttime. The LST is underestimated by the C6 MYD11 products at the four T-based sites during the daytime, with a mean bias of -2.82 K and a mean RMSE of 3.82 K, whereas the MYD21 LST product has a mean bias and RMSE of -0.51 and 2.53 K, respectively. The LST is also underestimated at night by the C6 MYD11 products at the four T-based sites, with a mean bias of -1.40 K and a mean RMSE of 1.72 K, whereas the MYD21 LST product has a mean bias and RMSE of 0.23 and 1.01 K, respectively. For the R-based validation, the MYD11 results are associated with large negative biases during both daytime and nighttime at three sand dune sites and biases within 1 K at the other seven sites, whereas the MYD21 results are more consistent at all ten sand dune sites, with a mean bias of 0.45 and 0.70 K for daytime and nighttime, respectively. The emissivities for these two products in MODIS bands 31 and 32 were compared with each other and then compared with the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) emissivity and laboratory emissivity. The results indicate that the emissivities in MODIS bands 31 and 32 of MYD11 at the four T-based and three of the R-based validation sites are overestimated and result in LST underestimation, whereas the emissivities of MYD21 are more consistent with the laboratory emissivity. Besides, an experiment was carried out to demonstrate that the physically retrieved dynamic emissivity of the MYD21 product can be utilized to improve the accuracy of the split-window (SW) algorithm for barren surfaces, making it a valuable data source for retrieving LST from different remote sensing data.

To solve the ill-conditioned problem of the traditional aerosol inversion methods, based on radiative transfer equations, an aerosol inversion method using deep learning for Landsat 8 OLI data was proposed in this article. The aerosol inversion model was generated by constructing a sample data set and training a deep learning network. Then the model was used to conduct aerosol inversion experiments in some typical areas worldwide, and AERONET data was selected to verify the accuracy of the inversions. Experiments and verification show that this method can achieve continuous and stable aerosol distribution for different underlying surface types, and the aerosol inversions are highly accurate and reliable. The method is of great significance in quantitative aerosol remote sensing and atmospheric pollution monitoring.

The deep blue (DB) algorithm is usually used for retrieval of AOD on high reflectivity surfaces, which cover complex surface types. However, due to low spatial resolutions, it shows limited information details in urban areas and cannot fully display the aerosol variation characteristics. Now, a new aerosol retrieval algorithm with a priori Land Surface Reflectance (LSR) database support (HARLS) is proposed for Landsat-8 (OLI) images at 1km spatial resolution to use the look-up table (LUT) method to retrieve AOD. The continental aerosol type defined in the 6S model is modified by AERONET single scattering albedo (SSA) parameter to correct the LUT for different seasons, and deep learning neural network is used to train the samples based on the corrected LUT and retrieve AOD. The retrieved AOD are then validated against the aerosol robotic network (AERONET) AOD ground measurements from five stations in Beijing and compared with MOD04 C6.1 10km AOD product. The overall results demonstrate that the AOD retrieved is highly consistent with the AOD derived from ground measurements.

In this paper, in order to produce long term fully global land surface temperature (LST) product, the generalized split-window (GSW) algorithm and dual-window (DW) algorithm was used to retrieve LST from different geostationary (GEO) satellite data, including the FY-2E/4A, MTSAT-2/Himawari-8, MSG2, and GOES13/15. First, the coefficients of the GSW and DW algorithm were obtained from a simulation database constructed using the MODTRAN 5.2 and the SeeBor V5.0 atmospheric profile database. Second, the emissivity was estimated using the vegetation cover method, with the bare soil component emissivity derived from the ASTER global emissivity dataset (GED). Finally, the LST results of FY-4A AGRI and Himawari-8 AHI were retrieved and cross-validated. The results show that the LST algorithms developed in this work are capable of generating accurate high temporal resolution LST retrieval from global GEO satellite data.

Mineral mapping with hyperspectral images has been demonstrated as an effective way for land resources survey. K-means, as a typical clustering algorithm, is commonly used to process the object identification of hyperspectral images. However, due to the influence of mixed pixel, the matching of data points and cluster centers of the traditional k-means clustering algorithm is very difficult. Therefore, this paper proposes an improved k-means clustering algorithm to identify the mineral types from the AVIRIS hyperspectral image of Cuprite mining area. This algorithm uses three methods to select the initial cluster centers and spectral information divergence instead of Euclidean distance for better measuring the similarity. Finally, by matching the clustering results with the mineral distribution map of this region and USGS mineral spectral library, it was found that the improved k-means clustering algorithm can get better clustering results and higher mineral mapping accuracy than the traditional algorithm.

