Land Surface Temperature (LST) is a critical parameter for monitoring surface energy balance and evaluating climate and environmental changes. However, LST retrieval from thermal infrared satellite remote sensing often suffers from data gaps in cloud-affected regions. Existing methods for estimating cloud-covered LST do not adequately account for physical mechanisms under complex meteorological and surface conditions, nor do they address dynamic error variations during the fusion process. To address these limitations, this study integrates the numerical weather prediction model (WRF), land surface model (Noah-MP) and satellite observation data. It comprehensively evaluates the accuracy of the LST simulated by the WRF and Noah-MP. Moreover, a data assimilation and fusion method based on the Kalman filter is used to consider the changes of errors, and the dynamic fusion of these LST data is carried out to obtain the hourly LST with a resolution of 1 km. Furthermore, assimilating downward shortwave and longwave radiation into the Noah-MP model improves its simulated LST accuracy to a certain extent. The fused LST is not only spatially continuous but also exhibits improved reliability. Validation within-situmeasurements shows that the Root Mean Square Error (RMSE) under clear-sky conditions is 2.56 K, and the RMSE of the LST under all-weather conditions is 2.88 K. This method has good potential in generating spatially continuous LST with high temporal and spatial resolution, thus promoting relevant research and applications.

Accurate understory terrain estimation is a key challenge in ecological modeling and forest resource management. Traditional optical remote sensing is affected by significant signal saturation in vegetated areas, while spaceborne LiDAR systems such as ICESat-2 are limited in supporting regional-scale continuous modeling due to their sparse and discrete footprint coverage. This study integrates filtered ICESat-2 understory elevation control points with optical remote sensing data to comprehensively assess the applicability and predictive accuracy of various machine learning models across different regions. By carefully selecting and optimizing models, the most suitable approach for each study area was identified, enabling precise regional-scale understory terrain estimation. Through multi-source remote sensing data fusion, a continuous surface elevation model was constructed, substantially enhancing overall estimation accuracy. Experimental results demonstrate a notable accuracy improvement, with ME = 0.42 m, RMSE = 2.80 m, and STD = 2.77 m. Furthermore, this study systematically quantifies the influence of environmental factors such as forest type, landform features, slope, aspect, and forest canopy height on estimation accuracy. Beyond advancing methodologies for high-precision understory terrain estimation, this study leverages machine learning optimization and multi-source data fusion to overcome the limitations of ICESat-2’s footprint coverage, providing robust technical support for global-scale understory terrain monitoring and ecosystem research.

Land surface temperature (LST) and emissivity (LSE) are the crucial parameters for thermal infrared (TIR) remote sensing. However, the coupling of the two parameters presents a challenge to achieving high-accuracy retrieval, particularly for sensors with only one or two TIR channels. Following the launch of Landsat 9, there has been a rapid increase in demand for methods to accurately estimate LSE and LST for sensors with high spatial resolution but limited TIR channels. Therefore, this article proposes a two-step framework to retrieve LSE and LST for Landsat 9 only using data of its own. First, the data in visible-to-near-infrared (VNIR) to short-wave infrared (SWIR) channels of Landsat 9 were used to retrieve LSEs based on a machine learning method. Subsequently, the split-window (SW) method was employed to retrieve LST based on the estimated LSEs. As a result, the retrieved LSE exhibits high accuracy across the cross and direct validation, with RMSEs all below 0.01 for the two TIR channels. For LST, the retrieved result was validated by the existing products and in situ LSTs from surface radiation budget (SURFRAD), demonstrating excellent accuracies, with RMSE of 1.86 K, which is superior to the LST product of Landsat 9, with RMSE of 2.14 K. Therefore, the proposed framework is feasible for LSE and LST retrieval without support of auxiliary data from other origins, which is of great significance for the sensors with limited TIR channels to produce accurate LSE and LST products.

Sea surface temperature (SST) is an important parameter for assessing sea-atmosphere energy interaction and understanding climate change. One of the primary approaches for obtaining global-scale SST is retrieving from satellite thermal infrared remote sensing data. However, it is challenging to accurately retrieve large-scale SST due to the complexity of retrieving the key intermediate parameter, i.e., sea surface emissivity (SSE), using the standard theoretical model. In this study, we proposed a simplified SSE estimation model based on the satellite view zenith angle (VZA) and wind speed and compared it with three commonly used SSE estimation models. Then, the retrieved SSTs based on those SSEs were validated againstin-situSSTs. Results show that the SSE from the proposed estimation model shows the highest consistency and the lowest biases with the theoretical values compared to the other three estimation models, especially in large VZAs.In-situobservation-based SST validation results show that the SST retrieved using the proposed SSE estimation model also achieves the highest accuracy compared to the other three SSTs, with a mean bias error of 0.08 K, and a root-mean-square error of 0.30 K, which is close to the official SST products. In conclusion, the proposed SSE estimation model shows good performance both in SSE estimating and SST retrieving. Furthermore, the proposed model has the potential to estimate SSE on large scales that can serve as a reference for obtaining SST from other similar sensors to promote the development of marine remote sensing.

As one of the key parameters in the physics of land-surface processes, land surface temperature (LST) plays an important role in many fields, such as urban heat island effect, forest fire monitoring, drought monitoring and so on. Thermal infrared remote sensing is the main way to obtain LST in large scale. For the sensors with one TIR channel, the single channel methods are mostly used. These methods need to know atmospheric parameters and emissivity. However, the accuracy of atmospheric correction is different to guarantee in many cases, which limited the applicability and accuracy of these methods. Therefore, based on the channel correlation hypothesis, a single channel algorithm without atmospheric correction is proposed in this paper. The feasibility of this method is preliminarily verified by simulated and satellite data.

