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Evapotranspiration (ET) is an important component of surface energy balance and a key process of hydrological cycle. Continuous and accurate estimation of regional ET is meaningful for water management, agricultural production, and climate change adaptation. Remote sensing (RS) technique is of important meaning for estimating ET on regional scale. In this study, a new method is developed to integrate the RS information and meteorological data based on the decouple model which is deduced from Penman–Monteith (PM) model. Firstly, a parameterization method for decoupling factor (Ω) which is key parameter in the decouple model and could reflect the coupling relationship between the land surface and atmosphere systems is introduced. Subsequently, the decouple model is applied to inverse regional ET in Beijing City and nearby region, from 1st, May, 2013 to 31th, October, 2020. Thirdly, the inversed daily ET values are evaluated based on the measured data at Huailai station and in Badaling Forest Farm. The results show that the changing trend of estimated ET and measured ET are basically the similar. The bias value is −0.12 mm d−1, the RSME value is 0.66 mm d−1, and the correlation coefficient is 0.52. Finally, the spatiotemporal trend of water consumption in study area is analysed based on the ET estimation results. The month with the largest water consumption in 2013 is July with the quantity of 0.92 billion m3, accounting for about 22% of the regional total water consumption in growing season. The regions with high ET values agree well with the regions of high soil moisture and high air humidity as well as high biomass. These results suggest that the decouple model is with great potential to generate regional daily ET products and to support irrigation management. [ABSTRACT FROM AUTHOR]

Daily mean land surface temperature (LST) is a crucial indicator for investigating global long-term climate change. The polar-orbiting thermal infrared sensor (POTIRS) can provide limited instantaneous LSTs of a single day on a global scale. Several studies have developed algorithms to derive daily mean land surface temperature (DMLST) from limited instantaneous daily LST data, but these methods are restricted to using data from a single POTIRS after 2000. This study presents a generalized method for estimating global DMLST utilizing one daytime and one night-time observation from any POTIRS since 1981. The proposed method employs a simple linear regression of two instantaneous LST measurements from different observation times (once during the day and once at night) of POTIRSs based on in situ LST measurements from 227 flux stations operating in diverse climate regions globally. The results demonstrate that this simple linear regression model provides highly accurate estimates of DMLST under all-weather conditions, with a root mean square error (RMSE) value lower than 1.7 K. Additionally, the proposed method was employed to estimate DMLST from instantaneous LST products obtained from various sensors, including Moderate Resolution Imaging Spectroradiometer aboard the Terra satellite, Advanced Very High Resolution Radiometer aboard the polar-orbiting National Oceanic and Atmospheric Administration satellite, and Meteorological Operational satellite. Validation results indicate that the DMLSTs estimated from these POTIRS products are in close agreement with the daily mean in situ LST, with RMSE values varying from 2.2 to 2.4 K. We expect that this generalized method will be useful for generating long-term and high-quality DMLST datasets. [ABSTRACT FROM AUTHOR]

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

Multitemporal remote sensing data, especially those for key phenological periods, play an important role in crop classification. However, cloudy/rainy climate conditions can easily lead to a lack of valid optical data, leading to crop classification difficulties. A general solution is taking advantage of all-weather synthetic aperture radar (SAR) datasets. In practice, SAR and optical datasets are often applied in the agricultural field by the method of image fusion, but it is difficult to apply when the number of optical images is too small. To solve this problem, this research proposes a data-transfer and feature-optimize-based method, which deploy an RNN-based encoding-decoding network to add additional data to the 'optical' temporal features at the farmland parcel scale and improve the utilization of optical fragments. On the basis of this method, we mitigate inconsistencies in spatial scale among different datasets and optimize the time-series parameters without expert knowledge in the crop classification procedure. The experimental results illustrate the crop classification accuracy of this method, which achieves a 4.1% improvement over the traditional approach and is especially effective for dryland crops (e.g. corn and rapeseed). Thus, this research demonstrates the effectiveness of the combined use of optical and SAR data for similar applications in cloudy/rainy mountainous areas. [ABSTRACT FROM AUTHOR]

