Land surface temperature (LST) is a critical parameter at the land-atmosphere interface, playing a key role in hydrology, climate, and ecological studies. The widely used generalized split-window (GSW) algorithm assumes constant aerosol influence, yet aerosol variability introduces significant errors in LST retrieval. To address this limitation, we propose an improved algorithm (GSW_AOD) that explicitly incorporates an aerosol optical depth (AOD) correction term into the GSW framework. Evaluation using an independent simulation dataset demonstrated substantial improvement. Under low-aerosol conditions (AOD < 0.3), the root mean squared error (RMSE) decreased from approximately 1.7 K for GSW to 0.8 K for GSW_AOD. Under high-aerosol conditions (AOD ≥ 0.3), RMSE decreased more dramatically from approximately 4.0 K for GSW to 1.7 K for GSW_AOD. Validation with in situ measurements from eight ground sites showed that while both algorithms performed similarly at low AOD (AOD < 0.3; RMSEs between 2.0 K and 3.3 K, differences < 0.3 K), GSW_AOD significantly outperformed GSW under high aerosol loading (AOD ≥ 0.3), where GSW RMSEs reached 2.8 K to 4.0 K. Compared to the standard MODIS LST product, GSW_AOD achieved lower RMSEs at most sites for AOD < 0.3 and reduced RMSEs at all sites for AOD ≥ 0.3. These results confirm that explicitly correcting for variable AOD significantly enhances the accuracy and robustness of thermal infrared LST retrievals, particularly under moderate to high aerosol conditions.

Land surface temperature (LST) is a crucial parameter driving water and heat exchange at the surface-atmosphere interface. Satellite-derived LST require rigorous validation to ensure its reliability in Earth system modeling and climate change research. To address validation accuracy degradation caused by cloud contamination artifacts and satellite-ground spatiotemporal mismatch errors, conventional mean- and median-based outlier detection methods were commonly used in the validation of satellite-derived LST products using in situ measurements. However, both methods are based solely on the degree of deviation within statistical data itself, without considering the uncertainties associated with satellite-derived and ground-based LST. This limitation could result in biased identification of outliers in satellite-derived LST validation. In this study, an uncertainty-based method was proposed to detect outliers in the validation of MODIS-derived LST using in situ measurements. This method quantifies total LST uncertainty budgets to flag anomalous data points by integrating uncertainties from both satellite retrievals and ground observations. Validation results across SURFRAD sites demonstrate the method’s efficacy when compared with those without outlier detection. Daytime implementation achieves significant root mean squared error (RMSE) reductions, notably at the BND site with a 3.1 K improvement, while nighttime applications yield marginal enhancements (< 0.4 K), reflecting diminished thermal contrast and uncertainty components during nighttime. The uncertainty-based method consistently outperforms conventional mean- and median-based methods during daytime, with RMSE improvements ranging from 0.2 K at DRA to 2.6 K at BND. Site-specific variations highlight the method’s sensitivity to surface heterogeneity and vegetation dynamics. All methods exhibit comparable performance at night (ΔRMSE < 0.15 K).

Rice extraction in complex landscapes is a challenging issue in remote sensing, particularly in areas with diverse land use types and spatiotemporal variability. To enhance the accuracy of rice extraction, this study proposes a novel approach integrating Sentinel-2 spectral-temporal-spatial imagery with a hybrid deep learning architecture for extracting single-cropping and double-cropping rice. Firstly, a time-series dataset of spectral and texture features was constructed to capture the seasonal variations of rice. Secondly, an active learning strategy was employed to select high-confidence samples, and spectral, temporal, and spatial information was integrated into a unified dataset. Finally, a hybrid deep learning model, Convolutional-Transformer Hybrid Network (CTH-Net), was developed, combining Convolutional Neural Networks (CNN) and Transformer networks. The model incorporates a fusion module to effectively integrate multi-scale temporal, spatial, and spectral features and a residual module to improve gradient flow, mitigating the vanishing gradient problem in deep networks. Results demonstrate that CTH-Net achieved 99.69% overall accuracy in rice extraction, maintaining >96% accuracy for single-cropping rice, double-cropping rice, and abandoned land. It outperformed models like CNN, Transformer, Long Short-Term Memory (LSTM), and Support Vector Machine (SVM) in handling fragmented rice distributions and mixed land types, significantly improving extraction accuracy. This study provides an efficient and reliable solution for rice extraction in complex landscapes, supporting agricultural monitoring and management.