Land surface temperature (LST) products with high spatial resolution and short revisiting cycles are crucial for environmental studies. However, due to the tradeoff between spatial and temporal resolutions of satellite observations, such data are not directly available. Spatial downscaling and spatiotemporal fusion methods are existing solutions for this problem, but their robustness is limited under different surface conditions. Here, we propose a Robust Framework for Combining Downscaling and spatiotemporal Fusion methods (RFCDF) to generate the synthesized daily high-resolution LST with high accuracy in different landscapes. RFCDF introduces a novel weighting strategy that determines pixel-level weights using an empirical function under the constraint of the image-level weights of two predictions. We implement the framework using the thermal sharpening algorithm (TsHARP) and Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) with Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and Moderate Resolution Imaging Spectroradiometer (MODIS) data in Beijing and Baotou, as well as Landsat 8 and simulated coarse resolution imagery in nine sub-regions with different surface landscapes in Beijing. Our results demonstrate that RFCDF can generate more accurate estimations and preserve more spatial details than either individual or combination methods, improving accuracy by 0.1–0.6 K and 0.4–1.3 K in the two study areas, respectively. Moreover, the proposed framework is robust, reducing the root mean square error of estimations by 8-24% under different surface conditions. RFCDF can also generate dense high-resolution LST time series, which is crucial for studying the surface thermal environment at a finer scale.

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.

Landsat data are the important sources for the inversion of land surface temperature (LST) with high spatial resolution. As an important parameter of LST inversion, the land surface emissivity (LSE) of Landsat TIR channels is usually inverted by the semi-empirical method, which has certain limitations. With the launch of Landsat 9, the requirement for LSE became more urgent. Therefore, this paper proposed a new retrieval method to estimate LSE with high spatial resolution for Landsat. Reflectance of Landsat VNIR-SWIR channels were used for the LSE inversion by Gradient Boost Regression Tree (GBRT) machine learning method. In order to evaluate the accuracy of the estimated results, the LSEs were compared with those estimated by NDVI threshold method and the field measurement data. The results showed the LSEs estimated by GBRT model were consistent with the measured data. So it demonstrated that this method was feasible to estimated LSE for Landsat.

Satellite-derived high-resolution LST observations are essential for environmental studies. However, the tradeoff between spatial and temporal resolutions largely restricts the application of current LST products. As a consequence, many spatial downscaling or spatiotemporal fusion methods were proposed to overcome this limitation. In this paper, we design a novel empirical weighting method to combine the results from the popular downscaling and fusion methods, thermal sharpening algorithm (TsHARP), and spatial and temporal adaptive reflectance fusion model (STARFM). Specifically, the error of the two methods are firstly estimated and the predictions are blended based on the inverse ratio of the corresponding error. Our method is tested with Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and Moderate Resolution Imaging Spectroradiometer (MODIS) data in Beijing. Compared with the actual ASTER LST, the combining results could both enhance the accuracy and structure similarity, as our method utilizes spatial-temporal-spectral information. Moreover, our method also has the potential for generating more accurate daily high-resolution LSTs.

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.

Land Surface Temperature (LST) is one of the important parameters in thermal environment monitoring. Satellite thermal remote sensing is the major way to obtain spatial-temporal information of LST. However, limited by the cloud contamination and the trade-off between spatial and temporal resolution, current temperature products are difficult to provide all-weather LST. In this paper, a method is proposed to obtain all-weather hourly LST. It consists of two main steps: 1) reconstruction of LST under cloudy-sky by using enhanced annual temperature cycle (ATCE) model and 2) establishment of relationship between LST and air temperature which is used for the acquisition of hourly LST. In the end, the performance of the method is analyzed through the artificial data which is created by masking the origin images. And the results show that the proposed method is valuable for generating all-weather hourly LST.

The thermokarst lake is one of the most typical thermal and thawing disasters, and also an key sign of permafrost degradation. It has a strong impact on the study of global climate change. In this paper, the Beilu river basin in Qinghai Tibet Plateau was selected as an example. With the global availability Landsat data (TM, ETM+, OLI), we obtained the multi-spectral indices, which is closely related to the state of thermokarst lakes rich area. Then, the longterm change trend parameter sets of the multi-spectral indices from 2000 to 2020 are taken as the input data sets of machine learning method to accurately characterize the change state of the thermokarst lakes. Based on the proposed machine learning method, the dynamic change results of the thermokarst lake rich area were obtained pixel by pixel. The results show that it is an effective way for thermokarst lake dynamics analysis within the permafrost region. It is not only helpful to predict and control the change of thermokarst lakes, but also has important practical significance for the study of global climate change.

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

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.

Satellite sensors with large view angles can provide wide-swath imaging for earth observations, which pose new challenges for cross-calibration due to bidirectional reflectance distribution function (BRDF). To address these challenges, we adopted global searching (GS) algorithms to “search” calibration coefficients with BRDF considered. The GS-based methods were implemented to calibrate two Chinese large-view-angle sensors: the Gaofen-1 first wide-field-of-view (WFV1) camera and Gaofen-4 panchromatic multispectral sensor (PMS) with Landsat-8/Operational Land Imager (OLI) as references. Validations were conducted by evaluating the top of atmosphere (TOA) radiance and surface reflectance using synchronous OLI data. The mean relative biases (MRBs) of the GS-derived TOA radiance for the WFV1 were smaller than 5.5% compared with the OLI-simulated TOA radiance, while those of using official coefficients were up to 13%. The performances of the GS method and traditional method using the Moderate Resolution Imaging Spectroradiometer (MODIS) BRDF products for BRDF correction are comparable. The GS-based scheme has the potential to correct BRDF during cross-calibration and thus free cross-calibration of large-view-angle sensors from BRDF models and products.

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

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

The Tianwan nuclear power plant located in Jiangsu province of China discharges warm water from its cooling system into the Yellow Sea and may cause ecological consequences. In this study, the sea surface temperature changes before and after the operation of the Tianwan nuclear power plant was studied by using Landsat data. After retrieving the sea surface temperature near Tianwan nuclear power plant, the datum temperature was firstly extracted. Consequently, the thermal pollution value was calculated. Finally, the area distribution maps of different temperature rise levels were analyzed. The results showed that there was no obvious thermal pollution before the nuclear power plant was put into use. Since the commercial operation of the nuclear power plant, the thermal pollution appears around the nuclear power plant. In the following years, the distribution area of thermal drainage has increased. The area of warm temperature pollution at the + 1 °C level was the largest in every year.