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

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

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

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

Land surface temperature (LST) and land surface emissivity (LSE) are two key parameters in global climate study. This article aims to cross-validate LST/LSE products retrieved from data of the Spinning Enhanced Visible and Infrared Imager (SEVIRI) on board the first geostationary satellite, Meteosat Second Generation (MSG), with Moderate Resolution Imaging Spectroradiometer (MODIS) LST/LSE version 5 products over the Iberian Peninsula and over Egypt and the Middle East. Besides time matching, coordinate matching is another requirement of the cross-validation. An area-weighted aggregation algorithm was used to aggregate SEVIRI and MODIS LST/LSE products into the same spatial resolution. According to the quality control (QC) criterion and the view angle, the cross-validation was completed under clear-sky conditions and within a view angle difference of less than 5° for the two instruments to prevent land surface anisotropic effects. The results showed that the SEVIRI LST/LSE products are consistent with MODIS LST/LSE products and have the same trend over the two study areas during both the daytime and the night-time. The SEVIRI LST overestimates the temperature by approximately 1.0 K during the night-time and by approximately 2.0 K during the daytime compared to MODIS products over these two study areas. The SEVIRI LSE underestimates by about 0.015 in 11 μm and by about 0.025 in 12 μm over the Iberian Peninsula. However, both LSEs agree and show a difference of less than 0.01 over Egypt and the Middle East. [ABSTRACT FROM AUTHOR]

Land surface soil moisture (SSM) is a fundamental variable in the hydrological cycle and is an important parameter in investigations on water and energy balances at the Earth's surface. Many efforts have been made to derive SSM from remotely sensed thermal infrared data. Using the Noah land surface model (LSM) and the Gaussian emulation machine for sensitivity analysis (GEM-SA) software, a sensitivity study was conducted for bare soil to investigate the interrelationship between the evolution of land surface temperature (LST) and SSM. Based on the diurnal cycles of LST and net surface shortwave radiation, eight parameters intuitively related to SSM were defined, and a sensitivity analysis (SA) was performed in the presence and absence of atmospheric variation. The results provided insight into the relationships between the eight parameters and various environmental factors such as soil physical parameters, soil moisture, albedo, and atmospheric parameters. For instance, the results suggested that the surface air temperature had a significant effect on the LST, especially the maximum, minimum, and average daytime temperatures. For a given atmospheric forcing data set, the LST rising rate normalized by the difference in the net surface shortwave radiation during the mid-morning (TN) was the parameter most sensitive to the SSM, contributing 80.72% to the total variance. In addition, the time at which the daily maximum temperature occurred (td), the daily minimum temperature, and the LST nocturnal decay coefficient were strongly related to the soil type. Using a linear combination ofTNandtd, a method was proposed to retrieve the SSM, and the coefficients of the linear model were found to be independent of the soil type for a given atmospheric condition. Compared with the actual SSM values used in the Noah LSM simulation, the root mean square error (RMSE) of the SSM retrieved from our proposed method was within 0.04 m3m−3for all the 20 clear days evaluated in the present study. [ABSTRACT FROM AUTHOR]

This article compares one- and two-source energy balance (OSEB and TSEB) models in the estimates of surface energy components using Landsat imagery and surface measurements acquired from an experimental field at Yucheng Station in Northern China. Compared to surface measurements, similar performance between the TSEB and OSEB models has been observed for estimated surface net radiation and soil heat flux. The root mean square difference (RMSD) is within 14–39 W m−2in both the TSEB and OSEB models. The residual energy (ER) correction method yields the best agreement in comparisons of the sensible (H) and latent (LE) heat fluxes estimated using both the TSEB and OSEB models to the eddy covariance (EC) system measurements. The TSEB model is shown to greatly outperform the OSEB model in reproducing surfaceHandLEmeasurements. Cirrus clouds are likely responsible for the surface temperature retrieved from the enhanced thematic mapper plus (ETM+) sensor being lower than air temperature on days of the year (DOYs) 178 and 218 of 2009. This atmospheric stability is contrary to the unstable atmosphere that the EC measurements observe. If data on these two days are excluded and theERcorrection method is applied, when comparing the estimatedHandLEto the EC measurements, RMSD is within 55 W m−2in the TSEB model and is larger than 97 W m−2in the OSEB model. [ABSTRACT FROM AUTHOR]

This paper presents a new algorithm to determine quantitatively column water vapour content ( W ) directly from ATSR2 (Along-Track Scanner Radiometer) Split-Window radiance measurements. First, the Split-Window Covariance-Variance Ratio (SWCVR) method is reviewed. The assumptions made to derive this method are highlighted and its applicability is discussed. Then, an operational use of this method is developed and applied to several ATSR2 datasets. The water vapour contents retrieved using ATSR2 data from SGP'97 (USA), Barrax (Spain) and Cabauw (The Netherlands) are in good agreement with those measured by the quasi-simultaneous radiosonde. The mean and the standard deviation of their difference are 0.04 g cm -2 and 0.22 g cm -2 , respectively. It is shown that water vapour content derived from ATSR2 data using the proposed algorithm is accurate enough in most cases for surface temperature determination with a split-window technique using ATSR2 data and for atmospheric corrections in visible and near-infrared channels of ATSR2. [ABSTRACT FROM AUTHOR]