Large-scale crop mapping is essential for decision-makers to evaluate agricultural resource usage and estimate crop yields. Considering the utility of annual crop inventory (CI) statistics for monitoring crop growth, generalizing near real-time crop classification over large areas becomes necessary. Accurate crop-type identification using remote sensing data remains challenging due to the variability in crop growth patterns across time and space, the presence of crops with similar phenological stages, and the scarcity of labeled data. This study develops deep learning-based approaches to map agricultural regions at the county level using multitemporal Sentinel-1 synthetic aperture radar (SAR) data, specifically evaluating the contribution of SAR-derived input predictors for discriminating both majority and minority crops in Bei’an, Northeast China. The proposed model architecture amalgamates 1-D convolutional layers (Conv1D) with attention-based long short-term memory (LSTM) to characterize the crop types exhibiting phenological similarities using a range of SAR-derived input predictors. The results are compared with alternative multitemporal deep learning frameworks, including standalone Conv1D and Transformer models, as well as the machine learning algorithm random forest (RF), which serves as the baseline for comparison. The designed architecture (Conv1D-LSTM) achieved the highest$F1$scores (maize: 87%, soybean: 86%, and other crops: 85%) when applied to an inherently imbalanced dataset, using m-chi decomposition features as input predictors. The results provide superior performance in terms of effectiveness and efficiency compared to other selected models. The monthly in-season crop classification underscores the importance of temporal dependencies and the availability of multitemporal observations for learning dynamic growth patterns over large areas. Furthermore, the interpretation of model learning processes and outcomes is explained by visualizing weight distributions and hidden features. This study offers a comprehensive evaluation of essential SAR features in multitemporal satellite data for accurate crop mapping, utilizing advanced deep learning techniques. This work is available athttps://github.com/Niantangliu/Deep-learning-crop-mapping.

Land surface temperature (LST) serves as a critical biophysical parameter characterizing global-scale surface energy partitioning and water exchange processes. The Split-Window (SW) and Temperature-Emissivity Separation (TES) methods are the most widely used LST retrieval methods, but they rely on accurate land surface emissivity and atmospheric correction as prior knowledge, respectively. While the SW-TES hybrid method mitigates these limitations, atmospheric correction errors in the SW stage can accumulate into the TES stage, thereby affecting the accuracy of LST retrieval. To address this limitation, this study develops an improved SW-TES algorithm that incorporates total column water vapor as a correction factor for surface-leaving radiance calculations. Based on GOES-16 ABI data, we utilized the improved algorithm to retrieve hourly LST estimates for the contiguous United States in 2020 and validated the results against the GOES-16 LST product and ground-based measurements from the Surface Radiation Budget Network (SURFRAD). The retrieved LST exhibits high consistency with the GOES-16 LST product, demonstrating a root mean square error (RMSE) of 1.48 K and a bias of 0.90 K. Based on SURFRAD ground observations, the improved SW-TES algorithm achieves an average RMSE of 2.27 K and a bias of 0.26 K across seven validation sites. In comparison, the GOES-16 LST product shows an RMSE of 2.86 K and a bias of –1.21 K at the same sites. Furthermore, the algorithm maintains superior stability under high temperature and high water vapor content conditions.

Land Surface Temperature (LST) estimation relies on precise measurements of surface thermal infrared (TIR) radiance. LST, classified as an essential climate variable (ECV), exhibits rapid temporal variations within specific spatial scales, closely tied to illumination and scan angles. To enhance the utility of LST products, this study proposes a novel approach for concurrently reconstructing the temporal profile and angular dependence. The suggested method, known as the visible-thermal envelope method, integrates kernel-driven (KD) and diurnal temperature cycle (DTC) models, addressing surface structure and thermal factors, respectively. Additionally, this method facilitates LST downscaling by leveraging the higher spatial resolution of visible and near-infrared (VNIR) data, assuming temperature differences are homogeneous within coarse pixels. To validate the reliability of the proposed approach, TIR data from the geostationary satellite Himawari 8 are amalgamated with VNIR data from the polar-orbit satellite Sentinel-3A/3B. When compared to field measurements, the reconstructed results exhibit improvements with a total bias of 0.55 K and Root Mean Square Error (RMSE) of 2.14 K. Notably, in contrast to the original uncorrected results, this correction results in an approximate 50% reduction in bias and a 10% decrease in RMSE.

Surface temperature (ST) is a vital physical parameter influencing surface-atmosphere interactions. This study presents an uncertainty-based validation approach applied to Sentinel-3/SLSTR land surface temperature (LST) and sea surface temperature (SST) products.In situmeasurements were obtained from various sites in China, namely, the Dunhuang Gobi site (DHGS), Huailai Guanting Reservoir site (HGRS), Wuliangsuhai Lake site (WLSLS) and Yantai Ocean site (YTOS). The spatial representativeness ofin situmeasurements at each site was assessed using available clear-sky and high-quality ASTER LST products from April 2000 to June 2023. The four sites exhibited high spatial homogeneity, demonstrating suitability for validating STs. Therefore,in situmeasurements from these homogeneous sites were used to validate the Sentinel-3/SLSTR ST products during the daytime and nighttime using a temperature-based method. The results showed that the root mean square error (RMSE) values are lower than 1.6 K, except for those at DHGS. Furthermore, since ground-based ST validation is affected by the coupled effects of surface and atmospheric characteristics, the validation results are different under different atmospheric and surface conditions. Consequently, assessing the consistency among multiple validation results becomes challenging. To address this issue, by assuming the independence of the validation samples, we propose a method for obtaining the key comparison reference value (KCRV) from multiple validation results based on Sentinel-3/SLSTR ST products. The KCRV is close to the ‘true’ value, indicating the high quality of the validation results. For the Sentinel-3A/SLSTR and Sentinel-3B/SLSTR LST products, the KCRVs are 1.91 K and 1.71 K, respectively, with corresponding uncertainties of 0.08 K and 0.08 K, respectively. Similarly, for the Sentinel-3A/SLSTR and Sentinel-3B/SLSTR SST products, the KCRVs are 0.78 K and 0.71 K, respectively, with uncertainties of 0.08 K and 0.07 K, respectively.