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.

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

The development of ground-based sampling strategies is vital to the validation of medium- or coarse-resolution satellite-derived land surface temperature (LST) products, extremely over heterogeneous ground with dramatic diurnal LST change. An optimal sampling strategy in support of LST validation across both spatial and diurnal scales (SDS) was proposed in this study. The SDS integrated prior knowledge of land-cover, multi-temporal feature information and spatial distribution of samples to improve the representativeness of the samples. The SDS were also compared with three sampling strategies including random, systematic, and land-cover base sampling. The results obtained by the remote sensing simulation data indicated that the SDS performed best with stable root mean square errors (RMSE) less than 0.1k when sample ratio was more than 2%, and the representativeness of samples selected by the SDS in both diurnal space and spatial space was superior to the current sampling strategies.

The angular effect is a primary obstacle for wide applications of land surface temperature (LST) products. Current directional thermal radiation models do not fully consider the difference between visible/near infrared and thermal radiative, i.e. thermal inertial effect, and are not practical enough. Therefore, this study proposed a practical method for angular normalization of LST products based on the component temperature and fractional vegetation cover (FVC). Analyzing with simulated data indicated that the proposed method could improve the LST retrieval accuracy caused by angular effect from 1.2 K to 0.8 K. In addition, the retrieval accuracy of component temperature would affect the performance of the proposed method whereas the retrieval accuracy of component emissivity had almost no effect on the performance.

Net surface shortwave radiation (NSSR) is a key parameter for the estimation of surface energy budget. This paper proposes a method to directly estimate the NSSR from simulated Chinese Gaofen-5 (GF-5) data without using any ancillary information. Firstly, the narrowband reflectances of visible/near infrared channels at the top of the atmosphere (TOA) were converted to the TOA broadband albedo. Secondly, by categorizing the land surface into three types, the NSSR was estimated under clear and cloudy skies separately based on the relationship between TOA broadband albedo and the Earth's surface absorbed shortwave radiation. The estimation error of the absorption coefficient for each land type is lower than 0.05. Finally, by employing a look-up-table acquired in the process of narrowband-to-broadband conversion, and the parameters in the NSSR estimation model for each land type, the performance of the proposed method was evaluated, where the root mean square errors (RMSEs) were 25.85 (13.97) W/m2, 20.39 (7.97) W/m2, and 40.54 (11.26) W/m2for land, ocean and snow/ice surfaces for clear (cloudy) skies, respectively.

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.

Net Surface Shortwave Radiation (NSSR) is a key component of the surface radiation budget, which controls the energy, water exchanges, and many physical processes. The primary purpose of this study is to build a concise and feasible model of estimating NSSR with data of Moderate Resolution Imaging Spectroradiometer (MODIS) onboard the Terra satellite. Random Forest (RF) machine learning method was applied to building the model with the FULXNET in situ observations because of its powerful ability in nonlinear fitting. Total 17 variables are considered in RF model for retrieving NSSR, and thousands of parameters combinations were carried to obtained optimal parameters of the proposed model. The Bias, RMSE, and R2of estimated instantaneous NSSR in 95 selected sites during 2014 whole year are -0.085 W m-2, 28.274 W m-2and 0.989, respectively. Consequently, retrieval of instantaneous NSSR with RF method would be believed to be an efficient method in the future by considering its concise process and great accuracy.

Microwave land surface emissivity (LSE) is an important parameter for retrievals of land surface and atmospheric characteristics, and it is also crucial as an input parameter for numerical weather prediction model data assimilation. This study develops a method for retrieving all-weather LSE over China based on radiative transfer model through reconstructing spatial-temporal continuous land surface temperature (LST) data using China Land Data Assimilation System (CLDAS). Atmospheric effect is also removed with the relationships among atmospheric transmittances, atmospheric effective radiating temperature, precipitable water vapor (PWV), and cloud liquid water (CLW). The retrieved LSE are preliminarily validated by the simulations of Community Radiative Transfer Model (CRTM) and two LSEs show a determined parameter (R2) of 0.81 at 18.7 GHz.

Leaf area index (LAI) is a very important vegetation parameter and has been used in growth monitoring, yield estimation, land surface modelling, among others. When the retrieval function built at a local scale are further applied at a large scale for heterogeneous surface, up-scaling effects would appear. The computational geometry method (CGM) is regardless of whether or not retrieval function is continuous or derivable. According to the theory of computational geometry, the exact LAI always falls into the interval determined by the lower and upper boundaries of the convex hull of the retrieval function. The mean value of those lower and upper bounds is assumed to be close to the exact LAI and are used to reduce upscaling effects. However, the constant weights of the scaling model are the key limitation of traditional CGM because the required uniform distribution rarely happens. To overcome this limitation, this paper tries to use variable weights rather than constant weights and successfully reduce the RMSE of retrieved LAI from 0.247 to 0.054

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.

This study proposes a land surface temperature (LST) downscaling method to downscale the LST of Moderate Resolution Imaging Spectroradiometer (MODIS) from 990m to 90m by using random forest (RF) regression algorithm. The LST product of Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), with 90m resolution, serves as the validation reference at the finer scale. The proposed method is based on the relationship between LST and a variety of surface parameters including band reflectance, spectral indices, land cover types and terrain factors. The proposed downscaling method is evaluated in Segovia, Spain, the Pefiarora mountain region. Comparison between downscaled LST and referenced LST proves that the proposed method shows a great accuracy in downscaling LST. Furthermore, another downscaling method, an algorithm for sharpening thermal imagery (TsHARP), is also implied to get finer resolution LST to make a more complicated comparison with the proposed method. The results are evaluated by root mean squared error (RMSE) and bias, which demonstrate that the accuracy and robustness of RF downscaling method compared with TsHARP.

