Real-time precipitation retrieval is crucial for timely warnings of extreme weather events such as heavy rain or floods. Geostationary satellite observations combined with machine learning methods provide an effective way to achieve real-time precipitation retrieval, yet are hampered by two issues. The traditional stable model, pre-trained with historical data, lacks precipitation information from adjacent time periods and its accuracy declines over time. Geostationary satellites can only capture cloud top information, leading to inaccurate localization of precipitation areas in estimations based solely on such data. To solve these problems, a novel Dynamic Two-step Real-time Precipitation Retrieval Algorithm (D-PRA) has been developed. It conducts real-time retrieval in two steps through dynamic data and variables selection: identifying precipitation and then retrieving precipitation intensity. It further incorporates atmospheric profile information to enhance the details of in-cloud and under-cloud conditions. D-PRA was applied to Himwari-8 observations and was validated with the rain gauge observations in Chinese regions at the hourly scale. The results showed that, in comparison with GSMaP_NOW, D-PRA exhibits significant enhancements. The probability of detection attained 0.72, representing a nearly fivefold increment, and the root mean square error was 0.99 mm, a 25.5% reduction. Moreover, D-PRA is stable across seasons and time periods, demonstrating its good reliability and robustness. D-PRA has great potential to improve the accuracy of precipitation retrieval for extreme event monitoring and disaster management.

The AMSR-E/NASA (Advanced Microwave Scanning Radiometer-Earth Observing System/National Aeronautics and Space Administration) daily global SM (soil moisture) product (2002-2011, 25 km resolution) has been widely used but exhibits limited sensitivity to intra- and inter-annual variability in many regions. This limitation is mainly attributed to inaccurate parameter values (A0andA1), which account for vegetation transmittance and emission in the AMSR-E/NASA SM retrieval algorithm. To address this issue, we recalibratedA0andA1using in-situ SM measurements from 13 observation networks (192 sites) and established their empirical relationships with FVC (Fractional Vegetation Cover). Four dominant land-cover types (i.e., bare soil, grassland, cropland, and forest) were considered due to their global representativeness and extensive coverage with in-situ SM measurements. Based on these relationships, we generated global maps ofA0andA1, and produced an improved Global Daily AMSR-E SM dataset (GD_AMSR-E_SM; 2002-2011, 25 km resolution) using GLASS (Global Land Surface Satellite) FVC dataset and AMSR-E observations. Validation against in-situ SM measurements within six independent networks shows that the GD_AMSR-E_SM dataset achieves greater consistency with in-situ SM measurements, with MAE (Mean Absolute Error) and RMSE (Root Mean Square Error) values of 0.026 cm³/cm³ and 0.032 cm³/cm³, respectively. This represents average reductions of 20% and 26% compared with the AMSR-E/NASA, AMSR-E/JAXA (Japan Aerospace Exploration Agency), and AMSR-E/LPRM (Land Parameter Retrieval Model) SM products. The enhanced algorithm improves the accuracy and reliability of AMSR-E observations for long-term global SM monitoring.

Accurate and timely precipitation data plays a crucial role in flood monitoring, forecasting and emergency management. The primary challenge with current real-time precipitation products is their limited accuracy in identifying precipitation. To improve the accuracy of precipitation retrieval, a dynamic two-step precipitation retrieval method was developed, i.e., first identifying precipitation and then estimating its intensity. The geostationary satellite infrared brightness temperature data, atmospheric profile data, and rain gauge observations were used to construct the precipitation identification model and precipitation intensity retrieval model based on a random forest machine learning method. This method was applied to precipitation retrieval in China and surrounding areas using the Himawari-8 observations and the ERA5 atmospheric profile data. The results of hourly precipitation identification showed good agreement with rain gauge observations, with a Probability of Detection of 0.725 and a False Alarm Ratio of 0.426. The estimated hourly precipitation intensity agrees well with the rain gauge data and shows a lower error than the GSMaP_NOW real-time precipitation product. The good performance of the developed method in this study for real-time precipitation monitoring was also confirmed in different seasons.

