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Particulate organic carbon (POC) plays a crucial role in the carbon cycle of inland lake ecosystems. The utilization of remote sensing satellite data provides an effective approach for monitoring the temporal and spatial variations in POC concentration within inland water. However, Erhai Lake, situated in the unique natural environment of the Yunnan-Kweichow Plateau, poses distinct challenges due to the complex and diverse origin of its water quality elements. Existing methods face difficulties in accurately detecting POC concentration in Erhai Lake. In this study, 112 water samples and 574 in-situ remote sensing reflectance curves were collected from Erhai Lake during April, May, and June 2023. The analysis of the optical characteristics of the water in Erhai Lake revealed the closest relationship between the concentration of total particulate matter and POC. Consequently, a POC concentration inversion algorithm was developed, utilizing bands 8, 12, and 16 of Sentinel-3 Ocean and Land Colour Instrument (OLCI). Various POC concentration inversion algorithms were evaluated utilizing a separate dataset. The results indicate that the three-band method offers superior accuracy in POC concentration inversion for Erhai Lake, with a root mean square error (RMSE) of 0.13 mg/L and a mean absolute percentage error (MAPE) of 28.80%. The three-band method was effectively applied to OLCI images from April, May, and June 2023, enabling the analysis of the spatiotemporal distribution of POC in Erhai Lake. [ABSTRACT FROM AUTHOR]

The lake water surface temperature (LWST) is a critical parameter influencing lake ecosystem dynamics and addressing challenges posed by climate change. Traditional point measurement techniques exhibit limitations in providing comprehensive LWST data. However, the emergence of satellite remote sensing and unmanned aerial vehicle (UAV) Thermal Infrared (TIR) technology has opened new possibilities. This study presents an approach for retrieving plateau lake LWST (p-LWST) from UAV TIR data. The UAV TIR dataset, obtained from the DJI Zenmuse H20T sensor, was stitched together to form an image of brightness temperature (BT). Atmospheric parameters for atmospheric correction were acquired by combining the UAV dataset with the ERA5 reanalysis data and MODTRAN5.2. Lake Water Surface Emissivity (LWSE) spectral curves were derived using 102 hand-portable FT-IR spectrometer (102F) measurements, along with the sensor's spectral response function, to obtain the corresponding LWSE. Using estimated atmospheric parameters, LWSE, and UAV BT, the un-calibrated LWST was calculated through the TIR radiative transfer model. To validate the LWST retrieval accuracy, the FLIR Infrared Thermal Imager T610 and the Fluke 51-II contact thermometer were utilized to estimate on-point LWST. This on-point data was employed for cross-calibration and verification. In the study area, the p-LWST method retrieved LWST ranging from 288 K to 295 K over Erhai Lake in the plateau region, with a final retrieval accuracy of 0.89 K. Results demonstrate that the proposed p-LWST method is effective for LWST retrieval, offering technical and theoretical support for monitoring climate change in plateau lakes. [ABSTRACT FROM AUTHOR]

Since forest fire is one of the most dangerous natural disasters and presents serious threats to the local ecological environment, economic growth, and public safety, it is essential to carry out accurate and real-time forest fire monitoring. In this study, a forest fire monitoring method that combines a threshold-based algorithm and random forest (RF) model using Himawari-8 data is proposed. The threshold-based algorithm employs the solar zenith angle to adaptively determine the potential fire point judgement threshold to extract possible fire points. The RF model constructed with spectral features and spatio-temporal information is subsequently utilized to eliminate pseudo-fire points from the results of the threshold-based algorithm. To eliminate fire points in non-forest areas, post-processing is performed using land cover data. Five fire occurrence moments in the research area are selected to verify the identification accuracy. The results reveal that the overall accuracy and the overall comprehensive evaluation values are 97.36% and 0.913, respectively, which demonstrates that the proposed method is capable of accurately identifying forest fire points and providing an effective means for forest fire monitoring. [ABSTRACT FROM AUTHOR]

Efficiently and accurately identifying the spatial distribution of tea plantations in the subtropical plateau regions of southwest China is of great significance for ecological and environmental protection. However, the lands of those regions are fragmented with complex vegetation types. Moreover, there is much cloudy and rainy weather over those areas, making it very difficult to identify tea plantations using only optical remote sensing data. In order to solve these problems, this paper uses Sentinel-1 (S1) Synthetic Aperture Radar (SAR) data and Sentinel-2 (S2) optical data to design seven classification feature combinations to explore the influence of red edge features, radar features and texture features on the identification accuracy of tea plantations. The feasibility of Jeffreys-Matusita distance (JM) feature selection and Recursive Feature Elimination (RFE) feature selection algorithm to find the optimal feature combination is verified, and the distribution of tea plantations in the study area is acquired by using the object-oriented random forest algorithm. The study shows that (1) the combination of SAR data and optical data can effectively improve the identification accuracy of tea plantations. (2) S2 red edge features and S1 radar features can significantly improve the accuracy of the identification results of tea plantations. (3) After applying the JM distance and RFE feature selection algorithms, the producer’s accuracy of tea plantations is improved by 1.39% and 2.38%, and the user’s accuracy is improved by 1.02% and 1.3%, respectively, compared with the identification of all features. The overall accuracy of the random forest algorithm combined with RFE is 93.43%. This study proposes the application of feature selection algorithms in identification of tea plantations, which improves accuracy and increases efficiency while minimizing redundant features and provides an effective approach to identify tea plantations in cloudy and rainy areas in the subtropical plateau of southern China. [ABSTRACT FROM AUTHOR]

Fengyun-4A (FY-4A) is the latest generation of China’s geostationary satellite. The Advanced Geosynchronous Radiation Imager (AGRI) onboard FY-4A can provide high-precision, high-frequency observation data, which makes a new possibility for estimating the downwelling surface longwave radiation (DSLR) with high spatial and temporal resolution. This work presents a new method for estimating DSLRs under all-sky conditions using a genetic algorithm–artificial neural network (GA-ANN) algorithm based on brightness temperature (BT) from the FY-4A AGRI infrared channels and near-surface air temperature and dew point temperature from ERA5 reanalysis data. Based on the verification results of two independent observation sites, it is shown that the bias and RMSE are - 4.31 W/m2 and 35.28 W/m2, respectively. Compared the CERES SYN all-sky DSLR product with the DSLR estimated by the new method, the bias and RMSE are 0.86 W/m2 and 26.87 W/m2, respectively, and the new method has a higher spatial resolution (4 km), which can display more details of spatial variation. [ABSTRACT FROM AUTHOR]

Due to the unique climatic characteristics and vegetation features of tropical regions, the correlation R2 of gross primary productivity (GPP) estimation in tropical regions using remote sensing models was generally lower than 0.3. Therefore, for the cloudy and rainy tropical regions, the influence brought by clouds on remote sensing images needed to be considered in GPP estimation. This paper developed a corrected vegetation photosynthesis model (VPM) for estimating GPP under cloudy conditions. It mainly corrected the two parameters, W scalar and Enhanced Vegetation Index (EVI), which were obtained from remote sensing images and were therefore greatly influenced by clouds in the model. First, the water stress factor W scalar was replaced by Evaporation Fraction (EF). Secondly, using the good correlation between near surface temperature and EVI, the conversion coefficient between near surface temperature and EVI was fitted to achieve the effective reconstruction of EVI contaminated by clouds. The correction of the two factors improved the estimation accuracy of the VPM model, and the comparison with the observed values of the GPP site in 4 years showed that the correction of EVI had a better improvement, with an increase of 0.22 in R2 compared with the pre-correction, and the correction of W scalar was increased by 0.11 in R2. To verify the proposed method, the in-situ observation data of Xishuangbanna flux site from 2007 to 2010 were used. The results showed that the proposed method effectively improved the accuracy of GPP estimation by VPM model, especially in 2007 it was strongly influenced by clouds, and the improvement was significant, with R2 increasing from 0.2 to 0.82. In general, the accuracy of GPP estimation by the proposed method had been significantly improved, with RMSE (gC·m−2·8 day−1) decreasing from 15,14.4, 18.1, 14.2 to 8.07, 6.56, 10.33, 11.44, respectively. Therefore, the proposed method can be used to estimate the GPP for tropical seasonal rain forests in Xishuangbanna. [ABSTRACT FROM AUTHOR]