Aerosol remote sensing monitoring is facing great challenges in complex background areas such as urban and industrial and mining enterprises, for the high surface reflectivity and the obvious characteristics of bidirectional reflection. This paper provides a MODIS aerosol inversion method for complex background conditions that is supported by BRDF/Albedo products. Using these products, the accuracy of surface reflectivity and the resulting inversion results of aerosols are improved. Three typical complex background areas (Beijing, Baltimore and Paris) were selected for application testing. Aerosol inversion was calculated using MODIS BRDF/Albedo data for six years from 2012 to 2017. Accuracy was verified using the AERONET ground-based observations in the corresponding areas, and results were compared with the existing MODIS aerosol product (MOD04 AOD). The inversion results of the new algorithm have higher precision and stability.

In this study, to improve the accuracy of land surface temperature (LST) products over barren surfaces, we present an operational algorithm to retrieve the LST from Moderate-Resolution Imaging Spectroradiometer (MODIS) thermal infrared data using physically retrieved emissivity products. The LST algorithm involved two steps. First, the emissivity in the two MODIS split-window (SW) channels was estimated using the vegetation cover method, with the bare soil component emissivity derived from the ASTER global emissivity data set. Then, the LST was retrieved using a modified generalized SW algorithm. This algorithm was implemented in the MUlti-source data SYnergized Quantitative (MuSyQ) remote sensing product system. The MuSyQ MODIS LST product and the Collection 6 MODIS LST product (MxD11_L2) were compared and validated using ground measurements collected from four barren surface sites in Northwest China during the Heihe Watershed Allied Telemetry Experimental Research (HiWATER) experiment from June 2012 to December 2015. In total, 2268 and 2715 clear-sky samples were used in the validation for Terra and Aqua, respectively. The evaluation results indicate that the MuSyQ LST products provide better accuracy than the C6 MxD11 product during both daytime and nighttime at all four sites. For the daytime results, the LST is underestimated by the C6 MxD11 products at all four sites, with a mean bias of -1.78 and -2.86 K and a mean root-mean-square error (RMSE) of 3.16 and 3.94 K for Terra and Aqua, respectively, whereas the mean biases of the MuSyQ LST products are within 1 K, with a mean bias of -0.26 and -1.03 K and a mean RMSE of 2.45 and 2.71 K for Terra and Aqua, respectively. For the nighttime results, the LST is also underestimated by the C6 MxD11 products at all four sites, with a mean bias of -1.60 and -1.26 K and a mean RMSE of 1.93 and 1.60 K for Terra and Aqua, respectively, whereas the mean biases of the MuSyQ LST products are 0.16 and 0.58 K and the mean RMSEs are 1.12 and 1.25 K for Terra and Aqua, respectively. The results indicate that the underestimation of the C6 MxD11 LST product at all four sites mainly results from the overestimation of the emissivities in MODIS bands 31 and 32. This study demonstrates that physically retrieved emissivity products are a useful source for LST retrieval over barren surfaces and can be used to improve the accuracy of global LST products.

Moderate resolution imaging spectroradiometer (MODIS) has been widely used in related aerosol studies because of its long data records. However, operational aerosol optical depth (AOD) products at coarse spatial resolutions limit their applications on small and medium scales. Thus, high-spatial-resolution AOD products are needed. In this paper, a regionally robust high-resolution aerosol retrieval algorithm is developed for MODIS images over Eastern China which has complex surfaces and severe air pollution. Several major challenges in aerosol retrieval are resolved including: 1) surface reflectance by correcting for the effects of surface bidirectional reflectance distribution function using the RossThick-LiSparse model; 2) aerosol models assumed by time-series data analysis with historical aerosol optical properties measurements from the Aerosol Robotic Network (AERONET) sites; and 3) cloud screening using the proposed universal dynamic threshold cloud detection algorithm. Moreover, gas (i.e., ozone and water vapor) absorption is also corrected. Finally, our AOD retrievals are compared with the newest AERONET Version 3 Level 2.0 AOD ground-based measurements, latest MODIS Collection 6.1 AOD products at 3- and 10-km resolutions, and multiangle implementation of the atmospheric correction (MAIAC) AOD product at a 1-km resolution. The results suggest that our algorithm performs well over dark vegetated and bright urban surfaces and that 78.56% of the retrievals meet the acceptable expected error of ±(0.05% + 20%) with a mean absolute error and a root-mean-square error of 0.074 and 0.125, respectively. Comparison results indicate that the newly generated 1-km AOD data set is much better than the routine MOD04 3- and 10-km dark target data sets, and slightly better than the 10-km deep blue (with lower resolution) and 1-km MAIAC (with narrower space coverage) AOD products. This attests to the robustness of our algorithm that generates an AOD product with a more continuous coverage and finer resolution over complex surfaces.

