Surface soil moisture (SSM) is essential for understanding the interactions between the atmosphere and Earth’s surface. The rapid development of remote sensing technology in recent decades has provided feasible alternatives for SSM retrieval. The triangle model is one such method that uses the relationship between land surface temperature (LST) and vegetation index (VI) on a triangular space to estimate SSM. However, the traditional LST-VI triangle models inherently suffer from two major drawbacks. First, the subjective requirements for a sufficient number of pixels are characterized by a wide range of vegetation and SSM under uniform atmospheric conditions. Second, this is the need for date-to-date calibration. To overcome these limitations, the present study proposed a novel scheme of the feature space, the single pixel in time-series (SPTS) triangle model. The basic assumption of this feature space is that a given satellite pixel for cropland or grassland will undergo distinct vegetation cover and SSM status due to natural growth and soil moisture dynamics over a relatively long period. Unique triangles for 44 sites in two networks of the International Soil Moisture Network (ISMN)—the TxSon (US) dominated by grassland and REMEDHUS (Spain) dominated by cropland—were constructed based on Landsat data over a period of ~10 years (2013–2023). Compared to the traditional triangle model, the proposed model reveals enhanced skills for SSM retrieval, with a decrease in root-mean-square error (RMSE) by 13.5% (~0.050 m3/m3) over the study sites.

Land Surface Temperature (LST) is a critical parameter in meteorology, hydrology, and environmental science. Compared to thermal infrared remote sensing, passive microwave (PMW) remote sensing for LST retrieval offers advantage under cloudy conditions. In this study, we utilized the Channel Resolution Enhanced (CRE) Microwave Radiation Imager (MWRI) brightness temperature data from the Chinese FengYun-3D (FY-3D) polar-orbiting meteorological satellite as the primary input to obtain global LST. Two physics-based PMW retrieval methods were introduced: the three-channel method (18.7, 36.5, and 89.0 GHz) and the PWV-CLW method, which integrates the 18.7 GHz and 23.8 GHz channels with precipitable water vapor (PWV) and cloud liquid water (CLW). The results indicate that both methods have generally achieved good accuracy. The three-channel method performs well in grasslands and barren lands during the daytime, with a Root Mean Square Error (RMSE) ranging from 4 to 5 K. At night, it excels in grasslands, croplands, and barren lands, with an RMSE from 2 to 3 K. The PWV-CLW method demonstrates superior accuracy for forests and croplands during the daytime, with RMSE values from 3.6 to 5.3 K, respectively. At night, this method excels in accuracy for forests, with an RMSE of 2.9 K. Additionally, a fusion method was proposed to improve the overall accuracy of LST estimation across different land cover types. The RMSE values for ascending and descending overpasses are 4.22 K and 2.76 K, with biases of -0.29 K and -0.6 K, respectively. This approach effectively mitigates spatial heterogeneity and atmospheric effects, enabling all-weather LST retrieval and showcasing the potential of CRE FY-3D/MWRI data for LST monitoring.

Land cover changes (LCCs) affect surface temperatures at local scale through biophysical processes. However, previous studies on the temperature effects of LCCs, whether the potential impacts of virtual LCCs using the space-for-time assumption or the actual impacts of observed LCCs using the space-and-time scheme, have primarily concentrated on analyzing their spatial distribution patterns. Consequently, the temporal trends of temperature effects due to LCCs are less discussed. This study analyzed the temporal trends of land surface temperature (LST) effects induced by actual LCCs by using long-term European Space Agency land cover data and Advanced Very High Resolution Radiometer LST data. The results show that, from 2001 to 2016, there was a gradual reduction in the count of pixels experiencing LCCs globally in which cultivated land expansion is an important cause of LCC. The LST's response to actual LCCs presented a trend of initial increase followed by a subsequent decrease.