The downward shortwave radiation (DSR) is a key input parameter for land surface models and climate models. Based on the daily averaged Global Land Surface Satellite downward shortwave radiation (GLASS-DSR) dataset over the Yunnan-Kweichow Plateau (YKP) from 1984 to 2018, this paper analyzes variation trend and breakpoints of DSR. The results show that: annual averaged DSR decreases at a decreasing rate of −1.84 W·m−2·decade−1 over the YKP from 1984 to 2018; the overall distribution of interannual averaged DSR shows higher in the mid-west, and gradually decreasing from west to northeast over the YKP; the estimated averaged DSR is larger in spring than in summer due to the influence of the monsoon; monthly averaged DSR reaches its maximum in May and its minimum in December; breakpoints are found in the seasonal and trend components of daily averaged DSR. Eleven driving factors are examined for their effects on DSR variation, including annual average temperature, precipitation, 10 m wind speed, aerosol optical thickness (AOT), total cloud cover, elevation, slope, aspect, longitude, latitude, and climate zones. According to the findings, AOT predominates in the spatio-temporal distribution of DSR over the YKP. This study will contribute to studies related to climate change and highland radiation. [ABSTRACT FROM AUTHOR]

Affected by geographical location and climatic conditions, crop classification in the Yunnan Plateau of China is greatly restricted by the low utilization rate of annual optical data, complex crop planting structure, and broken cultivated land. This paper combines monthly Sentinel-2 optical remote sensing data with Sentinel-1 radar data to minimize cloud interference to conduct crop classification for plateau areas. However, pixel classification will inevitably produce a "different spectrum of the same object, foreign objects in the same spectrum". A principal component feature synthesis method is developed for multi-source remote sensing data (PCA-MR) to improve classification accuracy. In order to compare and analyze the classification effect of PCA-MR combined with multi-source remote sensing data, we constructed 11 classification scenarios using the Google Earth Engine platform and random forest algorithm (RF). The results show that: (1) the classification accuracy is 79.98% by using Sentinel-1 data and 91.18% when using Sentinel-2 data. When integrating Sentinel-1 and Sentinel-2 data, the accuracy is 92.31%. By analyzing the influence of texture features on classification under different feature combinations, it was found that optical texture features affected the recognition accuracy of rice to a lesser extent. (2) The errors will be reduced if the PCA-MR feature is involved in the classification, and the classification accuracy and Kappa coefficient are improved to 93.47% and 0.92, respectively. [ABSTRACT FROM AUTHOR]

The quantitative retrieval of the chlorophyll-a concentration is an important remote sensing method that is used to monitor the nutritional status of water bodies. The high spatial resolution of the Sentinel-2 MSI and its subdivision in the red-edge band highlight the characteristics of water chlorophyll-a, which is an important detection tool for assessing water quality parameters in plateau lakes. In this study, the Nine Plateau Lakes in the Yunnan-Kweichow Plateau of China were selected as the study area. Using Sentinel-2 MSI transit images and in situ measured chlorophyll-a concentration as the data source, the chlorophyll-a concentrations of plateau lakes (CCAPLs) were investigated, and the surface temperatures of plateau lakes (STPLs) were retrieved to verify the hypothesis that the lake surface temperature could increase the chlorophyll-a concentration. By comparing feature importance using a random forest (RF), the Sentinel-2 MSI surface reflectance and in situ data were linearly fitted using four retrieval spectral indices with high feature importance, and the accuracy of the estimated concentration of chlorophyll-a was evaluated by monitoring station data in the same period. Then, Landsat-8 TIRS Band 10 data were used to retrieve the STPL with a single-channel temperature retrieval algorithm and to verify the correlation between the STPL and the CCAPL. The results showed that the retrievals of the CCAPL and the STPL were consistent with the actual situation. The root-mean-square error (RMSE) of the fifteenth normalized difference chlorophyll-a index (NDCI15) was 0.0249. When the CCAPL was greater than 0.05 mg/L and the STPL was within 28–34 °C, there was a positive linear correlation between the CCAPL and the STPL. These results will provide support for the remote sensing monitoring of eutrophication in plateau lakes and will contribute to the scientific and effective management of plateau lakes. [ABSTRACT FROM AUTHOR]

Surface upward longwave radiation (SULR) is a key parameter that regulates surface radiation budget balance and matter–energy exchange. However, the state-of-the-art SULR retrieval methods based on remotely sensed data are only effective under clear skies, which mean that the existing methods are unable to generate spatiotemporal continuous SULR product at regional or global scale. Herein, taking the advantage of long-pending abundant ground-based radiation observations, satellite products, and meteorological reanalysis data, a data-driven random forest (RF) method is proposed to retrieve the instantaneous SULR under all-sky conditions. Based on spectral samples of different surface types and simulation results from the moderate resolution atmospheric transmission (MODTRAN), spectral transformation is carried out to transform SULR of various measured domains into the defined 4–100 $\mu \text{m}$ domain at first. SULR and surface downward shortwave radiation (SDSR) observations from seven stations of the surface radiation budget network (SURFRAD) and nine stations of the baseline surface radiation network (BSRN) are used in model’s training and testing procedures, and the RF model achieves a high accuracy with the root-mean-square error (RMSE) of 10.45 W/ $\text{m}^{2}$ on test set. In model evaluation, ground measurements from 14 stations of FLUXNET have been used, and the overall RMSE is 18.40 W/ $\text{m}^{2}$. In the actual application process, SDSR is estimated by remotely sensed data of Meteosat Second Generation (MSG). The accuracy of RF model has been validated with the observations from five stations of BSRN in 2021, and RMSEs are 17.00, 10.94, 12.17, 27.89, and 12.54 W/ $\text{m}^{2}$ , respectively. Validation result shows that the data-driven method is capable of estimating SULR under all-sky conditions with a high accuracy. Finally, sensitivity analysis has been carried out, and the established RF model keeps robust even though there are great uncertainties among input parameters. [ABSTRACT FROM AUTHOR]

This work proposes a new method for estimating downwelling surface longwave radiation (DSLR) under cloudy-sky conditions based on a parameterization method and a genetic algorithm–artificial neural network (GA-ANN) algorithm. The new method establishes a GA-ANN model based on simulated data, and then combines MODIS satellite data and ERA5 reanalysis data to estimate the DSLR. According to the validation results of the field sites, the bias and RMSE are –9.18 and 34.88 W/m2, respectively. Compared with the existing research, the new method can achieve reasonable accuracy. Parameter analysis using independently simulated data shows that the near-surface air temperature ( T a ) and cloud base height (CBH) have an important influence on DSLR estimation under cloudy-sky conditions. With an increase in CBH, DSLR gradually decreases; however, with an increase in T a , DSLR shows a trend of gradual increase. When estimating DSLR under cloudy-sky conditions, under the influence of clouds, except for cirrus, the change in DSLRs with CBH and T a is greater than 20 W/m2. [ABSTRACT FROM AUTHOR]

Land surface temperature (LST) is a key parameter in global ecological and climate system, while its accurate estimation under cloudy-sky conditions remains a challenge. In this paper, a practical two-step framework has been proposed for all-sky LST estimation, which effectively combined physical mechanisms and machine learning algorithms. The first step was the estimation of hypothetical clear-sky LST. The Bayesian optimization-based Extreme Gradient Boosting (BO-XGB) model was developed to establish the implicit and complex relationship between hypothetical clear-sky LST and corresponding independent variables, including hypothetical clear-sky radiation energy input, surface biophysical parameters, atmospheric conditions, as well as spatial and temporal features. The second step was the cloud radiative forcing effect (CRFE) correction for hypothetical clear-sky LST under cloudy-sky conditions. Based on the surface energy balance (SEB) equation and conventional force-restore method, the analytical expressions of CRFE correction terms were derived, which speeded up the calculation and facilitated error analysis. Based on several remote sensing products, including Advanced Baseline Imager (ABI) LST and auxiliary data from Moderate Resolution Imaging Spectroradiometer (MODIS) and Global LAnd Surface Satellite (GLASS), as well as ERA5 reanalysis data, the proposed framework was applied to recovered spatially continuous hourly all-sky LST information over the contiguous US (CONUS) in 2018. The accuracy of the proposed method has been validated by ground measurements from 60 representative sites operated by the Surface Radiation Budget (SURFRAD) and AmeriFlux. For clear-sky conditions, the overall root mean square error (RMSE) was 2.67 K, with a bias of −0.47 K and R2 of 0.96. For cloudy-sky conditions, the corresponding accuracy metrics were 2.60 K, −0.49 K, and 0.96, respectively, and RMSEs for all sites were below 4 K, which indicated the stability of the proposed method. Besides, daily average LST has also been calculated based on the estimated hourly LSTs with RMSE of 1.68 K and bias of −0.48 K. • A new and fast hypothetical clear-sky LST estimation model is established. • Analytic expression of LST correct term caused by cloud radiation forcing is given. • The proposed framework combines physical mechanisms and machine learning model. • Accuracy metrics for predictions under clear and cloudy skies are quite similar. [ABSTRACT FROM AUTHOR]