Thermal infrared hyperspectral imagery presents a superior capability for capturing intricate spectral details of atmospheres and ground objects compared to multispectral images, thus offering a more nuanced dataset for land surface temperature (LST) retrieval. However, extensive inter-band correlations pose computational challenges and undesirable “dimension disaster” problem. To address this issue, this paper proposes a purpose-built framework of thermal infrared hyperspectral band selection using graph neural network for LST retrieval. Specifically, the thermal infrared hyperspectral data is firstly mapped onto a graph topology, followed by feeding it into a graph attention module with brightness temperature constraints to extract band features. Following this, the extracted band features undergo a comprehensive analysis through a multi-scale convolution module consisting of convolution kernels with multiple sizes, which has more variety and larger receptive fields for calculating the correlation between different bands features, assigning different weights to each band. Finally, a weight selection module is designed to filter the bands based on their assigned weights, creating a subset of bands with greater significance for LST retrieval. Training the designed model, 65100 observations are simulated utilizing MODTRAN, 80% allocated for training and 20% for testing. The experimental results validate the effectiveness of the proposed model, with a Root Mean Square Error (RMSE) of 1.85 K in practical applications on IASI imagery. This accomplishment substantiates the model’s capacity to reliably employ a judiciously selected subset of thermal infrared hyperspectral bands for LST retrieval applications, thus offering a promising contribution to the advancement of thermal infrared hyperspectral image processing methodologies.

Land surface temperature (LST) plays a crucial role in the physical and chemical processes of the land–atmosphere system. Remote sensing technology has greatly advanced the measurement of thermal infrared LST (TIR LST), which is the most widely utilized surface temperature product. However, cloud cover and mist often cause significant data loss in TIR LST. To address this issue and reconstruct the MYD11A1 LST under cloudy conditions, this study proposes an all-weather LST generation method based on the extreme gradient boosting (XGBoost) model. This method incorporates spatial-seamless passive microwave LST (PMW LST) to capture the nonlinear relationship between TIR LST and other variables. Compared to the MYD11A1 LST, the generated all-weather LST provides continuous spatial texture information without a significant boundary reconstruction effect, improving the accuracy of spatiotemporal variations in LST in China. In situ validation demonstrated the high accuracy of the generated all-weather LST, with mean$R^{2}$, bias, and unbiased root-mean-square error (ubRMSE) of 0.96 (0.91), 1.08 K (3.61 K), and 2.92 K (4.54 K) under clear (cloudy) daytime conditions, and 0.92 (0.95), −0.93 K (−2.96 K), and 3.09 K (3.04 K) under clear (cloudy) nighttime conditions. These results indicate the feasibility and reasonableness of the all-weather LST generation method developed in this study and affirm its ability to generate highly accurate all-weather LST.

Land surface temperature (LST) is an important physical parameter at the interface between the Earth’s surface and the atmosphere. Accurately quantifying LST uncertainty is essential for the generation of a long-term and consistent LST Climate Data Record (CDR) or Earth System Data Record (ESDR) from either multiple sensors or algorithms. In this study, a physical-based method was proposed to quantify the uncertainty of LST retrieval from satellite thermal infrared (TIR) data using the generalized split-window (GSW) algorithm. LST uncertainties were parameterized as a function of brightness temperature at the top of the atmosphere (TOA) and surface emissivity in two split-window channels, which are two key input parameters in the GSW algorithm, as well as their uncertainties. The performance of the parameterized uncertainty model was evaluated according to the simulation dataset at six prescribed viewing zenith angles (VZAs) of 0°, 33.56°, 44.42°, 51.32°, 56.25°, and 60°, with a root mean squared error (RMSE) of 0.001 K. The coefficients of the parameterized uncertainty model at arbitrary VZA within a sensor’s field of view (FOV) can be obtained by linear interpolation of the coefficients at the six prescribed VZAs. Once the coefficients of the parameterized uncertainty model for each pixel are available, total LST uncertainties can be quantified on a pixel-by-pixel basis. As an example, the parameterized uncertainty model was applied to actual MODIS data for displaying the spatial distribution of LST uncertainties. The results indicate that the parameterized uncertainty model can characterize the spatial variation in LST uncertainties well over various land cover types.