The root-zone soil moisture (RZSM) is essential to agricultural water management. However, it is still difficult nowadays to obtain spatiotemporal root-zone soil moisture dynamics using remote sensing technology due to several challenges including the limited penetrate capacity of satellite signals and complicated water flux within soils, especially over irrigated farmlands with growing crops. In the present study, the North China Plain (NCP) which is dominated with a summer maize-winter wheat rotation system, is selected as the study region to investigate the feasibility of predicting RZSM using the AquaCrop model. An in-situ measurements based RZSM dataset named Soil moisture of China by in-situ data (SMCI) was used for calibrating the performance of AquaCrop. Results indicated that an unbiased root mean square difference (ubRMSD) of approximately 0.05 m3m-3can be found between the AquaCrop RZSM and SMCI RZSM during calibration stage from 2013 to 2017. For the prediction period, ubRMSD of approximate 0.04 m3m-3was obtained for both summer maize period and winter wheat period, respectively.

Traditional climate data, such as air temperature and precipitation, have been widely used in various models for crop yield prediction. One of the major challenges is that most of these climate data were derived from either reanalysis products with relatively coarser spatial resolution or from in situ measurements with limited representativeness, which would inevitably reveal significant mismatches regarding spatial scale with other synchronously used vegetation and soil parameters at high resolution (e.g., ~1 km). To this end, satellite-derived land surface temperature (LST) and soil moisture (SM) at a high spatial resolution of 1 km were used as proxies of air temperature and precipitation to evaluate the feasibility of predicting maize yield in three major regions (northeast, northwest, and north China). Specifically, each region includes three provinces. Three widely used machine learning models, namely, the gradient boosting decision tree, extreme gradient boosted tree, and random forest (RF), were considered to avoid the contingency of a single model. In this study, the three models were trained at two spatial scales: 1) region by region and 2) entire maize planting area. Results indicated that using satellite-based LST and SM instead of traditional climate data of air temperature and precipitation can obtain a significantly improved maize yield prediction with the average root mean square error decreased from 862 to 827 kg/ha when the models were trained region by region and from 894 to 840 kg/ha when the models were trained over the entire maize planting area.

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. The independent retrievals of LST and SM from passive microwave observations are mutually restricted and highly dependent on auxiliary data. To solve this problem, a simulations retrieval method of LST and SM was proposed based on the characteristics of multi-frequency and dual-polarization. The simultaneous solution of LST and SM was realized by approximating and correcting the radiative transfer equation (RTE). The performance of the proposed method was evaluated using simulated data, resulting in a root mean square error (RMSE) of approximately 1.63 K and 0.063 m3/m3. This method was further used to retrieve LST and SM from AMSR-E observations. The retrieved LST was compared to the MODIS land surface temperature product under clear-sky, with RMSE of 5.68 K. The retrieved LST was validated using the ISD air temperature under cloudy-sky, with RMSE of 4.29 K. The accuracy of retrieved LST changes with the variation of vegetation. The retrieved SM was evaluated using the CCI soil moisture product and in-situ observations. The result shows that the accuracy ranges from 0.0157 to 0.1115 m3/m3with the change of vegetation. This study gives a feasible method to retrieve LST and SM simultaneously with reasonable accuracy.

Evapotranspiration (ET) is an essential parameter in the water cycle and surface energy balance. Accurate estimation of daytime or daily ET is of great significance for many activities in human economic and social development. This study aims to propose a novel method for estimating daytime average ET by using temporal measurements of land surface temperature (LST) and net surface shortwave radiation (NSSR), following a previous developed elliptical relationship between diurnal cycles of LST and NSSR under cloud-free days. The method was primarily developed from the simulated data of a physics-based Atmosphere–Land Exchange (ALEX) model under different underlying surfaces and atmospheric conditions. Based on the simulated data, the proposed method showed considerable accuracy with the overall coefficient of determination (R2) of 0.958 and the root mean square error (RMSE) of 25.3 Wm-2. In addition, ground ET measurements at four Ameriflux sites (US-ARM, US-SRM, US-Whs, and US-Wkg) during the 2018 growing season were collected to assess the estimated daytime average ET. Results show an overall RMSE of 64.7 Wm-2for the estimated ET at the four sites, and the US-Whs site reveals a best accuracy (R2=0.825, RMSE=44.4 Wm-2). These results indicated a potential for generating daytime ET with geostationary satellite observations at regional scales in future development.