Net surface shortwave radiation (NSSR) is a key parameter that drives the surface material exchange and energy balance. Herein, we propose an improved artificial neuron network (ANN) with parameterized (ANN-P) method to first calculate the albedo at the top of atmosphere (TOA) by considering the surface non-Lambertian effect. Subsequently, the NSSR is estimated based on the relationship between TOA broadband albedo and the Earth’s surface-absorbed shortwave radiation using a parameterized method under clear sky. The modeling process is implemented with Chinese Gaofen-5 (GF-5) visible/near-infrared channels data simulated via MODTRAN. For comparison, a previously reported lookup table (LUT) with parameterized (LUT-P) method and an ANN method are also employed. The performances of all these methods are evaluated. In terms of model simulation part, the root-mean-square errors (RMSEs) are 15.01 (17.07), 10.04 (13.67), and 20.39 (29.99) W/m2 for land, water, and snow/ice surfaces, respectively, for the ANN-P (versus LUT-P) method. Their mean bias errors (MBEs) are within 0.9 W/m2. With respect to the direct ANN method, it shows the highest accuracy yet relatively large deviation for water surface. Additionally, the sensitivity analysis of water vapor content (WVC) confirms that the ANN-P method is more stable than the LUT-P and ANN methods and is, thereby, recommended for clear-sky NSSR estimation. Finally, the ground validations indicate that the mean RMSEs (MBEs) for the LUT-P, ANN-P, and ANN methods are 49.33 (−3.01), 47.55 (1.75), and 104.24 (−75.72) W/m2, respectively. [ABSTRACT FROM AUTHOR]

Cloud base height (CBH) is a key parameter to characterize the cloud radiation effect. However, the CBH used in downwelling surface longwave radiation (DSLR) estimation is generally obtained indirectly through cloud top parameters retrieved by passive optical remote sensing instruments, which is of high uncertainty. At the same time, it is unreasonable to replace the effective radiation of the entire cloud layers by only using the cloud base radiation with single layer cloud model. This study proposes a new method to estimate cloudy-sky DSLR, which considers the radiation effect of the entire cloud layers from the cloud base to top. First, the CBH estimation model is established by the genetic algorithm-artificial neural network (GA-ANN) algorithm. The cloud top height and cloud attribute parameters (cloud optical depth, cloud water path, and cloud phase) from the passive remote sensing are used as the input features, and meanwhile CBH data from the active remote sensing are output features in the training and testing process of the model. Then, the cloud base temperature (CBT) is estimated based on the CBH combined with the temperature profile data in the EAR5 reanalysis data. Finally, the effective temperature of the entire cloud layers is calculated by using CBT and cloud top temperature. The verification results of CBH estimation showed that R2 is 0.83, the bias and root mean square error (RMSE) are 0.02 km and 1.56 km, respectively, which indicates a comparable accuracy and higher stability compared with the previous studies. The ground-based measurements in the SURFRAD network are used to validate the newly proposed DSLR estimation method, and the results showed that the bias and RMSE are 5.27 W/m2 and 28.48 W/m2, respectively. Additionally, this study found that although the effective temperature of the entire cloud layers has a weaker linear correlation with DSLR, the radiation contribution generated by cloud still occupies a certain weight, and the maximum ratio of cloud radiation in DSLR estimation can account for 30%. Therefore, the cloud radiation effect must be taken into account in the estimation of cloudy-sky DSLR. • A new CBH estimation model is established by combining passive with active remote sensing data. • The cloudy-sky DSLR is estimated by considering the radiation effect of the entire cloud layers from the cloud base to top. • The radiation contribution generated by cloud in DSLR estimation can up to 30%. [ABSTRACT FROM AUTHOR]

Dynamic monitoring of cropland using high spatial resolution remote sensing images is a powerful means to protect cropland resources. However, when a change detection method based on a convolutional neural network employs a large number of convolution and pooling operations to mine the deep features of cropland, the accumulation of irrelevant features and the loss of key features will lead to poor detection results. To effectively solve this problem, a novel cropland change detection network (CroplandCDNet) is proposed in this paper; this network combines an adaptive receptive field and multiscale feature transmission fusion to achieve accurate detection of cropland change information. CroplandCDNet first effectively extracts the multiscale features of cropland from bitemporal remote sensing images through the feature extraction module and subsequently embeds the receptive field adaptive SK attention (SKA) module to emphasize cropland change. Moreover, the SKA module effectively uses spatial context information for the dynamic adjustment of the convolution kernel size of cropland features at different scales. Finally, multiscale features and difference features are transmitted and fused layer by layer to obtain the content of cropland change. In the experiments, the proposed method is compared with six advanced change detection methods using the cropland change detection dataset (CLCD). The experimental results show that CroplandCDNet achieves the best F1 and OA at 76.04% and 94.47%, respectively. Its precision and recall are second best of all models at 76.46% and 75.63%, respectively. Moreover, a generalization experiment was carried out using the Jilin-1 dataset, which effectively verified the reliability of CroplandCDNet in cropland change detection. [ABSTRACT FROM AUTHOR]

Different from bidirectional reflectance, temperature variation takes some time with the change of illumination. However, previous thermal radiation directionality (TRD) models have less considered the influence of this temperature inertia (TI) effect. By using the concept of conversion component, this article proposed an improved geometric optical (GO) model, called MGP_TI model. This model considers the TI effect by further dividing the background component into the continuously sunlit, continuously shaded, converted from sunlit to shaded, and converted from shaded to sunlit backgrounds. Upon combining with in situ measurements and a comprehensive simulated data set of component temperatures and prescribing three levels of TI and six observation times, the TI influence on TRD modeling was comprehensively analyzed. Results indicated that: 1) the overall absolute and relative greatest influence were 0.34 °C and 6.9%, respectively, suggesting that the TI influence on the value of TRD was less significant compared with the land surface temperature (LST) retrieval accuracy and the TRD extent; 2) the TI would weaken TRD on the direction of sun motion, whereas it enhanced the TRD on the opposition direction, and the primary influence was enhancing first and then weakening during the period from 10:30 to 15:30, which were determined by the differences in conversion component fractions; and 3) the TI effect could also result in the delay of the hotspot, and the occurrence and degree of the delay were influenced by the TI strength, local solar time and temperature differences of sunlit/shaded components. [ABSTRACT FROM AUTHOR]

This paper proposes a different thermal channel combination split-window (DTCC-SW) method to estimate the land surface temperature (LST) and sea ST (SST) from the Chinese Gaofen-5 (GF-5) satellite thermal infrared (TIR) data. A nonlinear combination of two adjacent channels CH8.20(centered at $8.20~\mu \text{m}$) and CH8.63 (centered at $8.63~\mu \text{m}$) was proposed to estimate LST for low-emissivity surfaces. A nonlinear combination of two adjacent channels, CH10.80 (centered at $10.80~\mu \text{m}$) and CH11.95 (centered at $11.92~\mu \text{m}$), was developed to estimate LST and SST for high-emissivity surfaces under dry atmospheric conditions, and a nonlinear combination of two channels, CH8.63 and CH11.95, was used to estimate LST and SST for high-emissivity surfaces under wet atmospheric conditions. The numerical values of the DTCC-SW coefficients were obtained using a statistical regression method from synthetic data simulated with an accurate atmospheric radiative transfer model moderate spectral resolution atmospheric transmittance mode 5 over a wide range of atmospheric and surface conditions. The LST (SST), mean emissivity, and atmospheric water vapor content were divided into several tractable subranges to improve the fitting accuracy. The experimental results and the preliminary evaluation results showed that the root-mean-square error between the actual and estimated LSTs (SSTs) is less than 0.7 K (0.3 K), provided that the land surface emissivities are known, which indicates that the proposed DTCC-SW method can accurately estimate the LST and SST from the GF-5 TIR data. [ABSTRACT FROM AUTHOR]

An appropriate model to correct thermal radiation anisotropy is important for the wide applications of land surface temperature (LST). This paper evaluated the performance of three published directional thermal radiationmodels-the Roujean-Lagouarde (RL)model, the Bidirectional Reflectance Distribution Function (BRDF) model, and the Vinnikov model-at canopy and pixel scale using simulation, airborne, and satellite data. The results at canopy scale showed that (1) the three models could describe directional anisotropy well and the Vinnikov model performed the best, especially for erectophile canopy or low leaf area index (LAI); (2) the three models reached the highest fitting accuracy when the LAI varied from 1 to 2; and (3) the capabilities of the three models were all restricted by the hotspot effect, plant height, plant spacing, and three-dimensional structure. The analysis at pixel scale indicated a consistent result that the three models presented a stable effect both on verification and validation, but the Vinnikov model had the best ability in the erectophile canopy (savannas and grassland) and low LAI (barren or sparsely vegetated) areas. Therefore, the Vinnikov model was calibrated for different land cover types to instruct the angular correction of LST. Validation with the Surface Radiation Budget Network (SURFRAD)-measured LST demonstrated that the root mean square (RMSE) of the Moderate Resolution Imaging Spectroradiometer (MODIS) LST product could be decreased by 0.89 K after angular correction. In addition, the corrected LST showed better spatial uniformity and higher angular correlation. [ABSTRACT FROM AUTHOR]