The Visible Infrared Imaging Radiometer Suite (VIIRS) on board the Suomi National Polar-orbiting Partnership (NPP) is a new-generation polar-orbiting satellite imaging sensor. It has generated a variety of operational products similar to the widely used Moderate Resolution Imaging Spectroradiometer (MODIS) products. However, there are high uncertainties in official VIIRS aerosol products based on our previous validations, and a reduction in these uncertainties is needed before they can be used with confidence. To this end, we developed a revised high-spatial-resolution aerosol retrieval algorithm which can considerably improve the aerosol optical depth (AOD) estimations. The improvements mainly arise from: 1) correction of the surface bidirectional reflectance using the RossThick-LiSparse model with parameters obtained from the MODIS bidirectional reflectance distribution function (BRDF)/Albedo products; 2) finer customized monthly aerosol types assumed from the historical Aerosol Robotic Network (AERONET) measurements of optical properties; and 3) improved cloud screening with the revised dynamic threshold cloud detection algorithm. The new 750-m AOD retrievals are validated against AERONET AOD measurements and compared with the official VIIRS AOD products from 2014 to 2017 over the Beijing-Tianjin-Hebei region in China. The results illustrated that the retrievals are highly consistent with ground measurements ($R = 0.926$ ), with ~72% of them falling within the expected error of [±(0.05 + 20%)] on a regional scale. The mean absolute error is 0.082 and the root-mean-square error is 0.120. The new algorithm can significantly reduce the overestimations and improve the aerosol estimations over heterogeneous urban surfaces compared to the official aerosol products, especially in winter. This new VIIRS AOD product will thus be more useful for air pollution studies over medium- or small-scale areas.

In this paper, we proposed a temperature and emissivity separation (TES) algorithm for the simultaneous retrieval of land surface temperature and emissivity (LST&E) from the thermal infrared data of Chinese GaoFen-5 (GF-5) satellite's Multiple Spectral-Imager (MSI) payload. In order to improve the accuracy of the TES algorithm, a water vapor scaling (WVS) method for atmospheric correction was adopted. The Seebor V5.0 global atmospheric profile database and MODTRAN 5 were used to simulate the WVS coefficients. A total of 11 ASTER scenes were used to simulate the MSI images and concurrent ground measurements acquired in the HiW ATER experiment were used to validate the algorithm. The results showed that the bias and root mean square error (RMSE) in the retrieved LST were 0.47 K and 1.70 K, respectively, and the absolute emissivity differences between MSI and the ground measurements were smaller than 0.01 for the four MSI TIR bands, which demonstrated that the proposed algorithm can be used to retrieve high accurate and high spatial resolution LST&E from GF-5 MSI data.

Prior knowledge of the background land surface reflectance (LSR) constitutes one of the most important factors affecting the precision of cloud shadow detection. To resolve this problem, a surface reflectance-based cloud shadow detection (SRCSD) algorithm is proposed for multitemporal Landsat images. Monthly surface reflectance data sets constructed from MODIS surface reflectance products (MOD09A1) were used to provide the background LSR for cloud shadow detection. Based on the background LSR, the possible variation in the top of atmosphere (TOA) reflectance for each clear pixel can be estimated using the radiative transfer equation under different atmospheric conditions. If a pixel has a smaller TOA reflectance than the minimum value of the possible range under clear conditions, it is identified as being shadow covered. One hundred and twenty-five Landsat 8 Operational Land Imager scenes covered by various surface types were selected to evaluate the feasibility of the algorithm. A validation using manual cloud shadow masks showed that the average producer’s accuracy and user’s accuracy were approximately 0.805 and 0.893, respectively. A comparison of the results of the SRCSD algorithm with those of an object-based cloud shadow detection algorithm (Fmask) recently developed for Landsat images revealed that SRCSD generally detects cloud shadows better than Fmask. The most significant improvement of the SRCSD algorithm is the better detection capability for thin and broken cloud shadows, and this algorithm can be extended to multiple types of satellite data after proper modification.