Land surface temperature (LST) reflects the cold and hot conditions of the land surface and is one of the most important geophysical parameters in the study and research of the land–atmosphere system. Passive microwave (PMW) is one of the primary techniques for obtaining spatially continuous LST at regional, continental, and global scales. However, there is an orbital gap in the LST retrieved from PMW (PMW LST) due to the scanning scheme of the PMW sensor, which limits the application of PMW LST, so it is necessary for the proposed some methods to fill the orbital gap of PMW LST. In this study, a new orbital gap-filling method based on a deep neural network (DNN) was developed to address the issue of PMW LST orbital gaps. This method first established the DNN model based on the nonlinear relationship between AMSR2 LST and 11 environmental variables and then used the DNN model to generate a new spatially continuous LST product, namely, DNN-LST, and, finally, used DNN-LST to fill the orbital gaps of AMSR2 LST to generate the daytime/nighttime spatially seamless gap-filled LST (GF-LST) product for China from 2012 to 2020. GF-LST can more correctly represent the spatiotemporal variation of surface temperature in China than AMSR2 LST because it has continuous spatial texture information and no obvious boundary reconstruction effect. After verifying the accuracy of GF-LST products through simulated gap region validation and in situ validation, it can be found that: 1) DNN-LST in simulated gap regions showed high accuracy during the daytime and nighttime on July 15, 2012–2020, and the mean values of bias and root mean square error (RMSE) compared with AMSR2 LST at day (night) were, respectively, −0.08 K (−0.22 K) and 1.89 K (2.23 K); 2) the accuracy of DNN-LST was the best in autumn (mean RMSE values of 1.43 K at day and 1.89 K at night) and the worst in winter (mean RMSE values of 2.35 K at day and 2.36 K at night), no matter during daytime or nighttime, in different seasons in 2015–2017; 3) the RMSE value of DNN-LST during nighttime was slightly higher than the RMSE value of DNN-LST during daytime; and 4) the accuracy of DNN-LST was equivalent to AMSR2 LST, that is, the unbiased RMSE (ubRMSE) of DNN-LST and AMSR2 LST was all about 4 K compared with in situ LST, but the ubRMSE of DNN-LST was slightly lower than AMSR2 LST. The above accuracy validation analysis shows that DNN-LST has good robustness and good spatial consistency with AMSR2 LST and can be well used to fill the orbital gap of AMSR2 LST to generate spatial seamless GF-LST product.

Three operational long-term land surface temperature (LST) products from Landsat series are available to the community until now, i.e., U.S. Geological Survey (USGS) LST, Instituto Português do Mar e da Atmosfera (IPMA) LST, and China University of Geosciences (CUG) LST. A comprehensive assessment of these LST products is essential for their subsequent applications (APPs) in energy, water, and carbon cycle modeling. In this study, an evaluation of these three Landsat LST products was performed using in situ LST measurements from five networks [surface radiation budget (SURFRAD), atmospheric radiation measurement (ARM), Heihe watershed allied telemetry experimental research (HiWATER), baseline surface radiation network (BSRN), and National Data Buoy Center (NDBC)] for the period of 2009–2019. Results reveal that the overall accuracies of CUG LST with bias [root-mean-square error (RMSE)] of 0.54 K (2.19 K) and IPMA LST with bias (RMSE) of 0.59 K (2.34 K) are marginally superior to USGS LST with bias (RMSE) of 0.96 K (2.51 K). The RMSE of USGS LST is about 0.3 K less than IPMA/CUG LST at water surface sites and is about 0.4 K higher than IPMA/CUG LST at cropland and shrubland sites. As for tundra, grassland, and forest sites, the RMSEs of three Landsat LST products are similar, and the RMSE difference among three Landsat LST products is < 0.18 K. Considering the close emissivity estimates over water surface in these three LST data, USGS LST has a better performance in atmospheric correction over water surface compared with IPMA/CUG LST. For land surface sites, the RMSE of LST increases initially and then decreases with land surface emissivity (LSE) for three Landsat LST products. This indicates that the emissivity correction has a large uncertainty for moderately vegetated surface with emissivity ranging from 0.970 to 0.980. Underestimated emissivity for USGS LST at vegetated sites leads to overestimation of LST, which could have led to the higher bias and RMSE compared with IPMA/CUG LST. For the LST retrievals for the three different sensors [i.e., Thematic Mapper (TM), Enhanced Thematic Mapper Plus (ETM+), and thermal infrared sensor (TIRS)] onboard the Landsat satellite series, the accuracies are consistent and comparable, which is beneficial for providing long-term and coherent LST.