In the Tibetan Plateau (TP) region, the foreseeable increase in air temperature may have profound and complex effects on the local hydrological cycle, and is likely to increase water loss from the land surface to the atmosphere through evapotranspiration (ET). Quantifying ET and its regulatory mechanisms are major challenges for understanding the water cycle and land-atmosphere interactions in the TP region. We evaluated the performance of several Earth observation-based ET datasets in the TP region, and explored the spatiotemporal variation of ET in the same region. The accuracy of different global ET datasets was evaluated, and ETMonitor and PML-V2 provide the best accuracy with overall high correlation, low bias, and low root mean square error. ETMonitor ET is also the only product with both high spatial (~1 km) and temporal (daily) resolution. ETMonitor ET may reflect the effect of mountain topography on ET better than other global products, i.e., ET values are higher in the humid valleys with denser vegetation cover and higher soil moisture, and ET values are lower on the mountain slopes at higher elevations with less vegetation cover and colder climate. Other ET products failed to capture the spatial patterns of ET in the mountainous regions, and this suggests that the spatial resolution is not the only dominant factor leading to the poorer performance of these ET products in the mountain regions of the TP. The results show that multi-year average ET is 339 mm/yr in the TP region during 2000-2021, which accounts for about 51% of the total precipitation in the TP region. From 2000 to 2021, ET over the Tibetan Plateau shows an overall increasing trend with large spatial variability.

Accurate knowledge of the at-surface solar irradiance (SSI) is essential to retrieving surface and atmospheric properties using satellite measurements of back-scattered and reflected radiance. The latter is affected by surface-atmosphere interactions, including the effects of terrain. The SSI is affected by the same processes. This study proposes a method to estimate the components of instantaneous surface solar irradiance: direct, isotropic and circumsolar diffuse, and terrain irradiance, which is expected to improve the simultaneous retrieval of Aerosol Optical Depth (AOD) and surface reflectance. The method takes into account the coupled effects of topography and atmosphere by combining parametrization and the Look-Up Table (LUT) approaches. The method was applied to rugged terrain over the Tibetan Plateau using MODIS atmosphere and surface data, ERA5 reanalysis data, CALIOP aerosol data and a Digital Elevation Model (DEM). The results showed that the SSI estimates were in satisfactory agreement with ground observations at four stations over the Tibetan Plateau in 2018 with R2values of 0.61, 0.44, 0.41, and 0.49, respectively, and RMSE of 205.7 W/m2, 176.9 W/m2, 186.0 W/m2, and 201.2 W/m2, respectively. Estimations of the diffuse irradiance were evaluated separately against the only available in-situ observations at the Dali Station and the results were better than our SSI estimates with R2, RMSE, and relative BIAS being 0.71, 94.98 W/m2, and 31%, respectively. The isotropic and circumsolar diffuse irradiance accounted for 37.57% and 7.68% of the total annual SSI respectively, while diffuse irradiance accounted for 46.48% of the total annual SSI. Under clear skies, every 0.1 increase in AOD caused about a 35 W/m2increase in diffuse irradiance and a decrease of about 25 W/m2of SSI.

Evapotranspiration (ET) plays a crucial role in the global energy and water cycles. Clarifying the time-series characteristics of ET is essential for accurately estimating ET and comprehending the ET change. This study utilized the latest ETMonitor product to analyze ET time-series characteristics in China from 2001 to 2021. The standard deviation and coefficient of variation were used to measure the absolute and relative variability of ET. A monthly ET time series was decomposed using an additive decomposition method to analyze its components. The results showed that China's ET exhibited an increasing trend, especially in the middle regions of the Yellow River to northeastern China, at a rate exceeding 5 mm/year. Northwestern China showed larger variation coefficients due to lower ET. Seasonal and irregular components were found to dominate the monthly ET time series, with significant seasonal components observed in eastern China and irregular components dominating the western region. Moreover, the seasonal characteristic of ET was more evident in north and northeast China compared to southern and northwestern regions. Additionally, relative variation in ET during winter was more significant than in summer, primarily in northern river basins.

Satellite-based Earth Observation systems archived a variety of vegetation products during the last 50 years, which can reveal regional to global ecosystem dynamics across diverse spatiotemporal scales. The anomaly metrics such as Vegetation Condition Index (VCI) defined by comparing the current vegetation growth condition to historical average status based on long-term EO-based vegetation products were widely used to delineate abnormal vegetation variation exerted by either climatic or anthropogenic factors (e.g., droughts, wildfires). However, currently available long-term vegetation products may differ from each other in terms of sensors (observational platform or spectral bands), bio-physical definitions (e.g., NDVI, EVI, LAI, and VOD), spatiotemporal resolution, as well as the time-spans, which results in inconsistency across these vegetation products. Taking the VCI as an example, this study evaluated the uncertainty of vegetation anomalies detected based on different vegetation products over the middle reach of the Yangtze River by explicitly considering the effect of sensors, biophysical definitions, and time-spans. The preliminary results showed that VCI derived from NDVI products from different sensors (AVHRR vs. MODIS) induced significant inconsistent anomalies over most landscapes. The differences resulting from products with different biophysical definitions (NDVI vs. EVI, LAI, and VOD) are much lower than those from different sensors but still significant over specific areas. As for the time-spans, the 20-year NDVI based VCI presented a considerable reduction in variance over the study area on average compared to VCI calculated based on 5-year NDVI. In summary, caution should be taken when applying EO-based vegetation products for vegetation anomalies mapping, especially for quantitative assessment.