As well as very high resolution (VHR) remote sensing technology and deep learning, methods for detecting changes in buildings have made great progress. Despite this, there are still some problems with the incomplete detection of change regions and rough edges. To this end, a change detection network for building VHR remote sensing images based on Siamese EfficientNet B4-MANet (Siam-EMNet) is proposed. First, a bi-branches pretrained EfficientNet B4 encoder structure is constructed to enhance the performance of feature extraction and the rich shallow and deep information is obtained; then, the semantic information of the building is input into the MANet decoder integrated by the dual attention mechanism through the skip connection. The position-wise attention block (PAB) and multi-scale fusion attention block (MFAB) capture spatial relationships between pixels in the global view and channel relationships between layers. The integration of dual attention mechanisms ensures that the building contour is fully detected. The proposed method was evaluated on the LEVIR-CD dataset, and its precision, recall, accuracy, and F1-score were 92.00%, 88.51%, 95.71%, and 90.21%, respectively, which represented the best overall performance compared to the BIT, CDNet, DSIFN, L-Unet, P2V-CD, and SNUNet methods. Verification of the efficacy of the suggested approach was then conducted. [ABSTRACT FROM AUTHOR]

The midinfrared (MIR) spectral region (3–5 \mu\textm), which penetrates most haze layers in the atmosphere and is less sensitive to variations in atmospheric water vapor, seems to be appropriate for retrieving land surface temperature (LST). However, there are currently few studies of LST retrieval with MIR data because it is difficult to eliminate solar irradiance from the total energy measured in the MIR during the daytime. This paper proposes a physics-based method to retrieve LST from MODIS daytime MIR data. The bidirectional reflectivity describing the reflected solar direct irradiance is determined using the method by Tang and Li. The directional emissivity, representing the surface emitted radiance, is determined by a kernel-driven bidirectional reflectance distribution function model, i.e., RossThick-LiSparse-R. Intercomparisons using the MODIS-derived LST product MYD11_L2, for the Baotou experimental site in Urad Qianqi, Inner Mongolia, China, have a maximum root-mean-square error (RMSE) of 1.69 K and a minimum RMSE of 1.31 K, for four scenes of MODIS images. Furthermore, in situ LSTs measured at the Hailar field site in northeastern Inner Mongolia, China, were also used to validate the proposed method. Comparisons of the LSTs retrieved from MODIS daytime MIR data and those calculated using in situ measurements have a bias and RMSE of −0.17 K and 1.42 K, respectively, which indicates that the proposed method can accurately retrieve LST from MODIS daytime MIR data. [ABSTRACT FROM PUBLISHER]

A series of empirical analytical tools have been adopted to investigate the driving mechanisms of surface urban heat islands (SUHI) on a global scale, among which spatial heterogeneity is yet to be fully elucidated. In this study, we investigated the spatial non-stationarity of the driving factors concerning surface properties, climate conditions, and urbanization processes for global long-term SUHI. First, the potential impact on SUHI was explored using global ordinary least squares regression. Geographically weighted regression (GWR) and multi-scale GWR (MGWR) from local perspectives were employed for comparison. The results show that the MGWR has the highest goodness of fit at 0.87, 0.73, 0.90, 0.74, 0.85, and 0.76 for annual day/night (AD/AN), summer day/night (SD/SN), and winter day/night (WD/WN) scales, respectively. Although both global and local schemes exhibit similar influencing magnitudes and signs on the SUHI, the MGWR is better at capturing spatial non-stationarity. Globally, for AD, AN, SD, SN, WD, and WN, the coefficients of the urban-rural vegetation index difference (ΔEVI) and surface albedo difference (ΔWSA), urban mean precipitation (MAP), wind speed (WS), population density (PD), and urban area (UA) are −0.50, +0.30, +0.16, +1.31, −0.03, and +0.03, respectively, at daytime, and −0.38, −0.33, −0.39, −0.10, +0.18, and +0.08, respectively, at night-time. Given the spatial heterogeneity of multiple factors, ΔEVI exhibits a strong mitigation effect on the SD SUHI especially in arid zones. The negative influence of ΔWSA on night-time SUHI demonstrates a strong latitudinal disparity and greater sensitivity in the equatorial zone. The positive correlations between MAP and AD/SD SUHIs have evident latitudinal and longitudinal variations. The mitigation effect of WS displayed distinct coastal amplification, especially in WD. In contrast, the PD and UA presented prominent positive impacts on night-time SUHI with less seasonal contrast. [ABSTRACT FROM AUTHOR]

The Open-Source Digital Elevation Model (DEM) is fundamental data of the geoscientific community. However, the variation of its accuracy with land cover type and topography has not been thoroughly studied. This study evaluates the accuracy of five globally covered and open-accessed DEM products (TanDEM-X90 m, SRTEM, NASADEM, ASTER GDEM, and AW3D30) in the mountain area using ICESat/GLAS data as the GCPs. The robust evaluation indicators were utilized to compare the five DEMs' accuracy and explore the relationship between these errors and slope, aspect, landcover types, and vegetation coverage, thereby revealing the consistency differences in DEM quality under different geographical feature conditions. The Taguchi method is introduced to quantify the impact of these surface characteristics on DEM errors. The results show that the slope is the main factor affecting the accuracy of DEM products, accounting for about 90%, 81%, 85%, 83%, and 65% for TanDEM-X90, SRTM, NASADEM, ASTER GDEM, and AW3D30, respectively. TanDEM-X90 has the highest accuracy in very flat areas (slope < 2°), NASADEM and SRTM have the greatest accuracy in flat areas (2 ≤ slope < 5°), while AW3D30 accuracy is the best in other cases and shows the best consistency on slopes. This study makes a new attempt to quantify the factors affecting the accuracy of DEM, and the results can guide the selection of open-source DEMs in related geoscience research. [ABSTRACT FROM AUTHOR]

Thin cirrus clouds are dominated by non-spherical ice crystals with an effective emissivity of less than 0.5. Until now, the influences of clouds were not commonly considered in the development of algorithms for retrieving land-surface temperature (LST). However, numerical simulations showed that the influence of thin cirrus clouds could lead to a maximum LST retrieval error of more than 14 K at night if the cirrus optical depth (COD) at 12 μm was equal to 0.7 (cirrus emissivity equivalent to 0.5). To obtain an accurate estimate of the LST under thin cirrus using satellite infrared data, a nonlinear three-channel LST retrieval algorithm was proposed based on a widely used two-channel algorithm for clear-sky conditions. The variations in the cloud top height, COD, and effective radius of cirrus clouds were considered in this three-channel LST retrieval algorithm. Using Moderate Resolution Imaging Spectroradiometer (MODIS) channels 20, 31, and 32 (centred at 3.8, 11.0, and 12.0 μm, respectively) and the corresponding land surface emissivities (LSEs), the simulated data showed that this algorithm could obtain LSTs with root mean square errors (RMSEs) of less than 2.8 K when the COD at 12 μm is less than 0.7 and the viewing zenith angle (VZA) is less than 60°. In addition, a sensitivity analysis of the proposed algorithm showed that the total LST errors, including errors from the uncertainties in input parameters and algorithm error, were nearly the same as the algorithm error itself. Some lake surface water temperatures measured in Lake Superior and Lake Erie were used to test the performance of the proposed LST retrieval algorithm. The results showed that the proposed nonlinear three-channel algorithm could be used for estimating LST under thin cirrus with an RMSE of less than 2.8 K. [ABSTRACT FROM PUBLISHER]

The different imaging conditions of high spatial resolution remote sensing images (HSRRSIs) tend to cause large differences in the background information of bridges from the images, including problems of difficult detection of multiscale bridges, leakage of small bridges and insufficient detection accuracy for their detection. To address these problems, a YOLOv5 network with a decoupled head for the automatic detection of bridges in HSRRIs is proposed in this paper. First, the problem of inconsistent scale of information fusion of each feature in the feature pyramid network is solved using a weighted bi-directional feature pyramid network (BiFPN). Then, the convolutional block attention module (CBAM) is fused into the three effective feature layers after feature pyramid network processing. The bridge feature information is effectively extracted from the channel and spatial dimensions. Next, the decoupled head is fused in the YOLO Head to separate the classifier and regressor to speed up the network convergence and improve the network detection accuracy simultaneously. Finally, the practical effect is evaluated by calculating the average precision (AP). According to the experimental results, the AP of the proposed method is 98.1%, which is improved by 4.1%∼23.5% compared with other models. [ABSTRACT FROM AUTHOR]

