Remote sensing-based evapotranspiration (ET) has been widely used in the study of global climate change, water resources management and precision agriculture. However, due to the relative coarser spatial resolution of thermal infrared data obtained by remote sensing, the retrievals of fine resolution ET through different remote sensing-based models were full of challenge. In this paper, a general ET downscaling method based on the land surface temperature-vegetation index (Ts-VI) triangle was proposed. 990 m resolution ET datasets obtained by aggregating 90 m surface energy balance algorithm for land (SEBAL)-derived estimates from Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data over a spatial dimension of 9.9 km by 9.9 km around the AmeriFlux US-Ne1 site were downscaled to 90 m by using this new proposed Ts-VI-based ET downscaling method. Compared with the original 90 m ASTER ET, the 90 m downscaled ET results had a mean absolute error (MAE) of 19.2~40.2 W/m2, a root mean square error (RMSE) of 28.8~52.0 W/m2and a bias of 1.7~2.4 W/m2.

Drought risk assessment is one of the most important basic research topics on the quantitative understanding of the mechanism of drought risk and scientifically reducing the adverse effects of drought, which is of great significance in the theory and practice of developing coping strategies and drought management plans. In this paper, the drought risk on a global scale was quantified according to the hazard, exposure, and vulnerability of drought from 2020 to 2099. In addition, the trends of drought risk variation under two different representative concentration pathways (RCP45 and RCP85) scenarios are analyzed and compared. According to the variation character of drought risk in different scenarios, it is divided into 7 types, and the specific differences of each type are discussed. The results show that (1) the areas with high drought risk are primarily concentrated in populated and high precipitation variability places, such as Pakistan, western India, and central North America. (2) When the greenhouse gas concentration rises from RCP45 to RCP85, the drought risk in about 36.88% of the world will worsen, which is primarily concentrated in southern North America, southeastern South America, southern Africa, southern Oceania, southern Asia, and western Europe.

Due to the spatial heterogeneity within a relatively small distance of urban areas, it is necessary to consider the complex land cover types and three-dimensional geometric structure of urban surface. This study introduces the sky view factor (SVF) to calculate the equivalent emissivity of urban surface. In addition, the thermal radiation of adjacent pixels to target pixels is also considered to establish the urban radiative transfer model. The Landsat-8 collection-2 level-2 science product was taken to validate the proposed urban radiative transfer model. The area within the Fourth Ring Road of Beijing was regarded as the study area, then the land surface temperature retrieval algorithm was applied to estimate urban surface temperature (UST). The results of the UST retrieval algorithm were evaluated by comparing brightness temperature (BT) at the top of atmosphere (TOA) simulated by the Discrete Anisotropic Radiative Transfer (DART) model. The root mean squared error (RMSE) between brightness temperatures estimated by the urban radiative transfer model and those simulated by DART model was less than 0.21 K.

In this letter, we propose a dual-temporal dual-channel (DTDC) algorithm for soil moisture retrieval by using time-series observations from the Sentinel-1 C-band synthetic aperture radar. This algorithm utilizes the ancillary information of vegetation water content derived from optical images and assumes no variation on the surface roughness during the two consecutive radar measurements. Therefore, with the DTDC backscatter observations, four equations could be established using forward models, while three unknowns (the two consecutive soil moisture values and one roughness parameter) could be solved simultaneously by minimizing a cost function. The algorithm was tested with a series of Sentinel-1 dual-channel (VV + VH) data over croplands (sugarcane and cassava) of Northeast Thailand with an upscaling resolution of 1 km. Results show that the proposed algorithm could well capture the temporal change of soil moisture with root-mean-square errors within 0.06 m3/m3when ignoring days with precipitation, and could achieve a similar spatial pattern of soil moisture as detected from the Soil Moisture Active Passive mission, indicating the Sentinel-1 might be a proper tool for agricultural water management.

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.