Terrestrial surface soil heat flux is an important component of surface energy balance, uncertainty in surface soil heat flux may cause errors in evapotranspiration estimation when using energy balance-based algorithms at large scale using remote sensing observations. This study assessed the performance of different equations for daily averaged soil heat flux estimation for large-scale application using parameters derived from remote sensing observations, including five soil heat flux equations used in different evapotranspiration models and three machine learning-based soil heat flux algorithms. These algorithms, taking soil heat flux as a constant or varying fraction of net radiation, generally overestimate daily soil heat flux with root mean square error (RMSE) of 14.90 ~ 23.12 W m-2. The machine learning methods could achieve better accuracy of daily soil heat flux estimation, with the lowest RMSE of 3.85 W m-2 by random forest algorithm based on in situ observations at flux tower sites. The random forest algorithm was further applied for global soil heat flux estimation, and the obtained soil heat flux could capture the global patterns of soil heat flux well with RMSE of 4.77 W m-2. The results indicate that soil heat flux could be well estimated by the machine learning method, and it is promising to improve regional evapotranspiration estimation based on remote sensing observations.

Drought hazards induced by continuous water shortage may damage crop growth and cause severe grain loss. With one of the most intensive irrigation systems, the Indus basin has supported agriculture for millennia and feeds up more than 300 million people. The water supply for the basin scale irrigation is dominated by melting of glaciers and snowpack in the Himalaya and Karakoram mountain ranges, ground water as well as the Asian monsoon rainfall. To understand how ecosystems over the Indus basin with such complex water supply mechanism response to meteorological drought (rainfall shortage) is critical for future drought monitoring and evaluation applications. This study evaluated the spatiotemporal response pattern using correlation analysis of rainfall anomalies (3-month scale) and vegetation anomalies (1-month scale) with long-term satellite observations. The result found that the vegetation over northern Indus valley significantly coupled to rainfall variation during both summer (Kharif) and winter (Rabi) monsoon season. While significant response during Kharif season also was identified over the southern part, especially over the Punjab, where was well equipped with irrigation system. We concluded that special attention should be paid to drought assessment in terms of rainfall and vegetation anomaly over the Indus basin.

Soil moisture is a key variable for land-atmosphere water and heat energy exchange, crop growth, and the global water cycle. In this study, a method for soil moisture retrieval by utilizing both of the multi-angular and multitemporal features of brightness temperature from SMOS (Soil Moisture and Ocean Salinity) was proposed: first, vegetation optical depth (VOD) and single scattering albedo (ω) were retrieved using SMOS Horizontal-polarized multiangular and multi-temporal observations, then we applied the single-channel retrieval algorithm (SCA) to derive soil moisture. Results from this study showed that the consistency between the retrieved soil moisture by the new algorithm and the in-situ data was better than those from SMOSL3 and SMOS-IC through the comparison with insitu soil moisture from several observation networks with smaller value of unbiased root mean square error (ubRMSE).

This study quantitatively evaluated the performance of six global drought indices (the self-calibrating Palmer Drought Severity Index -scPDSI, the Standardized Precipitation-Evapotranspiration Index - SPEI, the Global Precipitation Climatology Centre Drought Index - GPCC_DI, the Multivariate Standardized Drought Index - MSDI, the Standardized Soil Moisture Index - SSI, and the Standardized Precipitation Index - SPI) over China using the drought records from the Emergency Events Database (EM-DAT) by developing two indicators of the monitored drought area percentage (MDAP) and the monitored drought period percentage (MDPP). The results showed that scPDSI, SPEI, GPCC_DI, and MSDI can capture drought events in China from 1980 to 2015 better than SPI and SSI, with MDAP of ~80% and MDPP of ~70% at optimal timescales, among which SPEI and MSDI is slightly better than scPDSI and GPCC_DI.