This work addresses the estimation of net surface longwave radiation (NSLR) from net surface shortwave radiation (NSSR) by analysing the Surface Radiation Budget Network (SURFRAD) radiation data under cloudy conditions. A general model is developed to estimate NSLR from the NSSR for cloudy skies with a root mean square error (RMSE) of 23.16 W m−2compared within situdata. The model is applied to AmeriFlux data. The results show that the mean error and RMSE are –2.31 W m−2and 29.25 W m−2, respectively, compared with the measurement of AmeriFlux. To examine the significance of the influence of seasons on the estimated NSLR, the model is proposed as a function of seasonal variation. The results show a slight improvement for winter and spring, whereas a larger error is found for autumn compared with the results obtained by the general model. The influences of land cover and elevation on the model are also investigated. The results show that the model is slightly sensitive to the normalized difference vegetation index (NDVI) and the elevation. The RMSE of the model decreases from 23.16 W m−2to 21.04 W m−2when the NDVI and the elevation are considered in the model. [ABSTRACT FROM AUTHOR]

The urban–rural temperature difference is widely used in measuring surface urban heat island intensity (SUHII), where the accurate determination of rural background is crucial. However, traditionally, the entire permeable rural surface has been selected to represent the background temperature, leaving uncertainty about the impact of non-uniform rural surfaces with multiple land covers on the accuracy of SUHII quantification. In this study, we proposed two quantifications of SUHII derived from the primary (SUHII1) and secondary (SUHII2) land types, respectively, which successively occupy over 40–50% of whole rural regions. The spatial integration and temporal variation of SUHII1 and SUHII2 were compared with the result from whole rural regions (SUHII) within 34 urban agglomerations (UAs) in China. The results showed that the SUHII1 and SUHII2 differed slightly with SUHII, and the correlation coefficients of SUHII and SUHII1/SUHII2 are generally above 0.9 in most (32) UAs. Regarding the long-term SUHII between 2003 and 2019, the three methods demonstrated similar seasonal patterns, although SUHII1 (or SUHII2) tended to overestimate or underestimate compared to SUHII. As for the multi-year integration at the regional scale, the day–night cycle and monthly variations of SUHII1 and SUHII were found to be identical for each geographical division separately, indicating that the spatiotemporal pattern revealed by SUHII is minimally affected by the diversity of rural landcover types. The findings confirmed the viability of the urban–rural LST difference method for measuring long-term regional SUHII patterns under non-uniform rural land cover types. [ABSTRACT FROM AUTHOR]

This paper aims to determine land surface temperature (LST) using data from a spinning enhanced visible and infrared imager (SEVIRI) on board Meteosat Second Generation 2 (MSG-2) by using the generalized split-window (GSW) algorithm. Coefficients in the GSW algorithm are pre-determined for several overlapping sub-ranges of the LST, land surface emissivity (LSE), and atmospheric water vapour content (WVC) using the data simulated with the atmospheric radiative transfer model MODTRAN 4.0 under various surface and atmospheric conditions for 11 view zenith angles (VZAs) ranging from 0° to 67°. The results show that the root mean square error (RMSE) varies with VZA and atmospheric WVC and that the RMSEs are within 1.0 K for the sub-ranges in which the VZA is less than 30° and the atmospheric WVC is less than 4.25 g cm−2. A sensitivity analysis of LSE uncertainty, atmospheric WVC uncertainty, and instrumental noise (NEΔT) is also performed, and the results demonstrate that LSE uncertainty can result in a larger LST error than other uncertainties and that the total error for the LST is approximately 1.21 and 1.45 K for dry atmosphere and 0.86 and 2.91 K for wet atmosphere at VZA = 0° and at VZA = 67°, respectively, if the uncertainty in the LSE is 1% and that in the WVC is 20%. The GSW algorithm is then applied to the MSG-2 – SEVIRI data with the LSE determined using the temperature-independent spectral indices method and the WVC either determined using the measurements in two split-window channels or interpolated temporally and spatially using European Centre for Medium Range Weather Forecasting (ECMWF) data. Finally, the SEVIRI LST derived in this paper (SEVIRI LST1) is evaluated through comparisons with the SEVIRI LST provided by the land surface analysis satellite applications facility (LSA SAF) (SEVIRI LST2) and the Moderate Resolution Imaging Spectroradiometer (MODIS) LST product (MOD11B1 LST product). The results show that more than 80% of the differences between SEVIRI LST1 and SEVIRI LST2 are within 2 K, and approximately 70% of the differences between SEVIRI LST1 and MODIS LST are within 4 K. Furthermore, compared to MODIS LST, for four specific areas with different land surfaces, our GSW algorithm overestimates the LST by up to 1.0 K for vegetated surfaces and by 1.3 K for bare soil. [ABSTRACT FROM PUBLISHER]

This article addresses the major scaling problems in leaf area index (LAI) retrieval for a heterogeneous surface associated with (1) the nonlinearity in the relationships between remotely sensed reflectances and LAI products, (2) the discontinuity caused by the mixture of contrasting cover types that is categorized as the dominating type within a large-scale pixel, and (3) the algorithm for the dominant cover type being used for the retrieval of the LAI in that large-scale mixed pixel. Through mathematical analysis, two scaling models (a component-based model and a pixel-based model) are proposed on the basis of the Taylor series expansion with the corresponding textural and contextural parameters (i.e. variance–covariance matrices and component fractions) to correct for the scaling effects among LAI products at different scales. These models express the magnitude of the scaling effects for the nonlinear and discontinuous situations as a function of (1) the degree of nonlinearity quantified by the second derivative of the retrieval function, (2) the spatial heterogeneity quantified by variance–covariance matrices, and (3) the component fractions in the large-scale mixed pixel. To evaluate the proposed scaling models, a scaling correction test is performed and analysed on a SPOT (Système Pour l'Observation de la Terre) image for two vegetation types. The component fractions have proven to be the main reason for the scaling effects in a mixed pixel. Compared to the results before scaling, using either of the two proposed models greatly reduces the retrieval errors that the scaling effects cause. The relative scaling effects of the LAI may be up to 55% in an uncorrected, large-scale mixed pixel. However, the relative scaling errors can be as low as 2% with the intra-component textural parameters and about 13% with the intra-pixel textural parameters. Because the scaling effects can be corrected for the spatial heterogeneity caused either by density changes within the same cover or by cover type changes, our work indicates that the proposed scaling models are promising and feasible. [ABSTRACT FROM PUBLISHER]

Abstract: Land surface temperature (LST) is one of the key parameters in the physics of land surface processes from local through global scales. The importance of LST is being increasingly recognized and there is a strong interest in developing methodologies to measure LST from space. However, retrieving LST is still a challenging task since the LST retrieval problem is ill-posed. This paper reviews the current status of selected remote sensing algorithms for estimating LST from thermal infrared (TIR) data. A brief theoretical background of the subject is presented along with a survey of the algorithms employed for obtaining LST from space-based TIR measurements. The discussion focuses on TIR data acquired from polar-orbiting satellites because of their widespread use, global applicability and higher spatial resolution compared to geostationary satellites. The theoretical framework and methodologies used to derive the LST from the data are reviewed followed by the methodologies for validating satellite-derived LST. Directions for future research to improve the accuracy of satellite-derived LST are then suggested. [Copyright &y& Elsevier]

This paper addresses a snow-mapping algorithm for the Tibetan Plateau region using Moderate Resolution Imaging Spectroradiometer (MODIS) data. Accounting for the effects of the atmosphere and terrain on the satellite observations at the top of the atmosphere (TOA), particularly in the rugged Tibetan Plateau region, the surface reflectance is retrieved from the TOA reflectance after atmospheric and topographic corrections. To reduce the effect of the misclassification of snow and cloud cover, a normalized difference cloud index (NDCI) model is proposed to discriminate snow/cloud pixels, separate from the MODIS cloud mask product MOD35. The MODIS land surface temperature (LST) product MOD11_L2 is also used to ensure better accuracy of the snow cover classification. Comparisons of the resulting snow cover with those estimated from high spatial-resolution Landsat ETM+ data and obtained from MODIS snow cover product MOD10_L2 for the Mount Everest region for different seasons in 2002, show that the MODIS snow cover product MOD10_L2 overestimates the snow cover with relative error ranging from 20.1% to 55.7%, whereas the proposed algorithm estimates the snow cover more accurately with relative error varying from 0.3% to 9.8%. Comparisons of the snow cover estimated with the proposed algorithm and those obtained from MOD10_L2 product with in situ measurements over the Hindu Kush-Himalayan (HKH) region for December 2003 and January 2004 (the snowy seasons) indicate that the proposed algorithm can map the snow cover more accurately with greater than 90% agreement. [ABSTRACT FROM AUTHOR]