Accurate retrieval of land surface temperature (LST) over urban areas is of great significance for urban thermal environment monitoring. In previous studies, most of the urban LST retrieval methods were developed based on the assumption of a flat surface without considering the influence of urban 3-D geometry structure, which has a significant impact on the retrieval accuracy of LST over urban areas. In this study, a radiative transfer equation (RTE)-based single-channel method was developed to retrieve LST with urban geometry effect correction from the Landsat 8 thermal infrared (TIR) data in band 10. The increase in adjacent radiance from the surrounding pixels and the decrease in atmospheric downwelling radiance caused by urban geometry structure were taken into account in this method. Because it is difficult to directly validate the retrieval accuracy of LST over urban areas usingin situLST measurements, the performance of the RTE-based LST retrieval method was evaluated via comparing brightness temperature (BT) at the top of the atmosphere (TOA) simulated by the discrete anisotropic radiative transfer (DART) model and the urban RTE over three subregions. There is a good agreement between BT at the TOA simulated by the DART model and the urban RTE, with a root-mean-squared error (RMSE) of less than 0.25 K. The variations in LST retrieved with urban geometry effect correction over different local climate zones (LCZs) were analyzed. In general, built-up LCZs have relatively higher LST than land cover LCZs. The differences between LST retrieved without/with urban geometry effect correction over different LCZs are greater than 0.2 K. The largest average LST difference over built-up LCZs is approximately 0.9 K, whereas that over land cover LCZs is approximately 0.65 K. LST retrieved without/with urban geometry effect correction was used to calculate urban heat island intensity (UHII) in terms of the LCZ-based method. The results indicate that UHII calculated from LST with urban geometry effect correction is lower than that calculated from LST without urban geometry effect correction, with an average difference of approximately 0.5 K.

Land surface temperature (LST) and soil moisture (SM) are two important parameters in land surface ecosystem at regional and global scale. The accurate acquisition of LST and SM can benefit various fields, including agriculture and climate which are closely related to human life. This study proposed a simultaneous retrieval method of LST and SM based on the approximate and correction of passive microwave radiation transfer equation. Compared to LST and SM in simulated database, the accuracy of retrieved LST is approximately 1.63 K and the accuracy of retrieved SM is about 0.063 m3/m3.

Land Surface Emissivity (LSE) is an important parameter in thermal infrared remote sensing, which is of great significance to temperature inversion. In this study, the Gradient Boost Regression Tree (GBRT) was proposed to directly retrieve LSEs of MODIS thermal infrared channels 29$(8.4-8.7\ \mu \mathrm{m}), 31(10.78-11.28\ \mu \mathrm{m})$, and 32 ($11.77-12.27\ \mu \mathrm{m}$) from the visible and near infrared (VNIR) data. We selected the variables related with LSE, including reflectivity, view zenith, solar zenith, land surface type, vegetation index (EVI), Normalized Difference Water Index (NDWI) and Leaf Area Index (LAI). The results of the test set showed that RMSEs of the estimated LSEs were 0.013 in channel 29, 0.005 in 31 and 0.004 in 32, which were more accurate than existing methods. Eight regions with different ground features were also selected to further evaluate the applicability of the model. In most areas, the RMSEs were below 0.015 in channel 29, below 0.005 in channel 31 and 32. In addition, the spatial distributions of the estimated LSEs and those extracted from MYD11B1 and MYD21A1D in H19V08 were compared, which were also reasonable. In general, it is feasible to use the selected variables with the GBRT model to directly retrieve the LSEs.

Relative humidity (RH) is one of the key variables for understanding the water, energy, and carbon exchange between the Earth and the atmosphere. Traditional methods for deriving RH from remotely sensed data usually require ground meteorological observations or are limited to clear-sky conditions, thereby making it a significant challenge to obtain spatially complete RH under all-sky conditions, especially over the regions with sparse meteorological instruments for observation. To this end, a new approach for deriving all-sky RH entirely based on Moderate Resolution Imaging Spectroradiometer (MODIS) data was proposed in the present study. Two key assumptions in the approach under cloudy conditions are that the actual water vapor is linearly related to the total precipitable water vapor (PWV) and that air temperature is linearly related to land surface temperature (LST). Results from a total of 30 AmeriFlux stations proved the aforementioned assumptions based on MODIS data collected over a study period of three years from 2009 to 2011. For different aridity conditions, RH retrieval revealed reasonable accuracy with a root-mean-square error (RMSE) of approximately 15.3% over an arid and semiarid region, whereas a comparable RMSE of 17.0% was obtained over a humid area. Further results also indicated that the aforementioned linear relationships were generally temporally stable, thereby indicating that the proposed method can be used to obtain all-sky RH at a regional or global scale entirely based on MOD06_L2-derived LST and MOD05_L2-derived PWV data given that the assumed linear relationships can be easily determined by historical MOD07_L2-derived atmospheric profiles.