Evapotranspiration (ET) plays an important role in a large range of applications in the fields of agriculture, hydrology and meteorology. Thermal infrared (TIR) land surface temperature (Ts) is a valuable metric for constraining ET. However, remote sensing-based Ts values corresponding to a scene viewed by different angles thus leading to different retrievals would bring directional anisotropy effect on ET estimation. In this paper, the anisotropy effect was evaluated at first by simulating the directional Ts with an integrated model named SCOPE (soil-canopy spectral radiances, photosynthesis, fluorescence, temperature and energy balance) in which the meteorological data at Yucheng site in China were used as driving data. Then ET were estimated with the surface energy balance system (SEBS) model using the simulated directional Ts. The ET differences with different directional Ts as input were analyzed for assessing the directional anisotropy effect on ET estimation. The analysis of the amplitude of anisotropy showed that the ET difference resulted from Ts anisotropy can reach up to 90 W/m2.

Thermal Infrared (TIR) cameras mounted on unmanned aerial vehicles (UAVs) provide low-cost, high spatial and temporal resolutions TIR data. This paper develops a novel single-channel algorithm adaptive to UAV TIR data. Atmospheric parameters were estimated using atmospheric reanalysis data and surface emissivity were acquired by the Portable Fourier transform thermal infrared spectrometer (102F). Then the effective atmospheric transmittance and emissivity were calculated owing to the broad spectral range of the UAV TIR channel. The results were validated using in-situ land surface temperature (LST) derived from SI-111 radiometers at an area of Baotou City, China. The root mean square error (RMSE) were 2.31K on 24 September and 1.82K on 26 September, which indicates that the proposed algorithm is a promising method to estimate LST from UAV TIR images.

Fine spatial resolution land surface temperature (LST) data derived from a thermal infrared remote sensing image are essential to the study of land surface energy, water and carbon cycles. As an alternative and effective way to obtain fine spatial resolution LST, a large number of LST downscaling methods have been proposed in recent decades to enhance coarse resolution LST to fine resolution. However, the drawbacks of the random selection of scaling factors and the establishment of statistical regression relationships are obvious. In this context, a general and physical LST downscaling method based on surface energy balance (DTsEB) is proposed in this study. Moderate Resolution Imaging Spectroradiometer (MODIS) LST data at 990 m spatial resolution were downscaled to 90 m by using this new proposed SEB-based LST downscaling method in this study. Compared with the concurrent 90 m resolution Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) LST data, the downscaled results have a mean absolute error (MAE) of 1.37 K and a root mean square error (RMSE) of 1.84 K.

Evapotranspiration (ET) plays an important role in a variety of practical applications. Land surface temperature (LST) is a valuable metric for constraining ET. However, retrieved remote sensed LST values corresponding to a scene viewed by different angles thus leading to different retrievals would bring directional effects on ET estimation. Here the directional effects were assessed by developing an approach to provide LST with consistent view angle. The key of this approach was to provide nadir (0° view zenith) surface reflectance with bidirectional reflectance distribution function (BRDF) model to derive nadir fractional vegetation cover (FVC); simultaneously, the original remote sensed directional LST was separated to soil temperature (Ts) and vegetation temperature (Tv) components which have no directional effects. Then the nadir LST were obtained by combining the Tsand Tvusing corresponding nadir FVC. Finally, ET were estimated with the surface energy balance system (SEBS) model using the remote sensed directional LST and the obtained nadir LST as input, respectively, and the difference between them was analyzed for assessing the directional effects. Meteorological and remote sensing data at Yucheng site in China were used as test data. The results showed that the directional effect from LST would cause the escalation of Root Mean Square Error (RMSE) by 15.8 W/m2(5%) in ET estimations.

Generally, atmospheric correction is a key process before the temperature and emissivity separation (TES). In view of the difficulty and accuracy of acquiring synchronous atmospheric profiles, several in-scene atmospheric correction algorithm have been proposed, one of which is the in-scene atmospheric compensation (ISAC) algorithm. Though this algorithm introduces a good way to find the black-body pixels for enhancing the practicability, it is limited by the spatial resolution of hyper-spectral sensors. This paper tries to apply this method to the thermal band data of Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), since the spatial resolution can preferably satisfy the assumption of homogeneous atmosphere and more homogeneous black-body pixels will be found. The results show that the proposed algorithm is capable of retrieving atmospheric parameters with promising accuracies.