Road traffic elements are important components of roads and the main elements of structuring basic traffic geographic information databases. However, the following problems still exist in the detection and recognition of road traffic elements: dense elements, poor detection effect of multi-scale objects, and small objects being easily affected by occlusion factors. Therefore, an adaptive spatial feature fusion (ASFF) YOLOv5 network (ASFF-YOLOv5) was proposed for the automatic recognition and detection of multiple multiscale road traffic elements. First, the K-means++ algorithm was used to make clustering statistics on the range of multiscale road traffic elements, and the size of the candidate box suitable for the dataset was obtained. Then, a spatial pyramid pooling fast (SPPF) structure was used to improve the classification accuracy and speed while achieving richer feature information extraction. An ASFF strategy based on a receptive field block (RFB) was proposed to improve the feature scale invariance and enhance the detection effect of small objects. Finally, the experimental effect was evaluated by calculating the mean average precision (mAP). Experimental results showed that the mAP value of the proposed method was 93.1%, which is 19.2% higher than that of the original YOLOv5 model. [ABSTRACT FROM AUTHOR]

This paper proposes a superpixel spatial intuitionistic fuzzy C-means (SSIFCM) clustering algorithm to address the problems of misclassification, salt and pepper noise, and classification uncertainty arising in the pixel-level unsupervised classification of high spatial resolution remote sensing (HSRRS) images. To reduce information redundancy and ensure noise immunity and image detail preservation, we first use a superpixel segmentation to obtain the local spatial information of the HSRRS image. Secondly, based on the bias-corrected fuzzy C-means (BCFCM) clustering algorithm, the superpixel spatial intuitionistic fuzzy membership matrix is constructed by counting an intuitionistic fuzzy set and spatial function. Finally, to minimize the classification uncertainty, the local relation between adjacent superpixels is used to obtain the classification results according to the spectral features of superpixels. Four HSRRS images of different scenes in the aerial image dataset (AID) are selected to analyze the classification performance, and fifteen main existing unsupervised classification algorithms are used to make inter-comparisons with the proposed SSIFCM algorithm. The results show that the overall accuracy and Kappa coefficients obtained by the proposed SSIFCM algorithm are the best within the inter-comparison of fifteen algorithms, which indicates that the SSIFCM algorithm can effectively improve the classification accuracy of HSRRS image. [ABSTRACT FROM AUTHOR]

The matching problem for heterologous remote sensing images can be simplified to the matching problem for pseudo homologous remote sensing images via image translation to improve the matching performance. Among such applications, the translation of synthetic aperture radar (SAR) and optical images is the current focus of research. However, the existing methods for SAR-to-optical translation have two main drawbacks. First, single generators usually sacrifice either structure or texture features to balance the model performance and complexity, which often results in textural or structural distortion; second, due to large nonlinear radiation distortions (NRDs) in SAR images, there are still visual differences between the pseudo-optical images generated by current generative adversarial networks (GANs) and real optical images. Therefore, we propose a dual-generator translation network for fusing structure and texture features. On the one hand, the proposed network has dual generators, a texture generator, and a structure generator, with good cross-coupling to obtain high-accuracy structure and texture features; on the other hand, frequency-domain and spatial-domain loss functions are introduced to reduce the differences between pseudo-optical images and real optical images. Extensive quantitative and qualitative experiments show that our method achieves state-of-the-art performance on publicly available optical and SAR datasets. Our method improves the peak signal-to-noise ratio (PSNR) by 21.0%, the chromatic feature similarity (FSIMc) by 6.9%, and the structural similarity (SSIM) by 161.7% in terms of the average metric values on all test images compared with the next best results. In addition, we present a before-and-after translation comparison experiment to show that our method improves the average keypoint repeatability by approximately 111.7% and the matching accuracy by approximately 5.25%. [ABSTRACT FROM AUTHOR]

Multi-temporal Interferometric Synthetic Aperture Radar technique (MTInSAR) has emerged as a valuable tool for measuring ground motion in a wide area. However, interpreting displacement time series and identifying dangerous signals from millions of InSAR coherent targets is challenging. In this study, we propose a method combining stacked autoencoder (SAE) and convolutional neural network (CNN) to classify InSAR time series and ease the interpretation of movements. The InSAR time series are classified into five categories, including stable, linear, accelerating, deceleration, and phase unwrapping error (PUE). The accuracy of labeled samples reaches 95.1%, reflecting the performance of the proposed method. This method was applied to the InSAR results for Kunming extracted from 171 ascending Sentinel-1 images from January 2017 to September 2022. The classification map of the InSAR time series shows that stable coherent points dominate around 79.28% of the area, with linear patterns at 10.70%, decelerating at 5.30%, accelerating at 4.72%, and PUE patterns at 3.60%. The results demonstrate that this method can distinguish different ground motion features and detect nonlinear deformation signals on a large scale without human intervention. [ABSTRACT FROM AUTHOR]

This paper proposed an improved method for separating soil and vegetation component temperatures from one pixel land surface temperature (LST) using multi-pixel and multi-temporal data. The two main features of the method are (1) the use of a diurnal temperature cycle (DTC) model to describe component temperatures and (2) the application of a spatial weighting matrix to consider the spatial correlation of component temperatures. The proposed method was evaluated using an extensive simulated dataset with five component temperature types, three LST errors and 69 fractional vegetation cover (FVC) types, and field measurements with a high temporal frequency. Due to the time extendibility of DTC model, the possibility for retrieving component temperatures at any time was analyzed. Correspondingly, the schemes for selecting the best observations for four representative periods, i.e., 10:00–12:00, 09:00–18:00, 18:00–03:00 (on the next day) and 09:00–03:00, were determined. The validations showed satisfactory accuracies, and it was found that the errors were significantly influenced by the original LST retrieval error. In addition, the difference between the ideal temperature pattern from the DTC model and the actual temperature variation also affected the accuracy of the temporally extended component temperatures. Furthermore, sensitivity analyses indicated that the separation accuracy was independent of the uncertainty of the component emissivity but was influenced by FVC. Specifically, the retrieval accuracy was sensitive to the size and variation of FVC, and the latter had a more significant influence, but the result was less sensitive to the retrieval error and angular effect of FVC. Considering its accuracy, operability and robustness, the proposed method is effective for separating soil and vegetation component temperatures. • An improved method for separating soil and vegetation component temperatures was proposed. • A diurnal temperature cycle model was used to describe component temperatures. • The best schemes to estimate component temperatures at any time were determined. • The influences of LST retrieval error, emissivity uncertainty and FVC were analyzed in detail. [ABSTRACT FROM AUTHOR]

The derivation of widely used geometric optical (GO) kernels in bidirectional reflectance distribution function models, that is, LiSparseReciprocal kernel (KGOLSR) and LiDenseReciprocal kernel, was based on two important assumptions: (1) The shaded components are perfect black and (2) the contributions of two sunlit components are identical. Different from the bidirectional reflectance, thermal radiation directionality effect mainly results from component temperature differences, suggesting the above assumptions are not applicable in most situations. Therefore, this study derived GO kernels for thermal radiation based on temperature differences rather than illumination differences. Specifically, four GO kernels, that is, KGO4 with considering sunlit/shaded vegetation and sunlit/shaded soil, KGO3 with considering sunlit/shaded soil and vegetation, KGO2 with considering vegetation and soil, and KGOg only considering the hottest sunlit soil, have been developed. By using a comprehensive simulated data set, their performances have been thoroughly evaluated and the comparison with KGOLSR has also been analyzed in depth. Results showed that (1) KGO4 had the highest accuracy and KGO3 was the second; in the case of only two available angles, KGOg performed best. (2) Variables such as component temperature, component emissivity, solar zenith angle, and the percentage of tree crown cover mainly affected the comparison result between KGOLSR and KGO2; KGOLSR would have a better performance for a scene with a stronger vegetation effect. Moreover, the best values of two structure characteristics, that is, the crown shape parameter b/r and relative height h/b, for these five kernels have been determined, which can provide instruction for practical application. Plain Language Summary: Land surface temperature is a key variable for a variety of geoscientific studies. However, because of the heterogeneity of land surface, this important parameter has a significant angular effect. Previous studies mainly learned from directional reflectance models. Therefore, in this study, we developed four geometric optical kernels for correcting directional effect of land surface temperature from the inherent characteristic of thermal radiation. These new kernels not only own clearer physical meaning but also have higher accuracies. Moreover, we also determined the best values of two structure characteristics to provide instruction for practical application. Key Points: Developed four new geometric optical kernels for directional thermal radiation based on component temperature differencesThe kernel only considering the hottest sunlit soil component performs best in the case of two available anglesDetermined the best values of two structure characteristics, that is, crown shape parameter and relative height [ABSTRACT FROM AUTHOR]