Soil temperature (Ts) and vegetation temperature (Tv) provide significant information for various practical applications such as evapotranspiration estimation, water requirement analysis for vegetation, and drought monitoring during the crop growth. In this study, we estimated Ts and Tv with a land surface temperature (LST)-vegetation index (VI) trapezoidal space-based method (hereafter abbreviated as the trapezoidal method) and a dual-angle algorithm in which the estimation equations for Ts and Tv were established with thermal infrared observations from two angles and solved to using the least squares method. The Ts and Tv estimations were conducted using Moderate Resolution Imaging Spectroradiometer (MODIS) and Sentinel-3 data from the Google Earth Engine platform, together with auxiliary meteorological data from eight sites with various land cover types in the Heihe River Basin in 2019, and compared with corresponding in-situ measurements and temperatures from Landsat. The results showed that (1) both estimation methods outperformed the Ts retrievals compared to those of Tv; (2) the trapezoidal method overall had a similar but slightly better performance compared to the dual-angle algorithm, with the root mean square error for Ts retrievals of 3.7 K and 5.7 K, and for Tv retrievals of 6.5 K and 7.2 K, respectively, in comparison with temperatures from Landsat; (3) the trapezoidal method had higher accuracy over sites with grassland, desert, or meadow for Ts retrieval and sites with forests for Tv retrieval. This study provides advice about the method for Ts and Tv estimations and might be useful for rational utilization and more accurate monitoring of soil and vegetation. [ABSTRACT FROM AUTHOR]

Gardens plays an important role in mitigating effects on the wind-heat environment, and their scientific planning and layout are of great significance to the improvement of the urban ecological environment and the local microclimate of the city. However, there are few studies of quantitative analysis of the impact of landscaping and their layouts on the urban wind-heat environment. Therefore, taking Suzhou Humble Administrator's Garden, Beijing Summer Palace, Suzhou Jingsi Garden and Beijing Taoranting Park as study area, we carried out a study of 3D spatial wind and thermal environment simulation of classical gardens and modern gardens based on high-resolution remote sensing and computational fluid dynamics (CFD). The mitigating effects including cooling intensity and the maximum mitigation distance are calculated. By analysing the overall of mitigating effects of classical/modern gardens on the surrounding thermal environment, we found that, compared with the shape index, the water area and the area of green planting are the main factors affecting the wind and heat environment.Comparing the quantitative simulation results, it was found that the maximum cooling value of the Summer Palace was 2°C higher than that of Taoranting Park by 1°C. The cooling distance in the downwind direction of the Summer Palace is as high as 1 km, which is 800m longer than that of Taoranting Park. Comparing the influence of the spatial layout differences of modern gardens, the results show that the cooling effect of Taoranting Park in Beijing is better than that of Jingsi Garden in the south. The maximum cooling value of the Jingsi Garden is only 0.2°C, and the cooling distance in the downwind direction is 200m. [ABSTRACT FROM AUTHOR]

Automatically extracting roads from remote sensing images is increasingly important for autonomous driving and geographic information systems. However, due to factors such as ground objects blocking road boundaries and some roads being narrow and long in high-resolution remote sensing images, it is difficult for the current mainstream pixel-level extraction model to guarantee the continuity of roads and the smoothness of boundaries. To solve this problem, this paper designs a neural network Rse-net based on semantic segmentation, using a two-stream semantic segmentation network algorithm to make the network pay more attention to the boundary information of roads and narrow roads. This structure uses shape stream to process road boundary information (roads’ shape stream), and processes it in parallel with classic stream. Use the Gated Shape CNN to connect the two streams of roads’ shape stream and classic stream, and use the classical stream to optimize the boundary information in the shape stream. At the same time, a multi-scale convolutional attention mechanism is used between the decoder and the encoder to expand the receptive field through large-core attention, and obtain more semantic information without causing too much calculation. Finally, the effectiveness of the proposed network is verified by comparative experiments. [ABSTRACT FROM AUTHOR]