Land surface temperature (LST) is crucial in many global and regional scientific studies and applications. The observation time differs along the scan line due to the intrinsic scanning characteristics of instruments, making satellite-derived LSTs incomparable. Although many algorithms have been developed to address this issue, they have many limitations and uncertainties in application. On the basis of the temporal evolution of clear-sky LST, this study proposed a hybrid and practical algorithm with good applicability for the temporal normalization of satellite-derived LSTs. The proposed algorithm was applied based on Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data across the Continental United States (CONUS) in 2020 and mainly validated by cross-comparisons with the Geostationary Operational Environmental Satellite R-Series 16 (GOES-R16) Advanced Baseline Imager (ABI) LST product over each season and various land cover types. The normalized MODIS LSTs had a superior agreement with the GOES-R16 LSTs. Especially for the temporal differences (original observation time minus the reference time) between 0.5 and 1.0 h, the root-mean-square error (RMSE) and bias of the normalized LSTs were improved by 0.32 K-1.03 K and 0.30 K-1.27 K, respectively. These results demonstrate that the proposed hybrid method has advanced potential for the temporal normalization of polar-orbiting satellite LST.

The monthly mean land surface temperature (MMLST) reflects more stable intra- and interannual temperature variations, and therefore, it has a wider range of applications than instantaneous land surface temperature (LST). This study aimed to generate a high-resolution global MMLST product by temporally upscaling the Moderate Resolution Imaging Spectroradiometer (MODIS) 1-km instantaneous LST. First, six current methods were comprehensively evaluated using cross-validation technology. These six methods are the cross combinations of two temporal aggregation schemes: the average by observations (ABO) and average by days (ABD), and three conversion models: the diurnal temperature cycle model (DTC), the simple average of two instantaneous LSTs (TSA), and a weighted average model for multiple instantaneous LSTs (MWA). The analysis with measurements from 235 flux stations worldwide revealed that the choice of conversion model considerably affected the overall retrieval accuracy, whereas the influence of the aggregation scheme was minor. From the conversion model standpoint, MWA performed best, followed by DTC, and finally TSA; this order remained the same even if DTC and TSA were improved with mean bias correction. Notably, the errors of ABDMWA decreased as the number of daily mean LST (NOD) increased, whereas the errors of ABOMWA were not related to NOD. Accordingly, we deduced that the optimal strategy for estimating MMLST is using ABOMWA when NOD is$\ge 20$. Subsequently, we adopted this combination method to process MODIS instantaneous LSTs and produced a global 1-km MMLST dataset for the years 2003–2020. The validation showed a satisfactory accuracy with a root mean square error (RMSE) of 1.6 K. The intercomparison with MMLSTs from geostationary (GEO) satellites (containing complete LST daily cycle) presented a good agreement (biases < 0.3 K and STDs < 2 K). Compared with atmospheric infrared sounder (AIRS) L3 monthly standard physical retrieval (AIRS3STM) product which had the same temporal span, the newly generated product exhibited a high consistency in reflecting temporal variations of global temperature. Most importantly, it had a prominently better ability to retrieve spatial details of temperature variations due to its higher resolution. Our new method and product show promising prospects for applications in global change studies, where accurate spatially resolved MMLST data are one of the fundamental geophysical variables required.