Advanced Very High Resolution Radiometer (AVHRR) sensors provide a valuable data source for generating long-term global land surface temperature (LST). However, changes in the observation time that are caused by satellite orbit drift restrict their wide application. Here, a generalized split-window (GSW) algorithm was implemented to retrieve the LST from the time series AVHRR data. Afterwards, a novel orbit drift correction (ODC) algorithm, which was based on the diurnal temperature cycle (DTC) model and Bayesian optimization algorithm, was also proposed for normalizing the estimated LST to the same local time. This ODC algorithm is pixel-based and it only needs one observation every day. The resulting LSTs from the six-year National Oceanic and Atmospheric Administration (NOAA)-14 satellite data were validated while using Surface Radiation Budget Network (SURFRAD) in-situ measurements. The average accuracies for LST retrieval varied from −0.4 K to 2.0 K over six stations and they also depended on the viewing zenith angle and season. The simulated data illustrate that the proposed ODC method can improve the LST estimate at a similar magnitude to the accuracy of the LST retrieval, i.e., the root-mean-square errors (RMSEs) of the corrected LSTs were 1.3 K, 2.2 K, and 3.1 K for the LST with a retrieval RMSE of 1 K, 2 K, and 3 K, respectively. This method was less sensitive to the fractional vegetation cover (FVC), including the FVC retrieval error, size, and degree of change within a neighboring area, which suggested that it could be easily updated by applying other LST expression models. In addition, ground validation also showed an encouraging correction effect. The RMSE variations of LST estimation that were introduced by ODC were within ±0.5 K, and the correlation coefficients between the corrected LST errors and original LST errors could approach 0.91. [ABSTRACT FROM AUTHOR]

Land surface temperature (LST) is a crucial parameter that reflects land–atmosphere interaction and has thus attracted wide interest from geoscientists. Owing to the rapid development of Earth observation technologies, remotely sensed LST is playing an increasingly essential role in various fields. This review aims to summarize the progress in LST estimation algorithms and accelerate its further applications. Thus, we briefly review the most‐used thermal infrared (TIR) LST estimation algorithms. More importantly, this review provides a comprehensive collection of the widely used TIR‐based LST products and offers important insights into the uncertainties in these products with respect to different land cover conditions via a systematic intercomparison analysis of several representative products. In addition to the discussion on product accuracy, we address problems related to the spatial discontinuity, spatiotemporal incomparability, and short time span of current LST products by introducing the most effective methods. With the aim of overcoming these challenges in available LST products, much progress has been made in developing spatiotemporal seamless LST data, which significantly promotes the successful applications of these products in the field of surface evapotranspiration and soil moisture estimation, agriculture drought monitoring, thermal environment monitoring, thermal anomaly monitoring, and climate change. Overall, this review encompasses the most recent advances in TIR‐based LST and the state‐of‐the‐art of applications of LST products at various spatial and temporal scales, identifies critical further research needs and directions to advance and optimize retrieval methods, and promotes the application of LST to improve the understanding of surface thermal dynamics and exchanges. Plain Language Summary: Land surface temperature (LST) is a crucial geophysical parameter related to surface energy and water balance of the land‐atmosphere system. Satellite remote sensing provides the best way to measure LST and generate various LST products at regional and global scales. In this review, to facilitate the application of LST products in different fields, we first present the physical meaning of satellite‐derived LST. Subsequently, we summarize recent advances in LST retrieval and validation methods, with a special focus on the state‐of‐the‐art product collections, product accuracies and intercomparisons, and main problems in current LST products as well as their possible solutions. Additionally, we also review the major applications of LST products in agricultural drought monitoring, thermal environment monitoring, thermal anomaly monitoring, and climate change. Finally, we offer recommendations or perspectives to promote LST retrieval methods and their applications. This review will aid the user in gaining a thorough comprehensive understanding of satellite‐derived LST products and promoting their appropriate applications. Key Points: State‐of‐the‐art satellite‐derived land surface temperature (LST) product levels, sources, uncertainties, and differences are providedTypical applications of LST products in various fields are summarizedFuture directions for the generation and applications of LST products are recommended [ABSTRACT FROM AUTHOR]

Abstract: The land-surface diurnal temperature cycle (DTC) and its model parameters characterize the thermal properties of the land surface. In this study, we propose a method to estimate the diurnal cycle of land surface temperature difference (ΔT s) from the top-of-the-atmosphere brightness temperatures of MSG–SEVIRI channels 9 (10.8μm) and 10 (12.0μm). Fitting a DTC model to the diurnal cycle of ΔT s can directly determine the land-surface DTC model parameters without resorting to land surface temperature and emissivity. The performance of the DTC model was evaluated using MSG–SEVIRI pixels over different land cover types. The results show that the fitting accuracy (root mean square error) of the DTC model is better than 1K for most pixels. The spatial patterns of diurnal temperature range, time of maximum temperature, and attenuation constant were analyzed, and the potential applications of these parameters are presented. These parameters are shown to be related to the physical properties of the land surface and may bring new insight into the estimation of thermal inertia and soil moisture. [Copyright &y& Elsevier]

In this study, the global surface urban heat island (SUHI) for 1711 cities during 2003–2019 was quantified by the dynamic urban-extent (DUE) scheme with the land surface temperature datasets from Moderate Resolution Imaging Spectroradiometer Terra and Aqua through the Google Earth Engine platform. The global pattern and regional contrasts of SUHI intensity (SUHII), and the interannual changing rate of SUHII (δSUHII) were revealed at the annual, summer, and winter scales. Further, the associated driving factors for long-term SUHII were explored from a temporal perspective. The main findings are as follows: (1) Globally, the global mean SUHII over 2003–2019 for annual daytime (1.32 °C) and annual nighttime (1.09 °C) by DUE are generally higher than that by previous simplified urban-extent (SUE) scheme. Accordingly, the summer daytime and nighttime SUHIIs are 1.98 °C, 1.05 ℃, while the winter daytime and nighttime SUHIIs are 0.76 ℃, and 1.10 ℃. (2) The annual, summer, and winter δSUHIIs are 0.11 °C/decade, 0.27 °C/decade, and −0.06 °C/decade, respectively, at daytime, and 0.07 °C/decade, 0.09 °C/decade, and 0.10 °C/decade, respectively, at nighttime. (3) The global SUHII and δSUHII demonstrates evident regional contrast. The warm temperate and snow zones show distinct seasonal variations from summer to winter for daytime SUHII. Specifically, the negative daytime SUHII is detected for the arid zone, which exhibits the highest day-night variation and shows decreasing trend. (4) The global SUHII and δSUHII indicate distinct latitudinal variations, and an additional flip-flop (daytime SUHII < nighttime SUHII) region is detected between 10 °S and 20 °S. (5) The long-term daytime SUHII are negatively regulated by the urban–rural difference on evaporative cooling of vegetation; while at nighttime, it is negatively affected by the urban–rural difference on surface thermophysical properties. It implies the urban greening and surface properties should be specifically concerned to increase the evaporation cooling and reduce the heat retention in SUHII mitigation. [ABSTRACT FROM AUTHOR]

Abstract: Land surface temperature (LST) is crucial for a wide variety of land–atmosphere studies. A long-term and time-consistent LST product is highly desirable for use in global climate studies. In this study, we developed a method to normalize the Terra-MODIS LST during daytime to a consistent local solar time to generate a time-consistent LST product. A multiple linear regression model for the slope of LST versus the local solar time during the period 10:00–12:00 as a function of the normalized-difference vegetation index, solar zenith angle, and digital elevation model was established using MSG-SEVIRI data. The regression equation was then applied to normalize the Terra-MODIS LST during daytime to a consistent local solar time (i.e., 11:00 local solar time). The accuracy of the proposed method was evaluated using MSG-SEVIRI-derived LST data. The results indicate that the root mean square error of the differences between the LST before temporal normalization and the actual LST (derived from MSG-SEVIRI data) is approximately 1.5K, whereas those between the LST after temporal normalization and the actual LST is approximately 0.5K. [Copyright &y& Elsevier]

To evaluate the in-flight performance of a new hyperspectral sensor onboard an unmanned aerial vehicle (UAV-HYPER), a comprehensive field campaign was conducted over the Baotou test site in China on 3 September 2011. Several portable reference reflectance targets were deployed across the test site. The radiometric performance of the UAV-HYPER sensor was assessed in terms of signal-to-noise ratio (SNR) and the calibration accuracy. The SNR of the different bands of the UAV-HYPER sensor was estimated to be between approximately 5 and 120 over the homogeneous targets, and the linear response of the apparent reflectance ranged from approximately 0.05 to 0.45. The uniform and non-uniform Lambertian land surface reflectance was retrieved and validated using in situ measurements, with root mean square error (RMSE) of approximately 0.01–0.07 and relative RMSE of approximately 5%–12%. There were small discrepancies between the retrieved uniform and non-uniform Lambertian land surface reflectance over the homogeneous targets and under low aerosol optical depth (AOD) conditions (AOD = 0.18). However, these discrepancies must be taken into account when adjacent pixels had large land surface reflectance contrast and under high AOD conditions (e.g. AOD = 1.0). [ABSTRACT FROM AUTHOR]