Accurate spatialized daily precipitation data plays an important role in meteorology, hydrology and ecology. Tropical Rainfall Measuring Mission (TRMM) precipitation data has been widely used in recent years for the relatively high resolution and large spatial coverage. Among them, two TRMM precipitation products are most commonly used: 3-hour scale (4B42) and monthly scale (3B43). The 3B42 product with a high temporal resolution but low accuracy, while the 3B43 product is the opposite. For hydrological modeling and water resource analysis, the acquisition of daily precipitation data is very important. In most cases, daily precipitation data is obtained by accumulating 3B42 product directly. However, this method ignores the change of precipitation rate. In the case of heavy rainfall, the daily precipitation data from 3B42 data shows a large deviation compared with the daily rainfall observed from rain gauges. Based on the analysis of ground measured daily precipitation and soil moisture data, this paper proposes a temporal disaggregation algorithm of TRMM monthly precipitation products using AMSR2 daily soil moisture data. The results show that this method is simple and feasible, which provide a new reference for the study of temporal downscaling of satellite-based rainfall dataset.

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) is widely considered as one of the key parameters in a variety of practical applications. However, because of the contamination of cloud cover, the retrieval of daily ET from optical remote sensing data under cloudy sky conditions is full of challenge. In this paper, we reconstructed daily ET on cloudy days using the relationship between the potential evapotranspiration ratio (RPET) and the available water fraction (FAW). The field data from 8 Ameriflux sites were chosen to explore this relationship and applied for gap-filling of daily ET at these sites at first. Then MODIS data and meteorological data from Yucheng site in China were used to estimate daily ETs on clear days, and daily ETs on cloudy days at this site were reconstructed using the explored relationship between the RPETand the FAW, as an application of this reconstruction method. The results from 8 flux sites showed that daily ET reconstructions had good agreement with ET measurements, with the root mean square error (RMSE) less than 31.25 W/m2. Based on MODIS and filed data from the Yucheng site, the reconstructed daily ETs under cloudy sky conditions were also consistent with measured daily ETs, with a bias of 2.62 W/m2and an RMSE of 35.21W/m2.

In general, the drought index is usually used for drought monitoring. It is necessary to distinguish different climates in large-scale drought studies because of different climates respond differently to drought. Based on ERA5 reanalysis datasets and the world Map of Koppen-Geiger Climate Classification, this paper evaluates the spatial and temporal distribution of drought under different climate areas along the Belt and Road (B&R) during 2000-2017 from four aspects: precipitation, runoff, evaporation and soil moisture. Results are as follows: except for parts of North Africa and West Asia, the annual variation of precipitation in the other places are not significant, but the runoff is the opposite. However, the amount of evaporation increased significantly in 2017, which may be caused by global warming or El Niño. Overall, the frequency of droughts may not increase in the near future, but if they do, they may occur faster and more dramatically.

Radiances observed by satellites are influenced by both land surface and atmospheric parameters, and it is difficult to retrieve these parameters simultaneously from multispectral measurements with high accuracies. Even though several methods have been proposed, those methods focus on the retrieval of land surface or atmospheric parameters. Generally, those atmospheric parameters are the atmospheric water vapor and temperature profiles. Thus, this study aims to establish a back propagation artificial neural network (ANN) to retrieve land surface emissivity, land surface temperature (LST), atmospheric transmittance, upward radiance and downward radiance simultaneously from hyperspectral thermal infrared data suitable for various air mass types and surface conditions. The principle component analysis (PCA) technique is first used to compress and remove noise from the data. The evaluation of the ANN using the simulated data indicated that the root mean square error (RMSE) of LST is approximately 0.643 K; the RMSEs of emissivity and transmittance do not exceed 0.011 and 0.016. The RMSEs of upward and downward radiance of all channels are approximately 0.72K and 2.95K, respectively. The results show that the proposed ANN is capable of retrieving atmospheric and land surface parameters with promising accuracies. Because of its simplicity, the proposed ANN can be used to produce preliminary results employed as first estimates for physics-based retrieval method.

Daily Evapotranspiration (ET) is of great significance among various practical applications in the fields of water management, drought monitoring and climate change study. This paper utilized instantaneous decoupling coefficient to estimate daily LE (used interchangeably with ET in this paper) with atmospheric and surface resistances calculated from MODIS data. The field data were used only at first to identify the errors induced by the parameter retrieval from remote sensing data. The estimated daily LE was compared with measured data and the result showed that the coefficient of determination (R2) was 0.960, with a root mean square error (RMSE) of 12 W/m2and a bias of −4 W/m2. When MODIS data were involved in the calculation of decouple coefficient and resistances, the R2of the estimated daily LE was 0.949, with a RMSE of 33.1 W/m2and a bias of −17.9 W/m2. Therefore, it is feasible and effective to obtain daily LE using instantaneous decoupling coefficient from remote sensing data.