Rapid urbanization has led to many urban thermal environment problems. Most studies focus on analysing the urban thermal environment from the perspective of land-use type, and often at a large scale. Thus, research of urban spatial thermal environment at a block scale with different urban functional zone (UFZ) becomes the focus of this paper. UFZ refers to urban planning units with similar socio-economic functions, which often have similar energy consumption and outdoor thermal environment. In this paper, the urban weather generator (UWG) model is used to quantitatively analyse the spatial thermal environment of Beijing. To accurately simulate the urban thermal environment, the underlying surface used in the simulation is replaced by the finely classified underlying surface of the urban functional area. In this paper, we first use POI + OSM data to divide the functional areas of Beijing. The recognition accuracy of functional area is 85.6%. Then, the UWG model is used to simulate the temperature and humidity differences in different functional areas, and finally the corresponding urban and rural weather stations were used to validate its accuracy. The results show that the model calibration method can effectively improve the simulation accuracy of UWG thermal environment, the R2 is increased by about 0.12, and the RMSE is reduced by about 0.84. The canopy temperature of different urban functional areas from high to low is: industrial area>commercial area>residential area>public area>road and traffic area>green space and square area. This model is more suitable for areas with relatively homogenous urban morphology and sparse urban vegetation. The thermal environment simulation results of the same urban functional area are basically the same, with slight differences in the temperature peaks, and the average temperature in the city centre is higher than that in the suburbs. [ABSTRACT FROM AUTHOR]

The current research on urban heat island (UHI) effect mostly focuses on the analysis of land use type changes and the surface UHI intensity. Few studies on the urban canopy heat island effect at a block scale of local climate zone (LCZ), although the canopy heat island effect is a key factor affecting human thermal comfort. Therefore, this study will combine the LCZ classification system and the urban weather generator (UWG) model to simulate and quantitatively analyse the urban canopy heat island effect in Beijing at the block scale. First, based on Sentinel-2 Multispectral remote sensing images, the residual neural network (ResNet) method was used to obtain the LCZ of Beijing, and the results of LCZs was validated based on the google earth engine (GEE). Then, according to the classification results of local climate zones, the input parameters of the UWG and their corresponding value ranges are calculated. Finally, the UWG model is used to simulate the canopy temperature in different local climate zones, and the urban canopy temperature is validated using the meteorological station dataset. We quantitatively analyse the temperature differences between different types of LCZs. The results shows that the canopy heat island effect in Beijing gradually weakened outward from the city centre. This is mainly due to the relatively dense distribution of compact local climate zones in the centre of Beijing, while the surrounding areas of Beijing have lower building density and better natural coverage. The canopy urban heat island intensity of built-up LCZs is significantly stronger than that of natural-covered LCZs. The heat island intensity of the compact LCZ is higher than that of the open LCZ with the same building height. However, for LCZs with comparable compactness, the heat island intensity of high-level LCZs is higher than that of low-level LCZs. [ABSTRACT FROM AUTHOR]

This article presents a methodology to quantitatively extract the solar-induced fluorescence (SIF) using the canopy reflectance index. The sensitivity analysis was conducted with a spectral vegetation Fluorescence Model (FluorMOD), and the results demonstrate that Sun zenith angle (θ), fluorescence quantum efficiency (Fi), leaf inclination distribution function (LIDF), leaf temperature (T), leaf area index, and leaf chlorophylla + bcontent (chl-a+b) had large effects on the fluorescence radiance at 761 nm (LF,761). Based on the results of the sensitivity analysis, the input parametersθ, Fi, LIDF,T, and chl-a+bvaried within a certain range during the generation of the simulated data. Based on the simulated data,R740/R630,R685/R850, andR750/R710were thought to be the best candidates to extract the fluorescence radiation. The quantitative relationships between the fluorescence retrieved byR740/R630,R685/R850, andR750/R710and LF,761 were analysed and expressed as functions ofθ, Fi,T, and reflectance index. The correlation coefficients (r) between the fluorescence retrieved usingR685/R850,R740/R630, andR750/R710and LF,761 are 0.94, 0.95, and 0.95, respectively, and the root mean square errors (RMSEs) were 0.32, 0.29, and 0.30 W m−2 μm−1 sr−1, respectively. Through comparison with FLD and 3FLD, the method presented in this article yielded better results, and could be used to estimate the fluorescence. This methodology provides new insights into the quantitative retrieval of SIF from the reflectance spectrum. [ABSTRACT FROM PUBLISHER]