Continuous monitoring of high spatial resolution evapotranspiration (ET) is critical for water resources management at both regional and local scales. This research employs a multi-sensor satellite data fusion approach (ESTARFM: Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model) combined with a Two-Source N95 model and a constant evaporative fraction method to compute daily ET at 30 m spatial resolution. Two schemes are followed: the first scheme is to apply ESTARFM on the LST data to estimate daily ET at 30 m spatial resolution. The second scheme is to apply ESTARFM on the ET derived from MODIS and Landsat 8 images. The results show that the ET fused by both schemes is in good agreement with the reference ET data from the Landsat 8, while the first scheme (applying the ESTARFM on LST) is observed with more variations.

The traditional methods used in the atmospheric correction depend on the empirical relationships or atmospheric radiative transfer model. However, both of them have some deficiencies. For example, as the empirical method depends highly on the training data, it will be applicable under certain conditions. On the other hand, the method that based on the atmospheric radiative transfer model has to run the code each time, which is not an appropriate choice for operational correction of the atmospheric effects. In this paper, a fast parametric model of estimating atmospheric parameters for Landsat 8 thermal infrared sensor is proposed. The results show that the RMSE (Root Mean Squared Error) of the total transmission is 0.003, the RMSE of for both the atmospheric upward and downward radiances are 0.0004. Therefore, the proposed model could be used without the help of any atmospheric radiative transfer model. That is, this model would have a better application market.

The Net Surface Shortwave Radiation (NSSR) at the Earth's surface drives evapotranspiration, photosynthesis and other physical and biological processes. The primary objective of this study is to estimate NSSR using multispectral narrowband data of the National Polar-orbiting Operational Environmental Satellite System (NPOESS) Visible Infrared Imaging Radiometer Suite (VIIRS). A method to convert narrowband reflectance to broadband albedo at TOA with VIIRS data is developed and retrieval of NSSR from TOA broadband albedo is also carried out. Accurate radiative transfer model MODTRAN 5 was used to simulate these physical quantities, and the least squares method is applied to get coefficients. The RMSE of Narrowband-to-broadband albedo conversion results is 0.011. And the RMSE of between actual and simulated NSSR in clear sky condition is 50.2 W/m2.

Directional anisotropy limits the application of land surface temperature (LST) and a simplified parametric model to effectively estimate directional brightness temperature (DBT) in the thermal infrared is critical. This study used a widely validated four-stream scattering by arbitrarily inclined leaves (4SAIL) model as a benchmark to evaluate the performance of the kernel bidirectional reflectance distribution function (BRDF) model and the three-kernel-model. Results showed that the two kernel-driven models can fit the DBT well and the maximum root mean square error (RMSE) is 0.13°C. The kernel BRDF model has a wider application scope including canopies of uniform, spherical, plagiophile and planophile LIDF with low LAI and hotspot. When LIDF is planophile and plagiophile, two models can reach the best fitting effect and the worst effect is the canopy with erectrophile LIDF. Under a specified LIDF, the relationship between fitting accuracy and LAI is negative while hotspot parameter is positive.

Evapotranspiration (ET) is one of the most significant components in the water and heat transfer between land and atmosphere. This paper develops an improved constant evaporative fraction (EF) method through a theoretical derivation to improve the upscaling of remote sensing instantaneous latent heat flux (LE) to daily scale. Preliminary results show that our improved constant EF upscaling method can significantly reduce the underestimation of the daily LE upscaled using the conventional constant EF upscaling method. More validation work will be conducted to test the robustness of our improved EF method for the upscaling of remote sensing instantaneous LE estimates to daily scale.

Quantification of land surface evapotranspiration (ET) at daily or longer time scales is of great significance in agricultural ecosystem and hydrologic cycle. Temporal upscaling of instantaneous remote sensing-based ET to daily or longer time scales is generally only based on a single instantaneous estimate. A test is made to use two instantaneous ET estimates for the daily upscaling. The results show that the temporal upscaling using two instantaneous ET estimates is superior to that using only single instantaneous ET estimate for the constant extraterrestrial solar radiation ratio (Rp) method, the constant global solar radiation ratio (Rg) method, and the constant evaporative fraction (EF) method. The largest improvement of daily ET estimation occurs when instantaneous ET in the morning is combined with that in the afternoon for the Rpand Rgmethods, while the for EF method the optimal combination comprises of two moments in the afternoon.

Thin cirrus clouds can reduce land surface long-wave transmission and re-emit energy at a colder temperature and thus making it difficult to estimate downwelling surface longwave radiation (DSLR) from satellite data. In this study, a simulation database is established in terms of radiances observed at the top of the atmosphere (TOA), cloud optical thickness (COT), atmosphere water vapor content (WVC) and height of the cirrus bottom (HCB) and DSLR. And the back propagation (BP) artificial neural network (ANN) was used to estimate DSLR from remotely sensed data for cirrus cloudy skies. Results show that the BP model with TOA thermal radiance, COT, WVC and HCB as inputs provides a practical and efficient tool for remote sensing applications to estimate DSLR under thin cirrus clouds with root mean square error (RMSE) of 11.66 W/m2.

This paper aims to extend the generalized split-window (GSW) algorithm in land surface temperature (LST) retrieval to atmospheres with air temperature inversion (ATI) near the Earth surface boundary. Simulation analysis shows that the influence of ATI on the LST retrieval of the GSW algorithm becomes larger when the ATI intensity increases. To further analyze the influence, all ATI atmospheric profiles are extracted from the Thermodynamic Initial Guess Retrieval (TIGR) cloud-free database. Combining the ATI atmospheric profiles and the GSW coefficients, we find that the LST retrieval error caused by ATI is larger than 0.3 K. To reduce the LST retrieval error associated with the ATI in the GSW algorithm, a quadratic equation as a function of ATI intensity is proposed. To validate the proposed method, some in situ measurements observed at the Hailar site are used. The results show that the proposed method could improve the LST retrieval accuracy by 0.47 K for atmospheres under ATI conditions.