Soil moisture (SM) is a crucial variable in many domains including climate, agriculture and hydrology. In the past decades, satellite-derived land surface temperature (LST) and fractional vegetation cover (FVC) has been widely used to estimate SM at various spatial scales. In the present study, the Chinese Feng Yun (FY) satellites (FY-4A and FY-3D) were first used to estimate SM over the mainland China, following a pixel-to-pixel scheme of the LST-FVC trapezoid method. The soil moisture active passive (SMAP) product was conducted to assess the estimated SM over the study area. Results indicated that a root mean square error of approximately 0.108 m3m−3can be found between the FY-derived SM and SMAP product.

Due to the coarse spatial resolution of currently available microwave (mostly passive) soil moisture (SM) products, it is difficult to apply these SM data in watersheds or at local scales. To this end, a number of downscaling approaches have been developed to improve the spatial resolution of microwave SM products. Specifically, the optical-/thermal-based downscaling methods are most widely used in recent decades. However, such methods normally rely on instantaneous optical/thermal land surface parameters, which are commonly inapplicable under cloudy conditions. The purpose of this study is to develop a new downscaling method based on the temporal variation in geostationary satellite-derived land surface temperature and net surface shortwave radiation. The proposed method has a certain potential to improve data availability under cloudy conditions, because geostationary satellites are capable of providing land surface parameters at high temporal resolution. The proposed method was tested over the REMEDHUS network in Spain. The scaling strategy of cumulative distribution function matching was used to remove systematic differences in spatial mismatch between satellite pixels andin situSM measurements. Results indicate that the downscaled SM agrees well within situmeasurements and has comparable accuracy with the original microwave SM product. The overall root mean square errors with thein situmeasurements for the original microwave SM and the downscaled SM are 0.054 and 0.057$\text{m}^{3}/\text{m}^{3}$, respectively. This method not only has a successful attempt to downscale microwave SM data using temporal information but also has the potential to avoid the failure of traditional instantaneous observations-based downscaling procedure due to clouds.

Microwave land surface emissivity (MLSE) at the K band plays a key role in driving geophysical parameters, such as land surface temperature (LST). However, satellite-based MLSE currently is hard to be quickly retrieved in clear skies since the time cost is high in removing atmospheric contributions. In this letter, one clear-sky atmospheric profile dataset, including a wide range of precipitable water vapor (PWV) values, was constructed using the Thermodynamic Initial Guess Retrieval database for analyzing numerical relationships between PWV and atmospheric parameters. Then, a simplified algorithm was developed for accurately retrieving instantaneous K-bandMLSEs (18.7 and 23.8 GHz) under clear skies, which can significantly save the time of atmospheric correction. The sensitivity analysis shows that PWV is a key factor affecting MLSE estimation at 23.8 GHz, and the brightness temperature (BT) uncertainty has a greater impact on MLSE estimation than LST. Additionally, with LST derived from the Moderate-resolution Imaging Spectroradiometer, BT from the Advanced Microwave Scanning Radiometer Earth Observing System (AMSR-E), and the ERA5 reanalysis PWV in 2008, the proposed algorithm was respectively applied in Europe and the United States for presenting its applicability at a station scale and regional scale. The actual sounding profile and global AMSR-E MLSE product were used as validation datasets. Results indicate the simplified approach has a good performance with RMSEs less than 0.02 in the site and regional validations. Whereas, there are some apparent overestimations in estimating clear-skies MLSEs, especially for 23.8 GHz. We believe the proposed approach is promising for retrieving other parameters.