To retrieve surface soil moisture (SSM) content over natural surfaces quantitatively, the effects of vegetation and soil texture on a previously developed bare SSM retrieval model are evaluated using simulated data from the common land model (CoLM). The results indicate that (1) both the accuracy and the five model parameters of the previous SSM retrieval model show relatively consistent variations when the fractional vegetation cover (FVC) varies from 0 to 0.7; and (2) the SSM exhibits a generally significant and exponential relationship with the rotation angle when the clay content is lower than 30%, with the FVC ranging from 0 to 0.7. These findings make it possible to estimate SSM directly under the conditions that the underlying surface is in the presence of spatially variable FVC and soil texture. On this basis, we further confirm the feasibility of using the previous bare SSM retrieval model to estimate SSM for FVC varying from 0 to 0.7 with a clay content lower than 30%. For the simulated data on eight cloud-free days, the total root mean square error (RMSE) of the retrieved SSM and the coefficient of determination (R2) are 0.033 m3m−3and 0.758, respectively. Ultimately, a preliminary validation is conducted using the ground measurements at the Bondville site; anR2 = 0.328 and a RMSE = 0.058 m3m−3are obtained for 14 cloud-free days. [ABSTRACT FROM PUBLISHER]

Land surface soil moisture (SSM) is crucial to research and applications in hydrology, ecology, and meteorology. To develop a SSM retrieval model for bare soil, an elliptical relationship between diurnal cycles of land surface temperature (LST) and net surface shortwave radiation (NSSR) is described and further verified using data that were simulated with the Common Land Model (CoLM) simulation. In addition, with a stepwise linear regression, a multi-linear model is developed to retrieve daily average SSM in terms of the ellipse parametersx0(horizontal coordinate of the ellipse centre),y0(vertical coordinate of the ellipse centre),a(semi-major axis), andθ(rotation angle), which were acquired from the elliptical relationship. The retrieval model for daily average SSM proved to be independent of soil type for a given atmospheric condition. Compared with the simulated daily average SSM, the proposed model was found to be of higher accuracy. For eight cloud-free days, the root mean square error (RMSE) ranged from 0.003 to 0.031 m3 m−3, while the coefficient of determination (R2) ranged from 0.852 to 0.999. Finally, comparison and validation were conducted using simulated and measured data, respectively. The results indicated that the proposed model showed better accuracy than a recently reported model using simulated data. A simple calibration decreased RMSE from 0.088 m3 m−3to 0.051 m3 m−3at Bondville Companion site, and from 0.126 m3 m−3to 0.071 m3 m−3at the Bondville site. Coefficients of determinationR2 = 0.548 and 0.445 were achieved between the estimated daily average SSM and the measured values at the two sites, respectively. This paper suggests a promising avenue for retrieving regional SSM using LST and NSSR derived from geostationary satellites in future developments. [ABSTRACT FROM AUTHOR]

In this article, land surface temperature (LST) and sensible heat flux (H) data assimilation schemes were developed separately using the ensemble Kalman filter (EnKF) and the common land model (CoLM). Surface measurements of ground temperature,H, and latent heat flux (LE) collected at the Yucheng (longitude: 116° 36′ E; latitude: 36° 57′ N) and Arou (longitude: 100° 27′ E; latitude: 38° 02′ N) experimental stations were compared with the predictions by assimilating different observation sources into the CoLM. The results showed that both LST andHdata assimilation schemes could improve the estimation of ground temperature andH. The root mean square error (RMSE) compared between the predictions andin situmeasurements decreased more significantly with the assimilation of values ofHmeasured by a large aperture scintillometer (LAS). Assimilating Moderate Resolution Imaging Spectroradiometer (MODIS) LST only slightly improved the predictions ofHand ground temperature. Daytime to night-time comparison results using both assimilation schemes also indicated that accurately quantifying model, prediction, and observation error would improve the efficiency of the assimilation systems. The newly developed land data assimilation schemes have proved to be a feasible and practical method to improve the predictions of heat fluxes and ground temperature from CoLM. Moreover, integrating multisource data (LAS and MODIS LST) simultaneously into the land surface model is believed to result in an efficient and robust way to improve the accuracy of model predictions from a theoretical point of view. [ABSTRACT FROM AUTHOR]

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

Optimization techniques are often used in remote sensing retrieval of surface or atmospheric parameters. Nevertheless, different algorithms may exhibit different performances for the same optimization problem. Comparison of some classic optimization approaches in this article aims to select the best method for retrieving aerosol opacity, or even for other parameters, from remotely sensed data. Eight frequently used optimization algorithms were evaluated using both simulated data and actual AATSR (advanced along track scanning radiometer) data. Several typical land cover types and aerosol opacity levels were also considered in the simulations to make the tests more representative. It was observed that the absolute error in retrieval would rise after a certain number of iterations due to the round-off error, and the algorithms showed different performances in the inversions without any a priori knowledge. When combined with reasonable a priori knowledge, the selection of various algorithms only slightly affected the retrieval accuracy. Given a summary of all the comparison tests, a special class named ‘trust-region methods’ (TR) was demonstrated to be the optimal choice in general cases. In contrast, some widely used optimization methods in aerosol research, for example, the Levenberg–Marquardt (LM) algorithm, seemed not to display a persuasive performance. [ABSTRACT FROM AUTHOR]