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

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

Ambient irradiance is pivotal to be considered for field measurements of soil emissivity with Portable Fourier Transform Infrared Spectro-radiometer (102F). Usually, a diffusely reflecting gold plate which has a near-Lambertian behavior was used to acquire the ambient irradiance. Because of the measurements of soil and ambient irradiance are not synchronized, It can generate errors on determination of soil emissivity, especially for the erratic cloud and instantaneous wind which can make the ambient irradiance a sharp change. In this study, four conditions about the ambient radiances were 30% underestimated, 50% underestimated, 30% overestimated and 50% overestimated to assess the impacts of ambient irradiance on determination of soil emissivity. Preliminary research shows that ambient irradiance has more impacts on determination of soil emissivity in 8-10um than it in 10-13um. In 8-10um, the relative difference of soil emissivity can be more than 0.005 when the ambient irradiance was 30% overestimated. And it can reach up to 0.01 when the ambient irradiance was 50% overestimated. The error magnitudes are related to soil types. By contrast, the impacts of ambient irradiance are not obviously in 10-13um. Similar results can be seen in the ambient irradiance were underestimated conditions.

This paper analyzes the influence of the anomalous temperature occurred at the near surface boundary layer of the atmosphere on the land surface temperature (LST) retrieval with the generalized split-window algorithm (GSW). The coefficients in the GSW algorithm corresponding to a series of overlapping ranging of the mean emissivity, the atmospheric water vapor content, and the LST are derived using a statistical regression method from the numerical values simulated with an accurate atmospheric radiative transfer model MODTRAN 4 over a wide range of atmospheric and surface conditions. The simulation analysis shows that the LST can be estimated by the GSW algorithm with the root mean square error (RMSE) increasing by larger than 0.2 K when atmospheric anomalous profiles are involved. Taking into account the angular dependence of the top of the atmosphere radiance, six different viewing zenith angles (VZAs) are used in the simulations. Results show that the RMSEs become larger when the VZAs change form 0°to 60°.

An algorithm has been developed for retrieving instantaneous microwave land surface emissivity using brightness temperature and precipitable water vapor data. Unlike previous algorithms, the new technique does not need infrared land surface temperature as the input data, and overcomes the limitation of previous algorithms under cloudy conditions. Compared with the values from physical retrieval algorithm, the result demonstrates that this new algorithm has a Root Mean Square Error of 0.038 and a bias of 0.012. Although the accuracy is worse than 1%, this new algorithm presents the potential to obtain the instantaneous microwave land surface emissivity under both cloud-free and cloudy conditions, which can be applied in some weather prediction models.

Because of the complex influences of cirrus clouds on the estimation of Land Surface Temperature (LST), the traditional LST retrieval algorithms can only be used for clear-sky conditions and there is no LST when the pixel is identified as clouds by cloud mask algorithm. To retrieve LST under cirrus clouds, a three-channel algorithm what is dependent on cirrus optical depth (COD) and effective radius was proposed. The simulated data showed that the daytime LST could be retrieved using the three-channel algorithm with a root mean square error of less than 3.0 K when COD (at 12 μm) was less than 0.7 and viewing zenith angle was less than 60°. Compared with the results of the traditional clear-sky two-channel LST retrieval algorithm, where the maximum RMSE was 17.8 K, the algorithm proposed in this study could significantly improve the accuracy of the daytime LST retrieved using satellite thermal-infrared data.

Evapotranspiration (ET) is one of the most significant components in the water and energy transfer between land surface and atmosphere at regional and global scales. this study aims to explore the underlying mechanism in the surface temperature versus fractional vegetation cover (Ts-Fr) space for regional ET and evaporative fraction (EF) estimation through a physically-based soil-vegetation-atmosphere transfer (SVAT) simulation. It also investigates the effect of vegetation type and physiology on the relationship between EF and Tsunder deep-layer water-saturated and water-stressed conditions. The preliminary results show that in the Ts-Frspace surface EF varies linearly with surface temperature when root zone layer is not water-stressed. However, the linear relationship may be different between one vegetation type and another. When root zone layer is water-stressed, the variation of root zone layer soil water content has a negligible effect on the canopy temperature but the EF can be significantly influenced.

This work estimated firstly net surface shortwave radiation (NSSR) from MODIS/Aqua data with six visible and near infrared channels by re-parameterizing the methodology proposed by Tang et al. (2006). Comparison of the estimated NSSR with those simulated actual one showed that the root mean square error (RMSE) is 34.1 W/m2. To validate the proposed parameterization scheme, some field measurements made at seven sites of the Surface Radiation Budget Network (SURFRAD) in October, 2008 were used. The result showed that the RMSE is 53.33 W/m2. To accurately capture the diurnal variation of NSSR for cloudy skies, a simple and practical linear regression model by combing the instantaneous NSSRs estimated from MODIS/Terra at local solar time 10:30 AM and MODIS/Aqua at 13:30 PM has been proposed to estimate the daily average net surface shortwave radiation (DANSSR). The results showed that the RMSE between the estimated DANSSR and those calculated from the seven SURFRAD measurements for cloudy days in 2008 is 42.59 W/m2.

This paper retrieves the Land surface temperature (LST) from MODIS mid-infrared data. Considering that the daytime mid-infrared satellite data contains both reflected radiance due to sun irradiance and emitted radiance from the surface and the atmosphere, this paper estimates the bidirectional reflectivity in mid-infrared channels firstly, and then derives the directional emissivity with the linear kernel-driven BRDF model. Finally based on the radiative transfer equations in mid-infrared channels, the LST is retrieved. The retrieved LSTs are preliminarily validated with the MODIS LST product MYD11B1. The results show that the root mean square error (RMSE) between the two estimated LST is below 1.9 K and the Bias is below 1.10 K. In addition, some in situ measurements are also used to validate the retrieved LST. The results show that the RMSE is 2.06 K and Bias is 0.73 K.