Soil salinity plays a key role in influencing the soil dielectric constant and soil backscatter coefficient. However, soil moisture (SM) retrieval models constructed based on active microwave data hardly consider soil salinity. Thus, obtaining the SM datasets with various salinity on regional and local scales is difficult. This study aimed to employ theoretical model simulation to investigate the errors of SM retrieval due to not considering the impact of soil salinity. Then, three typical saline soil dielectric constant models were validated and compared based on the experimental measurement datasets. Results show that the WYR saline soil dielectric constant model has excellent performance. The soil salinity mainly affects the imaginary part of the dielectric constant and the effect of salinity on the soil dielectric constant is more significant when the SM has larger values. In addition, in retrieving SM with soil salinity more than 10 g/kg, the retrieval result of SM has an absolute error of 0.04$\text{m}^{3}/\text{m}^{3}$and a relative error of 5% when not considering the soil salinity impact. In retrieving SM with soil salinity less than 10 g/kg, the retrieved SM error increased by 2%, and the absolute error increased by 0.01$\text{m}^{3}/\text{m}^{3}$as soil salinity increased by 3 g/kg. We believe that The study will give a theoretical reference for establishing the SM retrieval model in saline soil areas using microwave data.

A new two-step physical method was developed to retrieve the land surface temperature (LST) from infrared atmospheric sounding interferometer (IASI) observations. This method relinearized the radiative transfer equation (RTE) by the tangents around the initial estimates of the LST, land surface emissivity (LSE), atmospheric equivalent temperature ($Ta$), and water vapor content ($q$). The Tikhonov regularization method and discrepancy principle (DP) iteration algorithm were employed to stabilize the ill-posed problem and obtain the final maximum likelihood solution of the LST with updating the initial estimation of LST, LSE,$Ta$, and$q$. A new channel selection scheme was proposed for this physical method to obtain an accurate LST estimation. This physical-based algorithm was tested on both simulated and real data obtained from the IASI. The root-mean-square error (RMSE) of the simulated LST is ~1 K based on an initial LST estimate with an RMSE of 2 K (1.9 K). The sensitivity analysis shows that the LST retrieval accuracy is ~1 K based on an LST with a random error of 3 K, constant initial LSE (0.97), 10%$Ta$error, and 40%$q$error. Compared with the Advanced Very High Resolution Radiometer onboard Metop (AVHRR/Metop) LST product, the physical method achieves the LST retrieval accuracy of 1.5 and 1 K for real daytime and nighttime IASI data obtained in the study area. Based on the new method, the LST can be retrieved with an accuracy similar to that of the AVHRR/Metop LST product.

Trapezoidal configurations constituted by land surface temperature and fractional vegetation cover has been frequently used to estimate surface soil moisture (SSM). Determination of the SSM status over the trapezoidal dry and wet edges is required to decouple the volumetric SSM content from the trapezoid-derived M0 because of the coupling of volumetric SSM content and soil texture (i.e., soil moisture availability,M0). Currently, soil hydraulic characteristics generated from soil pedotransfer functions (PTF) provide a preferred solution for describing the SSM status over trapezoidal dry and wet edges; however, most PTF have been developed from laboratory-based soil measurements which have not been fully integrated into remote sensing models for SSM retrieval. This study investigated a practical calibration approach for PTF-derived soil hydraulic characteristics to enhance SSM retrieval using these trapezoidal configurations. Three years of high-resolution SSM measurements were estimated using trapezoidal configurations with Landsat-8 data over a semi-arid network in Spain. For the uncalibrated trapezoid, fair accuracy with a root mean square error (RMSE) of 0.062 m3/m3and bias of 0.040 m3/m3was achieved when compared with in situ measurements. Furthermore, a practical PTF-calibration approach with local measurements was proposed and subsequently integrated into the trapezoid to obtain SSM values. Our results indicated enhanced SSM estimates with an RMSE of 0.050 m3/m3and bias of 0.012 m3/m3with the calibrated PTF. Finally, we found that the calibrated trapezoid can eliminate overestimation and underestimation when the SSM was lower or higher, respectively, which occurred frequently for optical SSM retrievals.

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.