Inversion is an important process in remote sensing. To estimate the accuracy of retrieved parameters before inversion, we defined an Uncertainty and Sensitivity Ratio in the inversion of remote sensing models. It is a ratio of the priori uncertainty and sensitivity of one input parameter in the forward model to the sum of all parameters' uncertainty and sensitivity. USR was shown to reflect the variance of the retrieved parameter by error propagation theory. Subsequently, we took numeric experiments to illustrate the property of USR for typical vegetation cover scenes. Forty-eight multiangular datasets of Bidirectional Reflectance Factors were generated for these scenes by adding random noise to the Light Scattering by Arbitrarily Inclined Leaves model output. Seven parameters were retrieved for each scene. The results suggested that the Mean Square Error of the inversion results is highly correlated with USR. Based on this ratio, we selected two different angular observations in red and near-infrared (NIR) band to get a new Normalized Difference Vegetation Index (NDVI), which could produce less uncertainty to retrieve the Leaf Area Index (LAI) than using the conventional single-angular observations. The new NDVI was tested by retrieving LAI using the simulated multispectral and multiangular datasets. It was found that the inversion results of LAI are more accurate by using this new NDVI than using the traditional one. [ABSTRACT FROM AUTHOR]

Daily actual evapotranspiration over the upper Chao river basin in North China on 23 June 2005 was estimated based on the Surface Energy Balance Algorithm for Land (SEBAL), in which the parameterization schemes for calculating the instantaneous solar radiation and daily integrated radiation were improved by accounting for the variations in slope and azimuth of land surface and terrain shadow in mountainous areas. The evapotranspiration (ET) estimated from satellite data in this study for the whole watershed ranges from 0 mm to 7.3 mm day-1 with a mean of 3.4 mm day-1, which was validated by Penman-Monteith approaches for water body and paddy land. The comparison of ET estimates for a wide range of land cover types reflected distinct mechanisms of energy partition and water removal of various land cover types, showing differences in the spatial distribution pattern of ET, which could be not only the reflection but also the driving force of advection and local circulation that may violate the surface energy balance equation in the vertical direction. The spatial variation in daily solar radiation and ET estimates under the complex terrain of forest land were elaborated and evaluated by exploring the relationship between ET estimates and elevations for wood land and grass land. In addition, the utility and limitations of SEBAL's applicability to watersheds with various land cover types and complex terrain were analysed. [ABSTRACT FROM AUTHOR]

This study aims to preliminarily validate two newly developed temporal parameter-based surface soil moisture (SSM) retrieval models, namely the mid-morning model and daytime model, using both microwave satellite soil moisture product and in situ SSM measurements over a well-organized soil moisture network named REd de MEDición de la HUmedad del Suelo (REMEDHUS) in Spain. Ground SSMmeasurements and geostationary satellite observations were primarily implemented to obtain the model coefficients for the two SSM retrieval models for each cloud-free day. Thesemodel coefficients were subsequently used to estimate SSM using the Meteosat Second Generation products over the study area. Preliminary verification using both a satellite product and in situ SSMmeasurements demonstrated that SSMvariation can be well detected by both SSM retrieval models. Specifically, a generally similar accuracy (coefficient of determination R²: 0.419-0.379, root mean square error: 0.046-0.051m³m-3, Bias: -0.020 to -0.025m³m-3) was found for the mid-morningmodel and the daytimemodel with the microwave missions based climate change initiative SSM product, respectively. Moreover, except for the comparable R² (0.614-0.675), a better accuracy (Bias: 0.032-0.044 m³m-3, RMSE: 0.043-0.050 m³m-3) are achieved for the daytime model and the mid-morning model with network SSM measurements, respectively. These results indicate that the daytime model exhibited generally comparable or better accuracy than that of the mid-morning model over the study area. This study has strengthened the feasibility of using multi-temporal information derived from the geostationary satellites to estimate SSM in future research. [ABSTRACT FROM AUTHOR]