UDTCDA (universal dynamic threshold cloud detection algorithm) is a new cloud detection method which was proposed recently. This cloud detection method is supported by priori surface reflectance obtained from MODIS surface reflectance product (MOD09). Reflectance of four bands in the wavelength of visible to near infrared are used to detect the cloudy pixels. Because there is a priori reference data on the ground surface, pixels of thin cloud and the fractional cloud can be well detected from the clear pixels. MOD35 is the MODIS cloud mask product, combined use of reflectance and brightness temperature to determine the cloud pixels at 250m, and 1km resolutions. 22 out of 36 bands in the visible, near-infrared, and thermal infrared bands are used to create a high quality cloud mask. The methods of visual interpretation and comparison with the CALIPSO data are used to evaluate the two cloud detection algorithms.

A dust storm detection algorithm for NPP VIIRS data is proposed in this paper. The pixel dataset includes a variety of typical feature types, such as dust over different surface type, thick and thin clouds, vegetation, Gobi, ice/snow, etc. were collected and the distribution of the reflectance and brightness temperature were analyzed, based on which, a dust detection algorithm was generated. Multi-temporal NPP VIIRS images with dust storm happened were collected and applied to the experiments of dust storm detection with the proposed method. OMI AI products which can well describe the distribution of dust storm were selected for validation, and the results shows that this algorithm can detect the dust storm from NPP VIIRS over different land types in high precision.

Land surface temperature (LST) is a key parameter for a wide number of applications, which include hydrology, meteorology and model validation. In this paper a physical single channel algorithm was developed for retrieving LST from the Landsat 8 TIRS data. ASTER Global Emissivity Dataset (GED) and Vegetation Cover Method (VCM) were chosen to improve the accuracy of land surface emissivity and the fast radiative transfer model RTTOV was utilized for atmospheric correction which uses MERRA reanalysis data as inputs. The algorithm is evaluated by the ground measurements collected from in situ sites during the HiWATER experiment. The LST result shows a dynamical variation with the phenological changes and the average Bias and RMSE of the estimated LST for all sites after remove outliers are 0.09K and 2.20K, respectively. This indicates that the algorithm is suitable for producing LST product from Landsat 8 TIRS data and ASTER GED can be used to improve the accuracy of land surface emissivity in arid and semi-arid area.

To improve the spatial resolution of the global aerosol optical depth (AOD) distribution, a new method for AOD retrieval is proposed over land in this paper. A monthly Global Land Surface Reflectance Database (GLSRD) was constructed using the long time serious of MODIS surface reflectance product (MOD09A1) and used for the surface reflectance estimation over land for AOD retrieval. A seasonal Global Land Aerosol Type Database (GLATD) was also built based on the MODIS aerosol product (MOD04) and used for providing the aerosol types over land in AOD retrieval. Thus, the AOD global dataset with 1 km resolution was produced and was validated against with the AERONET ground-based AOD measurements located in the global 172 stations over land. Results showed that the AOD retrievals are highly consistent with AERONET AODs and showed an overall better precision over both dark and bright areas.

Cloud detection is a key processing step before extracting information of earth surface from the earth observation data. Lots of schemes have been developed for cloud detection, static threshold method is the main method that is widely used in cloud detection. However, for the huge difference between different land objects, it is much difficult to find a proper threshold to detect the cloudy pixel from clear sky, especially, when the land covered by the thin or broken cloud. Therefore, a dynamic threshold cloud detection algorithm was proposed in this paper to improve the cloud detection. A priori monthly surface reflectance database was constructed using MODIS surface reflectance products and used to estimate the surface reflectance for dynamic threshold determination. Dynamic thresholds were determined by the simulation relationships between the apparent reflectance and the surface reflectance under clear conditions with 6S model. MODIS and Landsat 8 OLI data were selected to perform the experiments. Results showed that this new algorithm demonstrated better detection results of different cloud types over different land types.

Desert plays a very import role on earth radiation budget and calibration research. In this paper, we propose a new algorithm for retrieving BRDF of desert using time series of MODIS imagery. The central idea of this algorithm is to detect the "clearest" observation during a temporal window for each pixel. For desert, the temporal window can be one year since its surface is highly stable. The clear observations are then used to fit the desert BRDF. Finally, the fitted BRDF is used to simulate the MODIS images under "real" conditions and the simulated MODIS images are compared with MODIS surface reflectance product (MOD09 and MYD09), which shows that the R2 and RMSE of the simulated surface reflectance is much better than those of MOD09 product. Therefore, the derived BRDF from this new algorithm much more accurately describe the directional characterization of the desert site.