Accurate estimation of land surface temperature (LST) is crucial for ecological environment monitoring and climate change studies in mountainous areas. The current LST retrieval algorithms were developed without accounting for the topographic effect, which can only be used to retrieve LST over relatively flat surfaces. Due to the impact of 3-D structure of mountainous surfaces, rugged terrain makes the processes of thermal radiation more complex. In this study, a radiative transfer equation (RTE)-based single-channel algorithm was proposed to retrieve LST with topographic effect correction from the Landsat 8 thermal infrared (TIR) data in mountainous areas. This algorithm accounts for the changes in the thermal radiation components in the TIR RTE caused by the topographic effect. According to the analysis of simulation data, sky-view factor (SVF), atmospheric water vapor content, surface emissivity of target pixel, and average LST of the surrounding terrain have significant influence on the magnitude of the topographic effect. The differences between the LST retrieved without/with topographic effect correction from the Landsat 8 TIR data are related to SVF. The topographic effect should be taken into account in the LST retrieval algorithm when SVF is smaller than 0.7. The largest LST difference of approximately 1 K occurs in the deep valley. The results indicate that LST without topographic effect correction could be overestimated to be as high as 1 K. Due to a lack ofin situLST measurements, the performance of the LST retrieval algorithm in mountainous areas was only evaluated by comparing the brightness temperature (BT) at the top of the atmosphere (TOA) simulated by the DART+MODTRAN model and the TIR RTE over mountainous surfaces at three subregions. There is a good consistency between BT at the TOA simulated by the DART+MODTRAN model and the TIR RTE over mountainous surfaces at the three subregions, with a root-mean-squared error (RMSE) of less than 0.23 K.

The tradeoff between spatial and temporal resolution in satellite observations substantially restrains the potential applications of Land Surface Temperature (LST) products. So far, many spatiotemporal fusion models have been developed to address the issue and a unified comparison in LST data fusion is still required. In this paper, four popular spatiotemporal fusion algorithms including Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM), Unmixing-based data fusion method, Flexible Spatiotemporal Data Fusion (FSDAF) and Spatio-Temporal Integrated Temperature Fusion Model (STITFM) were adopted to generate high spatial resolution LST using polar-orbiting and geostationary satellite data. The predicted LST was evaluated by the actual LST product and the result indicates that the overall accuracy of FSDAF is satisfied (about 2.87K) and the FSDAF algorithm is recommended to generate LSTs at high spatial and temporal resolution in heterogeneous area.

Evapotranspiration (ET) is one of the most critical parameters in water- and energy-related domains. Two basic assumptions with respect to soil-moisture variation have been widely investigated for the retrieval of ET based on the trapezoid methods. Specifically, soil moisture within the surface and root-zone layers was assumed to vary synchronously in most of the earlier analyses. However, several recent investigations assumed that soil moisture within the upper soil layer should be dried up before the root-zone layer is stressed. To this end, the retrieval of ET under different aridity conditions over North American grasslands was investigated with the two assumptions, and the estimated ET was assessed using the flux data collected from eight AmeriFlux sites. Based on the available data from 2002 to 2018, results showed that the “asynchronous-assumed” method can obtain better ET estimates than the “synchronous-assumed” method over semiarid and subhumid areas, whereas the “synchronous-assumed” method can obtain better ET estimates in humid areas. Moreover, because of the different closure techniques used for the ET correction, no consistent conclusions could be found for the arid conditions to determine which trapezoid was better. Specifically, it was found that the cases of surface soil with zero water availability that were defined by the asynchronous-assumed trapezoid method rarely occur, even in arid areas, which indicated that the critical boundary that determines whether the root-zone layer begins to be water-stressed may need to be redefined.

The change of global thermal environment plays an important role in land surface processes. In this study, global thermal environment was analyzed using the vertically polarized brightness temperature at 36.5 GHz. The daily brightness temperature from 2003 to 2010 were decomposed using the annual temperature cycle (ATC) model, and the annual cycle parameters (ACPs) were obtained. The results show that the brightness temperature decreases with the increasing latitudes respectively for the northern hemisphere and the southern hemisphere. The land covered by vegetation is colder than the desert and barren. Some plateaus lead to lower brightness temperature than surrounding areas. In addition, the atmospheric and ocean circulation also affect global brightness temperature. The ACPs from brightness temperature can generally characterize the global thermal environment.

Passive microwave data are much less affected by clouds than TIR data for the retrieval of land surface temperature (LST), providing its unique advantages in global mapping of LST. In this study, a physically-based algorithm for LST retrieval was applied to AMSR2 global brightness temperature data. The performances of this algorithm applied on different land cover types were further evaluated against nighttime MYD11A1 thermal infrared LST products. The results showed that (i) the overall accuracy of the algorithm is about 5.42 K by root mean square error (RMSE) and 2.99 K by bias against MODIS LST during nighttime; (ii) the algorithm overestimates the LST over all land types. The overestimation is most evident over barren/sparsely vegetated surfaces. The algorithm shows that the algorithm has a robust performance comparing with MODIS LST and could be applied to estimate LST effectively.

In this work, land surface temperature (LST) was downscaled by statistical regression model based on the nonlinear relationship with environment variables, including land surface reflectance, spectral indices, terrain factors, land cover type, reanalysis data and geolocation information. The correlation between predictor variables and LST was examined and compared with each other, in which 16 variables were finally selected into model, the variable dataset was credited to have relatively best performance with the trade-off between algorithm accuracy and computational complexity. With the optimal variable dataset, the LST of Moderate Resolution Imaging Spectroradiometer (MODIS) was downscaled from 990m to 90m by using random forest (RF) regression algorithm. Results of visual and quantitative analysis showed the satisfied downscaling results on 13 May, 2017 in Qinyang City, with the bias, coefficient of determination (R2) and root mean square error (RMSE) of -0.02, 0.9 and 2.18 K, respectively. Comparison with the algorithm for sharpening thermal imagery (TsHARP) also demonstrated the accuracy and robustness of RF model with selected variable dataset.