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

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

Coal fires are a common problem in most coal-producing countries in the world, which could cause serious environmental, economic and other problems. Rujigou coalfield in Shizuishan City, Ningxia, NW China, is well known for being the storehouse of anthracite coal. The coalfield is also well known for its coal fires among all coalfields in China. In the study, an attempt was made to study the coal fires dynamics in Rujigou coalfield during the range time from 2000 to 2007 using a TIR approach based on the multi-temporal nighttime Landsat data. A quantitative analysis was made over coal fires changes in the spatial extent. The results showed that the general spreading direction of coal fires was toward the north or northeast. From 2001 to 2007, the coal fires had a huge increasement with annual average area of 0.14 km2.

This paper presents an improved algorithm for simultaneously retrieving both land surface emissivity (LSE) and land surface temperature (LST) using data from the Spinning Enhanced Visible and Infrared Imager (SEVIRI) on board the MSG-2 satellite. First, the temperature-independent spectral index-based method for LSE retrieval is reviewed and improved in terms of three aspects: atmospheric correction, fitting of the bidirectional reflectivity model, and retrieval of the LSE in SEVIRI channel 10. Then, the generalized split-window method with seven unknown coefficients is used to derive the LST. Finally, this improved algorithm is applied to several MSG-2/SEVIRI data sets over a study area with geospatial coverage of latitude 30 ° N-45 ° N and longitude 15 ° W-15 ° E, and using detailed cases, the modifications to the original LSE/LST retrieval methods are shown to be effective and reasonable. In addition, the SEVIRI-derived LSTs are cross-validated primarily using the Moderate Resolution Imaging Spectroradiometer-derived validated LST data extracted from the MOD11B1 product on two clear-sky days (August 22, 2009 and July 3, 2008). The validation results indicate that more than 70% of the differences are within 2.5 K and that the LST differences tend to be lower at night than in the day, which may result from the homogeneous thermal conditions at night.

In this paper, a generalized regression neural network (GRNN) is used for land surface temperature (LST) retrieval from advanced microwave scanning radiometer-earth (AMSR-E) passive microwave data. To make neural network method more representative of the real situations, the simulated data under various atmospheric and surface conditions is generated with the aid of monochromatic radiative transfer model and the advances integral equation model, and is used to train GRNN, combined with AMSR-E measurements and MODIS LST product on the same platform (Aqua satellite). Because of the lack of simultaneous ground LST measurements in large scale, MODIS LSTs are taken as actual ground LST measurements. Through detailed analysis, the datasets in AMSR-E channels 23.8 V, 36.5 V, 89.0 V and 89.0 H GHz with the smallest root mean square error (RMSE) are used for LST retrieval, and the results show that more than 70% of errors are within 3 K, and the RMSE is 4.66 K.

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

This paper presents a spectral unmixing approach that is implemented using linear unminxing method by a genetic algorithm. The unmixing is constrained not only by the negativity and sum-to-one of the abundances of endmembers at each pixel but also by the spatial autocorrelation of their abundances among eight neighbor pixels. The Moran's I indices are proposed to describe the spatial autocorrelation among a pixel and its neighborhood. Based on the above constraints, the objective of unmixing by genetic algorithm is to minimize the mean square error of mixed spectral values. We tested this approach using Chinese HJ-satellite images and obtained an acceptable result.

Atmospheric correction plays an important role in the retrieval of land surface temperatures and emissivities from remotely sensed thermal infrared images. When imaging technology upgrades from multispectral to hyperspectral, an opportunity appears that atmospheric compensation can be resolved only according to hyperspectral thermal infrared data itself. A set of methods is now proposed to carry out atmospheric correction for the purpose of land surface temperature/emissivity separation: A segmental linear model is proposed to retrieve water vapor line absorption transmittance, a ¿H2O-CO2two channel groups¿ method is designed to retrieve water vapor continuum absorption transmittance, and a procedure to extract atmospheric upwelling radiance is presented. Tests with the simulated hyperspectral thermal infrared (TIR) data demonstrate that these techniques can provide good results for atmospheric compensation.