Based on analyzing the influence of atmospheric water vapor content (WVC) on coefficients determination in the generalized split-window (GSW) algorithm, it is found that the coefficients are relatively monotonic variable with the increasing of WVC, which were proposed to determine the coefficients as implicit linear functions. To improve the land surface temperature (LST) retrieval accuracy in the GSW algorithm, the WVC is proposed to determine the coefficients as an explicit parameter in this work. The results show that the proposed method can acquire relatively high accurate LST if WVC is known. The root mean square errors (RMSEs) between the actual LST and those estimated with the proposed method are lower than those retrieved with the coefficients in the GSW algorithm.

To compare and evaluate the performance of iterative spectrally smooth temperature and emissivity separation method (ISSTES) and linear emissivity constraint temperature and emissivity separation method (LECTES) on land surface temperature (LST) and land surface emissivity (LSE) estimation, the simulation data for hyperspectral infrared spectroradiometer are used. The results reveal that the LST can be retrieved within the accuracy of 1 K at various conditions for both methods. However, the 0.01 accuracy of LSE depends on the method selected and the noise level. The ISSTES method should be taken full consideration when it used to retrieve LSE for the warm and wet atmosphere. It is advised that the ISSTES method is used for cold and dry atmosphere and the LECTES method for warm and wet atmosphere. The noises in the ground measurements may be have more effects on the accuracies of LST and LSE than those in the atmospheric downwelling measurements.

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

Land surface temperature (LST) is a critical parameter for numerical weather forecasting, drought monitoring, water resources management and global climate change studies. Because of the supercooled temperature, the cirrus cloud can significantly reduce the LST retrieved from thermal infrared data. This paper focused on analyzing and reducing the influence of thin cirrus cloud on the accuracy of LST retrieved using the generalized split-window (GSW) algorithm. A correction method was proposed with the LST retrieval error expressed as linear functions of cirrus optical depth (COD). The slopes of the linear functions were further written as the combination of the difference and mean of two used channels emissivities and cirrus cloud top height (CTH). The results showed that the LST retrieval accuracy could be significantly improved with root mean square error (RMSE) of LST changing from 14.4 K before LST error correction to 1.8 K after LST error correction for COD equivalent to 0.3.

The total atmospheric precipitable water vapor amount (TWV) is a key variable for the study of the Earth's climate. This paper develops an algorithm to estimate the TWV over clear skies from the Medium Resolution Spectral Imager (MERSI) data in the near-IR channels. The MODTRAN 4 code is used to simulate the top of the atmospheric radiances for the MERSI channels. The results show that the proposed algorithm is suitable to estimate TWV form the absorbing channel centered at 0.940 μm and the atmospheric window channels centered at 0.865 μm and centered at 1.030 μm by the radiances over the clear pixels, with relative differences in the range of 10%-15%.

To evaluate the radiometric characteristics of the thermal infrared channels of Visible and InfraRed Radiometer (VIRR) aboard Chinese second generation polar-orbiting meteorological satellite FengYun-3B (FY-3B), the inter-calibration of those thermal infrared channels with high spectral resolution data acquired by the Atmospheric InfraRed Sounder (AIRS) aboard Aqua is carried out in this paper. Four steps, i.e. subsetting, collocating, transforming and regressing, were used to calculate the inter-calibration coefficients. The collocation data were picked out with a series of thresholds: the absolute viewing zenith angle differences less than 10°, the absolute viewing azimuth angle differences less than 20°, and absolute time differences less than 40 minute. The results on June 1st, 2012 reveal that the VIRR/FY-3B measurements are highly linearly related to the convolved AIRS/Aqua measurements. However, calibration discrepancies exist between VIRR and AIRS channels. When brightness temperatures in VIRR channels change from 270 K to 300 K under a normal condition, the AIRS-VIRR temperature adjustment linearly varies from -0.79 K to -2.32K for VIRR channel 4, from 0.14 K to -1.42 K for VIRR channel 5, respectively.

Soil moisture saturation index (SMSI) is an important indicator that demonstrates the status of the soil water content for drought monitoring. However, at present, most of the methods to calculate the SMSI from the in situ measurement data are inadequate or inaccurate. This paper proposed a simple method to determine the SMSI from the remotely sensed data. Combining the theory of thermal inertia and triangle method, the apparent thermal inertia and fractional vegetation cover can construct a triangular space. In this space, SMSI can be determined easily. Validation was performed with in situ measurements for 19 meteorological stations in the study area. Results indicated that the method can obtain the accurate soil water status that reflects the variation in soil moisture to some extent and is suitable for monitoring the regional surface soil moisture.

Land surface temperature and emissivity separation is a critical process for land surface temperature (LST) retrieval from hyperspectral thermal infrared data. This paper compared the iterative spectrally smooth temperature/emissivity separation (ISSTES) and the correlation based temperature/emissivity separation (CBTES) methods for land surface temperature and emissivities retrievals from simulated data under typical atmospheres and different land surface covers. The paper also compared both methods for retrieving low emissivities with simulated data. For typical land cover types, neglecting the instrumental noise, ISSTES is more accurate than CBTES with root mean square error (RMSE) of LSTs less than 0.0005K for the ISSTES and 0.1K for the CBTES. For low emissivity material, considering instrumental noise, both methods have large errors, but the CBTES performs much better.

Land surface microwave emission is mainly a function of soil moisture and surface roughness. However, the relationship between vertical and horizontal polarization land surface emissivities is not fully understood. This study attempts to develop a parameterized relationship to relate the emissivities at different polarizations for bare surfaces. A microwave emission database is simulated for bare surfaces with a wide range of surface roughness and dielectric properties using the Dobson model and the Advanced Integral Equation Model (AIEM) at 10.65 GHz under the configuration of the Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E). By analyzing the factors that influence microwave emission, parameterized relationships between vertical and horizontal polarization emissivities are established. With the proposed relationships, the effects of soil moisture and surface roughness on the soil microwave emission signal can be separated. Simulated results using the proposed relationships are compared with those of the AIEM. These results show that the proposed relationships are accurate, with absolute root mean square errors (RMSEs) of 0.0025, and they can be used as a reliable boundary condition to retrieve other surface geophysical parameters. Combining this relationship with the calculated soil moisture, the RMSE of the estimated soil moisture is 0.44% using simulated data. As an example, observations of AMSR-E are used to estimate the variation in soil moisture in Saharan Africa in 2004. By comparing with independent soil moisture data, the result shows that the proposed relationship is promising for retrieving surface geophysical parameters from microwave observations.