Evapotranspiration (ET) plays a key role for energy transfer and water circulation in the biosphere, lithosphere, hydrosphere, cryosphere and atmosphere. In present study, spatially complete ET over the Heihe river basin, Northwest of China, was estimated from the synergistic use of MODIS (MODerate-resolution Imaging Spectroradiometer) data and CLDAS (China Meteorological Administration Land Data Assimilation) gridded meteorological data from June 1 to September 15 in 2012. For the estimation of ET over clear-sky pixels, a pixel-to-pixel pattern of land surface temperature (LST)-vegetation index (VI) feature space was developed where meteorological data were used to determine the dry and wet edges for each pixel; whereas the traditional Penman-Monteith equation was implemented to obtain ET over clouds pixels. Finally, ground ET measurements collected at two sites (corn and orchard) were used to evaluate the estimated results, root mean square error (RMSE) of 77.2W/m2and 74.9W/m2can be obtained for the two sites, respectively, indicating that spatially complete ET can be derived from currently available satellite images and meteorological data.

This paper presents a complement analysis for the wavelet transform method for separating temperature and emissivity (WTTES) with different wavelets, wavelet levels and biased atmospheric downwelling radiance. According to the results, the WTTES algorithm is quite insensitive to the choice of the wavelet. By comparing the retrievals with different wavelet levels, a wavelet level of n=3 or n=4 is more recommended in most cases. In addition, compared with the white noise, the WTTES algorithm is more sensitive to the atmospheric downwelling radiance with bias errors. For the profile with a bias error of 10%, the RMSE of the emissivity retrievals can be increased approximately 0.17%-2.33%, which depends on the specified water vapor content of the profile. However, different from the obvious errors on emissivity, the overall accuracies of the temperature retrievals under different atmospheric profiles are all less than 0.7K, which means the WTTES algorithm is still feasible to retrieve the temperature under the condition of biased moisture profiles.

Land surface temperature is an important driving force in the exchange of water, heat, and even CO2at the surface-atmosphere interface in the desert regions. The rapid and continuous measurements of land surface temperature are meaningful to the ecological and environmental researches. A physically based single-frequency and double-polarization algorithm for retrieving land surface temperature is developed in this study. The 18.7 GHz vertically polarized emissivities are firstly estimated from the Polarization Ratio (PR, defined as the ratio of the horizontal to vertical brightness temperature at the same frequency) at 18.7 GHz. And then the estimated emissivities can be directly used to retrieve land surface temperature without considering the atmospheric effect. A preliminary validation is done in the Taklimakan desert. The retrieved land surface temperatures are compared to the infrared land surface temperature products for all the year of 2007 with a Root Mean Square Error (RMSE) of 3.05 K.

Land surface temperatures (LSTs) at high spatial resolution are crucial for hydrological, meteorological, and ecological studies. Downscaling LSTs from coarse resolution to finer resolution is an alternative way to obtain LSTs at high spatial resolution. In this paper, we proposed a new algorithm based on geographically weighted regression (GWR) to downscale Moderate Resolution Imaging Spectroradiometer LST data from 990 to 90 m. Unlike previous LST downscaling algorithms, this algorithm built the nonstationary relationship between LST and other environmental factors (including the normalized difference vegetation index and a digital elevation model) using geographically varying regression coefficients. The uncertainty in this algorithm was evaluated with a sensitivity analysis. The results show that the total uncertainty in this algorithm is less than 2 K. The performance of the GWR-based algorithm was assessed using concurrent ASTER LST data as a reference LST data set. Moreover, this algorithm was compared against the TsHARP algorithm, which was widely used for LST downscaling. The results indicate that the GWR-based algorithm outperforms the TsHARP algorithm in terms of statistical results. The root mean square error (mean absolute error) value decreases from 3.6 K (2.7 K) for the TsHARP algorithm to 3.1 K (2.3 K) for the GWR-based algorithm.

Land surface temperature (LST) is a crucial parameter in analyzing and evaluating climate change at various scales, the surface energy balance, soil moisture, evapotranspiration and urban heat islands. Currently, methods for its estimation from space have continuously been developed, while most studies focus on the Split-Window (SW) algorithms. According to the published works, some approximations and assumptions were used to develop SW algorithms. This paper investigated and revised the error caused by these approximations and assumptions with the help of TIGR 2000 database and MODTRAN 4.0 software. Then a new SW method to estimate LST from FY-3A/VIRR was proposed in this paper. The primarily accuracy evaluation of the proposed method shows that the root mean square error (RMSE) of LST estimation using TIGR atmospheric profiles is 0.768 K, with the bias of −0.122 K.