The derivation of microwave land surface emissivity (MLSE) under various weather conditions from the microwave radiometer plays a crucial role in acquiring land surface and atmospheric parameters. Nevertheless, currently, most existing studies mainly focus on the clear-sky scenarios owing to a lack of cloudy-sky land surface temperature (LST) and uncertainties in simulating the scattering and emission properties of atmospheric hydrometeors. Under this background, with satellite observations and the random forest (RF) model, this study proposes a method to estimate the MLSE under cloudy skies. First, clear-sky MLSEs with satisfactory accuracy are retrieved by using the brightness temperatures (BTs) from the Advanced Microwave Scanning Radiometer-Earth sensor, LSTs from the Moderate Resolution Imaging Spectroradiometer, and atmospheric profiles from the ERA5 reanalysis. Then, the relation among the clear-sky MLSE and related impact factors is built with the RF and extended to the cloudy-sky environment for generating all-weather MLSEs with a 0.25°. The results show that the input datasets present a considerable impact on the calculation of instantaneous MLSE, and a 5.73 K bias of ERA5 LST may generate a 0.014-0.021 error in the MLSE from 6.9 to 89 GHz horizontal polarization, while the impacts of BT and profile uncertainties on the MLSE are smaller. The retrieved clear-sky MLSE is coincident with the existing MLSE for the spatiotemporal variations, and there is an average difference range from -0.035 to 0.035 in January 2008. Meanwhile, the constructed RF model can successfully apply to cloudy-sky status and recover the MLSE image gaps affected by cloud contamination.

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.

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.

Investigating the relations between land surface temperature (LST) and biophysical compositions can help the understanding of the surface biophysical process. However, there are still uncertainties in determining the impacts of biophysical compositions on LST due to the atmospheric effects. In this article, four atmospheric correction algorithms were used to correct 12 Landsat 8 images in Xi'an, Beijing, Wuhan, and Guangzhou, China, including the Atmospheric Correction for Flat Terrain (ATCOR2), Quick Atmospheric Correction (QUAC), Fast Line-of-sight Atmospheric Analysis of Spectral Hypercube (FLAASH), and Second Simulation of Satellite Signal in the Solar Spectrum (6S). Then, geodetector was used to investigate the atmospheric correction differences in the spatial heterogeneity relationships between LST and normalized difference vegetation index (NDVI), normalized difference built-up index (NDBI), and bare soil index (BSI). Results indicate that the selected composition factors were greatly improved after atmospheric correction, and the relations between LST and three factors were characterized by obvious atmospheric correction differences in four study areas. On the whole, the 6S algorithm performed the best in improving the factor values and impacting the spatial heterogeneity relations between LST and biophysical compositions, followed by FLAASH, QUAC, and ATCOR2 algorithms. Except for Wuhan, 6S, FLAASH, and QUAC algorithms significantly enhanced the correlation between LST and NDVI. However, all algorithms weakened the correlations between LST, NDVI, and BSI, except Guangzhou. These findings have been verified using the regression analysis. In addition, with geodetector, combinations of any two composition factors all had strongly enhanced impacts on LST, and a combination between NDVI and NDBI performed the strongest in most cases.

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.

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.

Surface soil moisture (SSM) plays significant roles in various scientific fields, including agriculture, hydrology, meteorology, and ecology. However, the spatial resolutions of microwave SSM products are too coarse for regional applications. Most current optical/thermal infrared SSM retrieval models cannot directly estimate the quantitative volumetric soil water content without establishing empirical relationships between ground-based SSM measurements and satellite-derived proxies of SSM. Therefore, in this paper, SSM is estimated directly from 5-km-resolution Chinese Geostationary Meteorological Satellite FY-2E data based on an elliptical-new SSM retrieval model developed from the synergistic use of diurnal cycles of land surface temperature (LST) and net surface shortwave radiation (NSSR). The elliptical-original model was constructed for bare soil and did not consider the impacts of different fractional vegetation cover (FVC) conditions. To optimize the elliptical-original model for regional-scale SSM estimates, it is improved in this paper by considering the influence of FVC, which is based on a dimidiate pixel model and a Moderate Resolution Imaging Spectroradiometer normalized difference vegetation index product. A preliminary validation of the model is conducted based on ground measurements from the counties of Maqu, Luqu, and Ruoergai in the source area of the Yellow River. A correlation coefficient (R) of 0.620, a root-mean-square error (RMSE) of 0.146 m3/m3, and a bias of 0.038 m3/m3were obtained when comparing the in situ measurements with the FY-2E-derived SSM using the elliptical-original model. In contrast, the FY-2E-derived SSM using the elliptical-new model exhibited greater consistency with the ground measurements, as evidenced by an R of 0.845, an RMSE of 0.064 m3/m3, and a bias of 0.017 m3/m3. To provide accurate SSM estimates, high-accuracy FVC, LST, and NSSR data are required. To complement the point-scale validation conducted here, cross-comparisons with other existing SSM products will be conducted in the future studies.