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.

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

The crowning objective of this research are to analyze precipitation character of Cb for different dynamical characters in Huai river basin(HRB) with China's first operational geostationary meteorological satellite FengYun-2C (FY-2C) data. Firstly, 5 cloud patch dynamic parameters with respect to life stage and moving parameters are derived based on the Cb tracking method the author has proposed by combing artificial neural network (ANN) cloud classification[1], and cross-correlation-based approach to track Cb patch motion. Secondly, Cb precipitation over different life cycles and motion characters are analyzed. The result shows that: 1) Rain probability has a similar variation to rain rate, and rain rate is generally not more than 6 mm/hour, and probability is randomly higher than 50%. 2) Both rain rate and probability of single Cb is lower than that of complicated Cb which involves cell-merger and cell-split of some minor Cb patches. 3) Motion features such as horizontal moving speed of cloud patch (HMSP), horizontal moving direction of cloud patch (HMDP), and vertical moving character of cloud patch (VMCP) have no obvious impact on rain.

To resolve uncertainties in evapotranspiration (ET) estimates caused by the retrieval error of remotely sensed data, this study develops an evaporative fraction (EF) parameterization based on surface energy balance and the assumption of generally invariant EF during the daytime. EF is deduced as a function of temporal change of surface temperatures, temporal change of air temperature, temporal change of net radiation, and fractional vegetation cover. The EF parameterization is evaluated by the simulated data from a soil-vegetation-atmosphere transfer model with a coefficient of determination (R2) of 0.786 and a root mean square error (RMSE) of 0.117. When the EF parameterization is used to estimate the daily ET of the Yucheng station in North China by in situ measurements, the estimated results are acceptable with an RMSE of 0.7 mm (relative RMSE of 25%) and an R2of 0.837.

Land surface temperature and emissivity separation (TES) is a key problem in thermal infrared remote sensing. Current TES methods were proposed and succeeded to apply for the retrieval of land surface temperature and emissivity for the materials with emissivity close to 1. This work addressed the performance of linear spectral emissivity constraint (LSEC) method proposed by wang et al. (2011) for the TES of hyperspectral TIR data for contrast samples (high- and low- emissivity materials). The simulated hyperspectral TIR data are used for analysis and generated with six MODTRAN standard atmospheric profiles by hyperspectral atmospheric radiative transfer model (4A/OP). The influence of initial emissivity estimation is considered in this paper. The results show that initial emissivity estimation has a great impact on the performance of LSEC. LSEC method performs a fairly good result when the initial emissivity is close to the true value, and the RMSEs of temperature and emissivity are smaller than 0.5K and 0.01 when initial emissivity is good. However, the performance is worst when the initial emissivity has a great deviation.

In this paper, great efforts are focused on the temperature and emissivity separation (TES) from hyperspectral thermal infrared data. However, instead of proposing new method, the performances of several published TES methods, including iterative spectrally smooth temperature emissivity separation method (ISSTES), automatic retrieval of temperature and emissivity using spectral smoothness method (ARTEMISS), spectral smoothness method (SpSm), downwelling radiance residual index method (DRRI) and linear spectral emissivity constraint method (LSEC) are analyzed under different instrument characteristics, including the shifting of spectral and the broadening of the full-width half-maximum (FWHM), with the simulated data. The results shows that LSEC has the most robust and accurate performance. DRRI also has a good performance, but a channel selection procedure is required before the use of this method. ISSTES, ARTEMISS and SpSm are more sensitive to the instrument characteristics with some larger errors than other two methods.

This paper proposed two methods to estimate the instantaneous downwelling surface longwave radiation (RL, D) using the MODIS measurements observed at the top of the atmosphere (TOA) over the Tibetan plateau region for clear-sky conditions. One is the method proposed by [2] and refined in this work, and the other is an artificial neural network (ANN) method. The upwelling surface longwave radiation (RL, U) was estimated using the Stefan-Boltzmann law with MODIS surface temperature/emissivity products (MOD11_L2). The two methods are all based on the atmospheric transfer simulation. The net surface longwave radiation (Rn, l) can then be obtained by differing the RL, Dand the RL, U. The results showed that the RMSEs of the estimated RL, Dand measured RL, Dwith the first method are smaller than those of with the ANN method for the sites over the Tibetan plateau region.

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.

Soil moisture (SM) is a major concern in the earth science. In previous studies, in the process of retrieval of SM from remote sensing data, surface roughness effects on the retrieval of SM is obvious and a priori knowledge of surface roughness is necessary. In this paper, a simple method to retrieve SM from passive microwave data is proposed. Using the proposed method, surface roughness effects on soil moisture retrieval can be reduced. Result of sensitivity analysis shows it can be a promising method to retrieve SM. Both simulated data and actual data have been used to retrieve SM with the proposed method in this work. The result shows that the SM can be obtained with a RMSE of 1.14% from the simulated data and 1.7% from actual data.

This study firstly analyses the spatial representation of LAS (Large Aperture Scintillometer)-observed heat fluxes for a complex surface; and then evaluates the performance of SEBS model applied to a complex surface in comparison with in situ measurements. The results showed that LAS observation is indeed more stable than EC measurements even for complex surfaces, and the sensible heat flux from LAS is less than that from EC observations because of some land types with more evapotranspiration included into LAS footprint. SEBS overestimated latent heat flux at QYZ station in southern China because of the underestimation of H, but SEBS-estimated turbulent fluxes are more consistent with the LAS measurement.