Land surface temperature (LST), land surface emissivity (LSE), and atmospheric profiles are of great importance in many applications. Radiances observed by satellites depend not only on land surface parameters (LST and LSE) but also on atmospheric conditions, and it is difficult to retrieve these parameters simultaneously from multispectral measurements with high accuracies. This work aims to establish a neural network (NN) to retrieve atmospheric profiles, LST, and LSE simultaneously from hyperspectral thermal infrared data suitable for various air mass types and surface conditions. The distributions of surface materials, LST, and atmospheric profiles were elaborated carefully to generate the simulated data. The simulated at-sensor radiances were divided into two sub-ranges in the spectral domain: one in the atmospheric window and the other in the water absorption band. Subsequently, the radiances were transformed in the eigen-domain in each sub-range, and then the transformed coefficients were used as the inputs for the network. Similarly, the atmospheric profiles, LST, and LSE were used as outputs after the eigen-domain transformation. The validation of the NN using the simulated data indicated that the root mean square error (RMSE) of LST is approximately 1.6 K, and the RMSE of the temperature profiles is approximately 2 K in the troposphere. Meanwhile, the RMSE of total water content is approximately 0.3 g cm−2, and that of LSE is less than 0.01 in the spectral interval where the wave number is less than 1000 cm−1. Two experiments using actual thermal hyperspectral satellite data were carried out to further validate the proposed NN. All of these studies showed that the proposed NN is capable of retrieving atmospheric and land surface parameters with compromised accuracies. Because of its simplicity, the proposed NN can be used to yield preliminary results employed as first estimates for physics-based retrieval models. [ABSTRACT FROM PUBLISHER]

Precipitable water vapor (PWV) is one of the most variable components of the atmosphere in both space and time. In this study, a passive microwave-based retrieval algorithm for PWV over land without land surface temperature (LST) data was developed. To build the algorithm, two assumptions exist: (1) land surface emissivities (LSE) at two adjacent frequencies are equal and (2) there are simple parameterizations that relate transmittance, atmospheric effective radiating temperature, and PWV. Error analyses were performed using radiosonde sounding observations from Zhangye, China, and CE318 measurements of Dalanzadgad (43°34′37′′N, 104°25′8′′E) and Singapore (1°17′52′′N, 103°46′48′′E) sites from Aerosol Robotic Network (AERONET), respectively. In Zhangye, the algorithm had a Root Mean Square Error (RMSE) of 4.39 mm and a bias of 0.36 mm on cloud-free days, while on cloudy days there was an RMSE of 4.84 mm and a bias of 0.52 mm because of the effect of liquid water in clouds. The validations in Dalanzadgad and Singapore sites showed that the retrieval algorithm had an RMSE of 4.73 mm and a bias of 0.84 mm and the bigger errors appeared when the water vapor was very dry or very moist. [ABSTRACT FROM AUTHOR]

Soil moisture is an important parameter that influences the exchange of water and energy fluxes between the land surface and the atmosphere. Through the simulation by a Soil–Vegetation–Atmosphere Transfer model, Carlson proposed the universal spatial information-based method to determine soil moisture that is insensitive to the initial atmospheric and surface conditions, net radiation, and atmospheric correction. In this study, a practical normalized soil moisture model is established to describe the relationship among the normalized soil moisture (M), the normalized land surface temperature (T*), and the fractional vegetation cover. The dry and wet points are determined using the surface energy balance principle, which has a robust physical basis. This method is applied to retrieve soil moisture for the Soil Moisture-Atmosphere Coupling Experiment campaign in the Walnut Creek watershed, which has a humid climate, and at the Linzestation, which has a semi-arid climate. The validation data are obtained on days of year (DOYs) 182 and 189 in 2002 in the humid region and on DOYs 148 and 180 in 2008 for the semi-arid region; these data collection days are coincident with the overpass of the Landsat Thematic Mapper/Enhanced Thematic Mapper Plus. When the estimates are compared with thein situmeasurements of soil water content, the root mean square error is approximately 0.10 m3 m−3with a bias of 0.05 m3 m−3for the humid region and 0.08 m3 m−3with a bias of 0.03 m3 m−3for the semi-arid region. These results demonstrate that the practical normalized soil moisture model is applicable in both humid and semi-arid regions. [ABSTRACT FROM PUBLISHER]

To integrate soil moisture into the algorithm of the Moderate Resolution Imaging Spectroradiometer (MODIS) global evapotranspiration (ET) project (MOD16), two improvements were implemented: two layers of relative soil moisture parameters were combined with a surface resistance model; and the complementary relationship was replaced with the Penman-Monteith (P-M) method to estimate the dry soil surface evaporation. In the vegetation surface resistance model, a multiplierRsm1was added, and the influence of the relative soil moisture in the root zone was accounted for. In the soil surface resistance model, an empirical exponential relationship was used. To calculate the relative soil moisture parameters, soil hydraulic parameters, such as field capacity (Fc), wilting point (Wp), and saturation point (Sp), were estimated according to the soil texture information; these parameters were used as critical values to estimate the relative soil moisture. Both the MOD16 method and improved method were validated using ET flux data collected at nine flux-tower sites in the USA from 2000 to 2009. The mean absolute BIAS and the root mean square error (RMSE) decreased from 0.36 to 0.30 mm day–1and from 1.14 to 0.97 mm day–1, respectively, after integrating the soil moisture parameters. Meanwhile, the mean correlation coefficient (R) for the nine sites increased from 0.54 to 0.70. Therefore, the improved method performed better than the MOD16 method. Furthermore, the uncertainties associated with the MODIS leaf area index (LAI) products, flux-tower measurements, soil texture, soil moisture, and model parameters were analysed. The outlook for future modifications was also discussed. [ABSTRACT FROM PUBLISHER]

Leaf area index (LAI) is a key variable for modeling energy and mass exchange between the land surface and the atmosphere. Inversion of physically based radiative transfer models is the most established technique for estimating LAI from remotely sensed data. This study aims to evaluate the suitability of the PROSAIL model for LAI estimation of three typical row crops (maize, potato, and sunflower) from unmanned aerial vehicle (UAV) hyperspectral data. LAI was estimated using a look-up table (LUT) based on the inversion of the PROSAIL model. The estimated LAI was evaluated against in situ LAI measurements. The results indicated that the LUT-based inversion of the PROSAIL model was suitable for LAI estimation of these three crops, with a root mean square error (RMSE) of approximately 0.62 m 2 m −2 , and a relative RMSE (RRMSE) of approximately 15.5%. Dual-angle observations were also used to estimate LAI and proved to be more accurate than single-angle observations, with an RMSE of approximately 0.55 m 2 m −2 and an RRMSE of approximately 13.6%. The results demonstrate that additional directional information improves the performance of LAI estimation. [ABSTRACT FROM AUTHOR]

As an intrinsic property of natural materials, land surface emissivity (LSE) is an important surface parameter and can be derived from the emitted radiance measured from space. Besides radiometric calibration and cloud detection, two main problems need to be resolved to obtain LSE values from space measurements. These problems are often referred to as land surface temperature (LST) and emissivity separation from radiance at ground level and as atmospheric corrections in the literature. To date, many LSE retrieval methods have been proposed with the same goal but different application conditions, advantages, and limitations. The aim of this article is to review these LSE retrieval methods and to provide technical assistance for estimating LSE from space. This article first gives a description of the theoretical basis of LSE measurements and then reviews the published methods. For clarity, we categorize these methods into (1) (semi-)empirical or theoretical methods, (2) multi-channel temperature emissivity separation (TES) methods, and (3) physically based methods (PBMs). This article also discusses the validation methods, which are of importance in verifying the uncertainty and accuracy of retrieved emissivity. Finally, the prospects for further developments are given. [ABSTRACT FROM PUBLISHER]

Land surface temperature (LST) and its diurnal variation are vital to the study of land-atmosphere interactions and climate change. In this study, in situ and MSG-SEVIRI-derived LSTs were used to evaluate the performance of six published diurnal temperature cycle (DTC) models, i.e. the GOT01, GOT01_0, VAN06, JNG06, INA08, and GOT09 models. Two time intervals were considered: one is the entire day (sunrise to sunrise: Period A), and the other is from 09:00A.M. to 03:00A.M. on the following day (local solar time) (Period B). The results of Period A indicated that the JNG06 and GOT09 models performed best with overall root mean square errors (RMSEs) of 0.5K. The GOT01, VAN06, and INA08 models performed similarly with overall RMSEs of 0.8K. The GOT01_0 model performed the worst with an overall RMSE of 1K. The results of Period B demonstrated that, except for the GOT01_0 model, the other models produced similar results with overall RMSEs of 0.4K. However, if the width over the half-period of the cosine term (ω) in the GOT01_0, GOT01, and INA08 models was treated as a free parameter in the model fit during Period A, the performance of the GOT01 and INA08 models was significantly improved and attained the same level of accuracy as the JNG06 and GOT09 models. Although the accuracy of the GOT01_0 model was also improved to an overall RMSE of 0.8K, with five free parameters, this model cannot accurately describe the variations of the LSTs around sunrise and noon. [ABSTRACT FROM AUTHOR]