Vegetation water content (VWC) is an important parameter of agriculture and forestry. In this letter, specific polarization ratios were evaluated for estimating VWC of corn and soybean. Backscattering coefficients (σhh, σvv, σvhand σhv), polarization ratios (σhh/σvv,σvv/σvh, and σhh/σhv), and the radar vegetation index derived from L-band (1.26 GHz) and S-band (3.15 GHz) radar data of the passive and active Land S-band sensor (PALS) in Soil Moisture Experiments 2002 were implemented to develop various linear relationship models with field VWC measurements for corn and soybean, respectively. L-band σhh/σvvwas found to be most correlated with corn VWC (R = 0.81), while for soybean, L-band σhh/σhvwas the best parameter to estimate VWC with an R of 0.90. Based upon these analyses, prediction equations for the estimation of corn and soybean VWC using the polarization ratios were developed. Results indicated that L-band σhh/σvvwas able to estimate corn VWC with a root mean square error (RMSE) of 0.53 kg/m2and a mean absolute relative error (MARE) of 11.48%. As for soybean, L-band σhh/σhvwas capable of estimating soybean VWC with an RMSE of 0.12 kg/m2and an MARE of 13.33%. The main reason for these differences is most likely due to the disparate structure features and VWC distribution of corn and soybean. This letter proposes an effective method for acquiring VWC in regional areas, and it is also considered to be a powerful supplement for the current methods based on optical remotely sensed data.

The aim of this study was to estimate soil moisture from RADARSAT-2 Quad-polarization Synthetic Aperture Radar (SAR) data acquired in the agricultural areas. The adopted approach is based on the combination of semi-empirical Water-Cloud model and Dubois model. Firstly, VH backscattering coefficient was used to develop empirical relationship for crop water content estimation. Secondly, crop water content was then used to correct the semi-empirical Water-Cloud model for vegetation effects in order to get the VV and HH backscattering coefficient of soil surface in the absence of vegetation cover. Thirdly, the soil moisture was retrieved based on Dubois model using VV and HH backscattering coefficient of soil surface in the absence of vegetation cover. Finally, the soil moisture retrieved is evaluated over wheat crop fields using ground measurements. This paper proposes an effective method for acquiring soil moisture in the agricultural areas under any weather conditions.

Surface soil moisture (SSM) is a significant variable in various fields of science. This paper aims to analyze and improve a SSM retrieval model to apply it to estimate SSM at the regional scale. Firstly, the model parameters were been analyzed. The rotation angle was transformed into exponential form and the ellipse center horizontal coordinate was decreased for the improved SSM retrieval model. After validation with the simulated data from Common Land Model (CoLM), the result indicated that the accuracy of improved model was not lower than the original one after one model coefficient removed. Subsequently, regional SSM was mapped with improved model from FengYun-2E (FY-2E) observation. In addition, the improved SSM retrieval model showed a good consistency with Climate Change Initiative soil moisture (CCI SM) product. Ultimately, a preliminary validation was conducted using the ground measurements in the source area of the Yellow River (SAYR). The result presented an R of 0.53, a RMSE of 0.06 m3/m3and a bias of 0.03 m3/m3.