Land surface emissivity (LSE) is an essential parameter in deriving land surface temperature form remote sensing data. According to the single channel characteristics of HJ-1B Infrared Scanner (IRS), a combined method for estimating LSE was proposed based on the vegetation cover method and classification-based method. The proposed method requires inputs such as static land cover product, vegetation and ground emissivity for each land cover and vegetation cover product. The sensitivity analysis indicates that this method could achieve good accuracy with LSE relative errors vary from 0.4% to 2%.

Land surface temperature (LST) is required for a wide variety of scientific studies, from climatology to hydrology and ecology. The feasibility of using atmospheric profile extracted from NCEP data for LST retrieval from HJ-1B/IRS data was analyzed in this paper. A series of ground measurements were carried out to validate the IRS LST results in Hebei province, China, from May to September, 2010. The results indicate that the LST derived from IRS data by using NCEP data showed a good agreement with the ground LSTs, with RSEM lower than 1.5K. Therefore, it can be concluded that the profile extracted from NCEP data is a useful source for LST retrieval from HJ-1B/IRS data.

In this paper, we propose a method to extract the feature of Bi-directional Reflectance Distribution Functions (BRDF) over Badain Jaran Desert using Landsat-TM/ETM+ and ASTER GDEM data. Badain Jaran Desert is characterized with homogeneous and rugged terrain, which forms a natural Bi-directional Reflectance data sets with hypotheses that the surface structure of each slope element does not vary with the variations of slope and aspect; therefore, we can use nadir view Landsat-TM/ETM+ imagery reconstruct the BRDF characterization of this experimental site. The results show that this method can simulate the BRDF feature of land surface accurately.

Aerosol optical thickness (AOT) is retrieved from HJ-1A/B images using Structure Function Method (SFM) over Beijing and its surrounding area. SFM is discussed by establishing structure function formula, choosing window size and distance value. Retrieved result is validated by the ground-based observation.

Based on the four step method, the paper puts forward a method for deriving mid-infrared emissivity. This method obtains thermal infrared emissivity and temperature with high accuracy by utilizing the ISSTES algorithm from thermal infrared data, then introducing the derived temperature into mid-infrared emissivity extraction, reducing the number of parameters need to be inversed in mid-infrared, forming redundant observation, and using the least square method to solve the equation at last. More attention has been paid into analyzing the impacts of instrument calibration error and simplification of radiative transfer equation on the extraction of mid-infrared emissivity. Finally, the paper gives out the reason for large error of emissivity inversion in some bands of mid infrared based on the simulated data.

Due to the failure of Landstat 7 ETM+, Landstat 5 images are widely used in many fields again since May 2003. In this letter, a method, based MODTRAN+TM+topographical maps, was used to solve the preprocessing problems of TM images towards retrieving of the endmembers. It proved effective to retrieve the reflectance values of surface substances and confirm the pure surface endmembers before an in-site spectral experiment. The result was also consistent with actual reflectance of surface substances. A case of retrieving endmembers in Luancheng, China was too demonstrated.

In this paper, a generalized split-window method to derive land surface temperature (LST) from MODIS (Moderate Resolution Imaging Spectroradiometer) data is applied. A major problem in land surface temperature inversion is that there are too many unknown variables, especially for MODIS data which is in low resolution, one pixel is a mixture of several cover types. To analysis the uncertainties of the LST retrieval algorithm based on MODIS images, the field measurements, together with fine resolution images, AMTIS (the airborne multi-angle TIR/VNIR imaging system) data and ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer) data have been used

Land-cover classification represents one of the most fundamental applications of remote sensing, and is widely used to estimate carton stocks and parameters hydrological and biogeochemical models. Several studies reveal that changing the spatial resolution of land-cover maps has important effects on the proportion of a landscape occupied by a particular land cover type. We study the proportions of vegetations based on multi-scale land-cover classifications in the area of Qianyanzhou in the province of Jiangxi in the South China on the base of ground investigations. The viability of coarse spatial resolution data for land-cover classification is evaluated using degraded Landsat Thematic Mapper (TM). The uncertainties of multi-scale land cover classifications are finally analyzed based on the different aggregated TM land-cover maps.