Soil moisture plays an important role in the process of energy exchange and water cycle. Soil moisture also provides critical information in agriculture, including crop growth and drought. In this study, the comparison between NASA AMSR-E soil moisture product and ground-based measurement are performed in terms of (1) measurement depths of soil moisture, and (2) satellite overpass times. The results show that the NASA AMSR-E soil moisture product can be used to monitor time-series variation in soil moisture. Compared to the AMSR-E product from descending overpasses, the AMSR-E product from ascending overpasses has better ability in monitoring soil moisture variation. Also the AMSR-E product has better ability in monitoring soil moisture at the depth of 0-10 cm than 10-20 cm.

Soil moisture is a key variable that influences the redistribution of the radiant energy and the runoff generation and percolation of water in soil. Knowledge of soil moisture temporal-spatial variations is important in a wide range of studies. This study aims to investigate the temporal-spatial variations of soil moisture using microwave polarization difference index (MPDI). The AMSR-E/Aqua Daily Global Quarter-Degree Gridded Brightness Temperature at 10.65 GHz channel was used to calculate the MPDI. In addition, the AMSR-E/Aqua Daily L3 Surface Soil Moisture was used in this study. The temporal and spatial patterns between the MPDI and soil moisture were analyzed. The results indicate that the temporal and spatial patterns of the MPDI are consistent with those of soil moisture. The MPDI reflects the temporal and spatial variations of soil moisture.

Land surface temperature (LST) is crucial for a wide range of applications such as meteorology, climatology, and hydrology. In this study, we develop a method to normalize the Terra-MODIS LST to the same local solar time. An empirical relationship is established to estimate the slope of LST versus local solar time from the MSG-SEVIRI brightness temperature at the top of the atmosphere during the period 10:00-12:00 and 21:00-23:00 local solar time. This relationship is then used to normalize the Terra-MODIS LST to the same local solar time. The results indicate that the spatial variations of the MODIS LST caused by different local solar time are removed after the temporal normalization. The temporal normalized LST may become more suitable for global climate studies.

This letter presents a method to calculate the width ω over the half-period of the cosine term in a diurnal temperature cycle (DTC) model. ω deduced from the thermal diffusion equation (TDE) is compared with ω obtained from solar geometry. The results demonstrate that ω deduced from the TDE describes the shape of the DTC model more adequately around sunrise and the time of maximum temperature than ω obtained from solar geometry. Additionally, taking into account the physical continuity of land surface temperature (LST) variation, a day-to-day temporal progression (DDTP) model of LST is developed to model several days of DTCs. The results indicate that the DDTP model fits in situ [or Spinning Enhanced Visible and Infrared Imager (SEVIRI)] LST well with a root-mean-square error (RMSE) less than 1 K. Compared with the DTC model, the DDTP model slightly increases the quality of LST fits around sunrise. Assuming that only six LST measurements corresponding to the NOAA/AVHRR and MODIS overpass times for each day are available, several days of DTCs can be predicted by the DDTP model with an RMSE less than 1.5 K.

MODIS land surface temperature (LST) products have been widely used in numerous applications. Each pixel within the MODIS LST products is acquired at different local solar time even though they are in the same granule. A temporal consistency and spatial comprehensiveness data set will benefit us in the utilization of the LST products in related applications and researches. In this study, a diurnal temperature cycle (DTC) model was employed to normalize the MODIS LSTs to the same local solar time. The MODIS LSTs were derived from the Terra/MODIS and Aqua/MODIS LST products (MOD11_L2 and MYD11_L2, respectively). The results at daytime only are presented because the larger LSTs heterogeneity makes the comparison of LSTs before and after the temporal normalization much clearer. The preliminary results indicate that the spatial variations of the MODIS LSTs caused by different local solar time are removed after the temporal normalization. The temporal normalized LSTs may become more suitable for the analysis of land surface processes.

HJ-1B satellite is one of the small satellites in the constellation for disaster prediction and monitoring which will be launched in 2008. The infrared sensor, which is one of the payloads of HJ-1B satellite, contains the MIR and TIR channels. The improved capabilities of HJ-1B data offer an opportunity for the computation of subpixel fire temperature and fire area. The simulated HJ-1B MIR and TIR channel images are used in this paper for the algorithm test. Fires with various sizes and temperatures are simulated in a wide range of terrestrial biomes and climates conditions by MODTRAN 4. A bispectral method developed by L. Giglio and J. D. Kendall is adopted to retrieve the temperature and area of a subpixel fire within an otherwise homogeneous pixel. It is evident that HJ-1B satellite data are more sensitive to the smaller and the cooler fires than that of MODIS or AVHRR Data. For the HJ-1B data, if the fire area is about 450m2and fire temperature is about 1000K, it can also offer a capability of retrieving the fire temperature and area in a relatively high accuracy. It has been demonstrated that the accuracy will increase with the growing fire area or temperature. By sensitivity analysis it has been found that the uncertainties of the retrieved fire temperature and area using HJ-1B data are about 10.0% and 30% at the given simulation condition.