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

Soil moisture plays a critical role in the energy exchange and water redistribution of the land-atmosphere system. Knowledge of the temporal variations in soil moisture is vital in agricultural applications. Microwave indices are often used to characterize the temporal variations in soil moisture. In this study, we evaluate the temporal variations in soil moisture based on the microwave polarization difference index (MPDI) using ground-based measurements in China.In situsoil moisture at six test sites during the crop-growing season from 2009 to 2011 is obtained. The consistency of the temporal variations between the MPDI values and thein situsoil moisture is analysed in terms of (1) microwave frequencies, (2) satellite overpass times, and (3) measurement depths of soil moisture. The results show that the accuracies of the consistency vary from approximately 40% to 90%. Compared with thein situsoil moisture at 0–10 cm, the temporal variations in soil moisture are best characterized by the 6.9 GHz MPDI values from the ascending overpasses (MPDI_06A). Furthermore, the accuracies of the consistency between MPDI_06A and thein situsoil moisture at 0–10 cm are greater than those between MPDI_06A and thein situsoil moisture at 10–20 cm. [ABSTRACT FROM PUBLISHER]

Evaporative fraction (EF) is an important index for partitioning surface available energy. Temporal information derived from multi-times observations of remote sensing can reduce the uncertainty of EF estimates caused by the retrieval error of land surface parameters. Two temporal-information-based EF methods were assessed usingin situmeasurements from the Energy Balance Bowen Ratio system and the outputs from the second phase of the North American Land Data Assimilation System (NLDAS-2) over the Southern Great Plains (SGP). One is a newly developed method for estimating daily EF using a semi-empirical parameterization based on the day–night differences of land surface temperature (LST), air temperature, and incoming solar radiation; the other is the triangular feature space method constructed by the day–night difference of LST and fractional vegetation cover. The results showed that the two methods could reasonably estimate EF over the SGP region in spatial distribution. The EF estimated from the newly developed method was closer to thein situmeasurements with a bias of −0.028 and a root mean square error (RMSE) of 0.194, and it was also closer to the outputs of the NLDAS-2 with an RMSE of ~0.13 compared with the results from the feature space method. A strong relationship between the results from the two methods was found with a coefficient of determination up to 0.824 and an RMSE of 0.143. The RMSE for the EF estimates caused by different vegetation indexes in the feature space could reach 0.15, and larger bias mainly occurred on surfaces with low evapotranspiration surface. [ABSTRACT FROM AUTHOR]

Quantification of land-surface evapotranspiration (ET) is highly significant in water resources management, climate change studies, and numerical weather prediction. The constant reference evaporative fraction method (EFr, the ratio of the actual to reference ET), which assumes that the daily EFris equal to that at the satellite overpass time, is a scheme that has been widely applied to upscale remotely sensed instantaneous ET to daily ET. To overcome the difficulties encountered in the acquisition of tower-based meteorological variables, this study investigates the feasibility of using publicly available weather forecast information to estimate the daily reference ET using the constant EFrmethod. A two-source energy balance model is adopted to compute the instantaneous ET using Moderate-Resolution Imaging Spectroradiometer (MODIS) remote-sensing data acquired between January 2011 and October 2012 at the Yucheng Comprehensive Experimental Station in the North China Plain. The results show that the daily maximum and minimum air temperatures from weather forecast information are consistent with the corresponding ground-based measurements, with a bias of 0.8 K and a root mean square error (RMSE) of <2.0 K. The daily global solar radiation and daily wind speed were poorly forecast when compared with the ground-based measurements. Using the meteorological variables from the daily weather forecast information produced a small bias of 0.1 mm day–1and an RMSE of 0.6 mm day–1when the estimated daily reference ET was compared with that derived using the ground-based meteorological measurements. When the remotely sensed instantaneous ET and half-hourly reference ET were as accurate as the ground-based measurements, the upscaling method produced the daily ET, using the meteorological variables from the weather forecast information, with a bias of 0.1 mm day–1and an RMSE of 0.7 mm day–1. [ABSTRACT FROM PUBLISHER]

Efficient detection and monitoring of physiological conditions and activity hidden by walls or rubble are considered challenging in rescue, surveillance, and security operations. In this paper, a new method based on wavelet analysis called wavelet entropy (WE) is proposed to not only detect vital signs but also to monitor the target’s physiological conditions through ultra-wideband (UWB) radar with a centre frequency of 400 MHz. Results of the experiments show that this new method can accurately locate a human target in free space, through a brick wall at a certain distance, and in cases of simulated rubble. Furthermore, a human target’s micro-motion responses due to different physiological conditions with a stationary body in the case of simulated rubble, and that are unrecognizable using common methods, were also quantitatively detected. The WE method not only locates the target’s position but also monitors changes in his/her physiological conditions. This method may also be a useful tool in the practical application of UWB radar. [ABSTRACT FROM PUBLISHER]