This work addressed the estimation of the directional reflectance in Middle Infra-Red (MIR) channel from the data acquired by the Stretched Visible and Infrared Spin Scan Radiometer (SVISSR) onboard Chinese geostationary Meteorological satellite FengYun 2C (FY-2C). SVISSR/FY-2C sensor acquires image covering the whole disk with a temporal resolution of 30 minutes. The MIR directional reflectance retrieval procedure can be seen as follows. Firstly, the atmospheric profiles data provided by European Centre for Medium-Range Weather Forecasts (ECMWF) were used to correct atmospheric influence with the radiative transfer code (MODTRAN 4.0). Secondly, the bi-directional reflectance in SVISSR/FY-2C MIR channel 4 (3.8 micron) was estimated from the combined MIR and TIR channel with day-night SVISSR/FY-2C data. Finally, a BRDF model referred to as the RossThick-LiSparse-R model was used to estimate the directional reflectance in MIR channel from the time-series bi-directional reflectance data. The results have been demonstrated that the method can be applied well to estimate the directional reflectance in MIR channel of SVISSR/FY-2C sensor.

In this paper, an improved method is proposed to simultaneously retrieve land surface temperature (LST), emissivity (LSE) and atmospheric profiles. This method employed the linear spectral emissivity constraint to efficiently reduce the number of retrieved variables. The proposed method was validated with some simulations. The initial guesses were derived from a neural network model. This method could greatly improve the accuracies of LST, LSE and atmospheric profiles. The RMSE of LST was decreased from 5.12 K (the initial guesses) to 1.59 K (the physical retrieved). The retrieved emissivity spectrum was in good agreement with the actual spectrum. An improvement of 1K in the tropospheric temperature was also been found. Those results showed that the proposed method is capable of improving the retrieval accuracies of land surface and atmospheric parameters with the remotely sensed thermal infrared data.

An operational statistical method suitable for nearly real-time estimate of land surface and atmospheric parameters was developed and applied to the Infrared Atmospheric Sounding Interferometer (IASI) observations. The proposed method utilized three steps to solve the ill-posed problems and to stabilize the solution in a fast speed regression manner: 1) the atmospheric profiles and land surface emissivity spectra were expressed by their eigenvectors to reduce the number of unknowns; 2) a ridge regression procedure was introduced to improve the conditioning of the problem and to lessen the influence of noises; 3) a set of optimal channels was selected to decrease the effect of forward model errors or uncertainties of trace gases, and to increase computational efficiency. The retrieval results using the independent simulated data indicate the proposed method is promising. The root mean squared error (RMSE) of land surface temperature is 3.5 K, the RMSE of land surface emissivity at the selected channels is 0.01, and the RMSE of atmospheric temperature and moisture profile are about 2.0K and 0.001g/g, respectively.

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

Total precipitable water (TPW) is an important atmospheric parameter in many applications. A method was proposed to estimate TPW from thermal infrared hyperspectral data. First, 21 channel groups were selected to retrieve TPW. Then, two indices, namely, the differenceand the ratio-depth in each channel group, were used as the measurement of the water vapor absorption. By multivariate regression, the relationship between the TPW and the indices was established. Finally, this relationship was applied to the simulated thermal infrared hyperspectral data. Results showed that the root mean square error (RMSE) of the model is 0.102 g·cm-2, and the relative error is 8.1%. The proposed method needs to be further refined in the future work, including the complete elimination of the Earth's emission in the retrieval.

Owing to the ill-posed problem of radiometric equations, the separation of land surface temperature (LST) and land surface emissivity (LSE) from observed data has always been a troublesome problem. On the basis of the assumption that the LSE spectrum can be described by a piecewise linear function, a new method has been proposed to retrieve LST and LSE from atmospherically corrected hyperspectral thermal infrared data using linear spectral emissivity constraint. Comparisons with the existing methods found in literature show that our proposed method is more noise immune than the existing methods. Even with a NEΔT of 0.5 K, the rmse of LST is observed to be only 0.16 K, and that of LSE is 0.006. In addition, our proposed method is simple and efficient and does not encounter the problem of singular values unlike the existing methods. As for the impact of the atmosphere, the results show that our proposed method performs well with the uncertainty of the atmospheric downwelling radiance but suffers from the inaccuracy of the atmospheric upwelling radiance and atmospheric transmittance, which implies that an accurate atmospheric correction is still needed to convert the radiance measured at the satellite level to the at-ground radiance. To validate the proposed method, a field experiment was conducted, and the results show that 80% of the samples have an accuracy of LST within 1 K and that the mean values of LSE are accurate to 0.01.

Land Surface Temperature (LST) is a major concern in the earth science. Accurately retrieving LST from passive microwave data will promote work in many other research fields. In this paper, a simple linear relationship is developed to relate the microwave surface emissivities at vertical and horizontal polarizations for the channels of Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E) instrument. On the basis of this relationship and the radiative transfer equation, a method is also proposed to derive directly LST from AMSR-E data, provided that the volumetric soil moisture and atmospheric quantities are known or can be estimated a prior. The preliminary validation results indicate that LST can be obtained with a RMSE of 1.4 K from the simulated data with NEΔT=1.0 K, as for the actual AMSR-E data over the desert region, compared with MODIS LST product, the proposed method can give an estimation of LST with a RMSE of 4.9 K.

This paper makes an attempt to address the scaling problem of Leaf Area Index (LAI) and to analyze the propagation of scaling effect of LAI. On the basis of the Taylor series expansion and following the general scaling procedure, it is demonstrated that the magnitude of the scaling effect is the product of the degree of the non-linearity of the retrieval model and the spatial heterogeneity of input variables involved in this model. Finally, a scaling correction model is proposed to correct for the scaling effect of LAI. The validation using the simulated data indicates that the proposed scaling correction model of LAI gives promising accuracy if the spatial heterogeneity is well characterized by its wavelet variance. The RMSE and relative error of retrieved LAI induced by the scale effect can be greatly reduced after scaling correction. The scaling propagation analysis of LAI reveals that the scaling effects caused by several non-linear components may compensate for each other, which would enhance our confidence in using LAI product over heterogeneity areas.