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

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

Land surface temperature (LST) is one of the key state variables for many applications. This article aims to apply our previously developed LST retrieval method to infrared atmospheric sounding interferometer (IASI) and atmospheric infrared sounder (AIRS) data. On the basis of the opposite characteristics of the atmospheric spectral absorption and surface spectral emissivity, a ‘downwelling radiance residual index’ (DRRI) has been recalled and improved to obtain LST and emissivity. To construct an efficient DRRI, an automatic channel selection procedure has been proposed, and 11 groups of channels have been selected within the range 800–1000 cm−1. The DRRI has been tested with IASI and AIRS data. For the IASI data, the radiosonde data have been used to correct for atmospheric effects and to retrieve LST, while the atmospheric profiles retrieved from AIRS data have been used to perform the atmospheric corrections and subsequently to estimate LST from AIRS data. The differences between IASI- and Moderate Resolution Imaging Spectroradiometer (MODIS)-derived LSTs are no more than 2 K, while the differences between AIRS- and MODIS-derived LSTs are less than 5 K. Even though an exceptionally problematic value occurred (–12.89 K), the overall differences between AIRS-estimated LST and the AIRS L2 LST product are no more than 5 K. Although the IASI-derived LST is more accurate than the AIRS-derived one, the convenient retrieval of AIRS atmospheric profile made this method more applicable. Limitations and uncertainties in retrieving LST using the DRRI method are also discussed. [ABSTRACT FROM AUTHOR]

The accuracy of a radiance transfer model neural network (RM-NN) for separating land surface temperature (LST) and emissivity from AST09 (the Advanced Spaceborne and Thermal Emission and Reflection Radiometer (ASTER) Standard Data Product, surface leaving radiance) is very high, but it is limited by the accuracy of the atmospheric correction. This article uses a neural network and radiance transfer model (MODTRAN4) to directly retrieve the LST and emissivity from ASTER1B data, which overcomes the difficulty of atmospheric correction in previous methods. The retrieval average accuracy of LST is about 1.1 K, and the average accuracy of emissivity in bands 11-14 is under 0.016 for simulated data when the input nodes are a combination of brightness temperature in bands 11-14. The average accuracy of LST is under 0.8 K when the input nodes are a combination of water vapour content and brightness temperature in bands 11-14. Finally, the comparison of retrieval results with ground measurement data indicates that the RM-NN can be used to accurately retrieve LST and emissivity from ASTER1B data. [ABSTRACT FROM AUTHOR]

In this letter, the performance of newly developed drought indices, the perpendicular drought index (PDI) and modified perpendicular drought index (MPDI), are further explored for regional surface dryness monitoring to provide clear guidance on appropriate implementation of these indices over different eco-systems through in-depth analysis of their advantages and constraints. Spatio-temporal patterns of surface drought derived by MODerate Resolution Imaging Spectroradiometer (MODIS)-based PDI and MPDI are compared against field-measured soil moisture (SM), rainfall, and regional hydrological conditions. Results indicate that there are significant negative correlations between the PDI, the MPDI, and mean 0-20 cm SM content and rainfall. The PDI and the MPDI provide similar results at the early stage of vegetation growth, but a greater agreement between the drought information extracted by the MPDI and field measurements is observed for vegetated surfaces where the PDI fails. Therefore, it is recommended that PDI be used for bare soil applications, since it does not require calculation of additional information such as the fraction of vegetation which may contain some uncertainties, but the MPDI should be used for vegetated regions. [ABSTRACT FROM AUTHOR]

We propose a strategy to construct a priori knowledge in Bidirectional Reflectance Distribution Function (BRDF) model-based Leaf Area Index (LAI) inversion. In this strategy, the physical limitations, a best guess and its uncertainty for each parameter needed to be inverted were obtained from a spectral database. Vegetation index (VI) and growth date were used to provide more information about LAI. The relationship between LAI and VI was obtained by forward simulation using the BRDF model. The empirical model of the changing LAI and the growth date was obtained by statistical analysis of more than 600 field samples from a wheat paddock. A SAIL-reflectance model including the hotspot-effect (SAILH model) was used to generate bidirectional reflectance distribution. Gaussian distributed random noises were added on the reflectance as 'observation'. SAILH model was inverted to validate the effectiveness of this strategy. It was further validated using both of the ground measurements and airborne remote-sensing data. It is found that a priori knowledge is important for successful inversion, and our strategy is expected to yield more reasonable spatial and temporal LAI distribution. [ABSTRACT FROM AUTHOR]