Mountain glaciers are one of the major fresh water resources. Glacier mass balance on the Tibetan Plateau (TP) can directly reflect local climate change and plays a crucial role in the terrestrial water cycle and food security of local people. In this study, we improved the procedure to analyze Three-Line-Array (TLA) stereo images to estimate the glaciers mass balance in Kangri Karpo mountains using Zi Yuan-3 (ZY-3) TLA data and C-band Shuttle Radar Topography Mission (SRTM) Digital Elevation Model (DEM) in two periods, i.e. 2000–2013 and 2013–2017. The results showed that the mean mass balance of glaciers between 2000 and 2017 was −0.91 ± 0.02 m w.e. a−1. The glaciers presented accelerated mass loss in recent years (2013–2017, −2.84 ±0.05 m w.e. a−1), compared with the first decade in 21st (2000–2013, −1.59 ± 0.03 m w.e. a−1), while the melt rate in debris-covered glaciers was larger than in clean-ice glaciers.

The AMSR-E (Advanced Microwave Scanning Radiometer - EOS) sensor onboard NASA's Aqua satellite is a passive microwave radiometer and is widely used in land surface Soil Moisture (SM) retrieval. The NASA National Snow and Ice Data Center Distributed Active Archive Center (NSIDC DAAC) archives and distributes a daily SM product (AMSR-E/NASA) from 2002 to 2011 at 25km resolution retrieved by a simplified soil moisture retrieval algorithm derived from a simplified radiative transfer model. However, the SM dynamic range of this product is very narrow, which basically cannot reflect intra- and inter-annual variations. This paper analyzed the AMSR-E/NASA SM product and compared it with the AMSR-E/JAXA SM. Furthermore, the reason of the narrow intra- and inter-annual variation of AMSR-E/NASA SM was analyzed in depth. Finally, the AMSR-E/NASA SM product was improved using in-situ data collected in the Tibetan Plateau. Results indicated that the improved monthly SM AMSR-E product was better than AMSR-E/NASA and AMSR-E/JAXA in Naqu study area from January to September of 2011, as documented by a lower RMSE (Root Mean Square Error) against in-situ SM data, i.e. 0.065 cm3cm-3.

The monthly nighttime light data observed by the Visible Infrared Imaging Radiometer Suite (VIIRS) sensor onboard the National Polar-orbiting Partnership (NPP) satellite are useful to assess human activities. However, noise in the VIIRS data and unstable power supply in many areas often limit its application in detecting anthropic activities. This study aims to develop a new method (i.e., Patches Denoising Method, PDM) to remove noise in the VIIRS monthly nighttime light data towards better information on anthropic activities. The new synthesized data were examined in the Sahel region where the intensity of nighttime light is often affected by unstable power supply. The results showed that the new data could capture more settlements correctly compared with the annual data released by the National Centers for Environmental Information (NCEI) of the National Oceanic and Atmospheric Administration (NOAA). This suggests that the Patches De-noising Method is a useful way to remove noise in the NPP-VIIRS monthly nighttime light data.

Mountain glaciers are sensitive to climate change and are thus relevant indicators of regional climate variability. In order to understand the dynamics of glaciers, retrieving glacier surface velocity is valuable in understanding physical processes in glaciers. We apply an image cross-correlation algorithm in the frequency domain to derive glacier velocity on the Yanong glacier between 2013 and 2018. The results indicate that the flow patterns are related to the terrain complexity. Yanong Glacier maximum velocity was 168 m/year at the elevation around 4700 meters. The surface velocity exceeded 100 m/year above the elevation of 4200 m, while above 5000 m the surface velocity fluctuated around 60 m/year along the main stream.

Thailand is characterized by typical tropical monsoon climate, and is suffering from serious water related problems, including seasonal drought and flooding. It is critical to study the spatiotemporal pattern of evapotranspiration (ET) in Thailand to support the local water resource management. In this study, daily ET was estimated over Thailand by ETMonitor, a process based model, based on mainly satellite earth observation datasets. The original ETMonitor is improved by combing land surface temperature -based method to calibrate the algorithm regionally by parameterizing the constrain of soil moisture. Good agreements were found between the estimated daily ET and flux tower observation with root mean square error from 1.06 to 1.21 mm d-1. The Chi and Mun river basins, located in the Northeast Thailand, were selected to analyze the ET spatiotemporal pattern. The results indicate that the ET had large seasonal variability in these two basins, which is largely influenced by the monsoon climate.

The cloud causes the invalid observation from sensor aboard on satellite on cloudy day, which will corrupt the spatio-temporal continuity of the land surface parameters retrieved based on remote sensing, and prevent the fusing multi-source remote sensing data in the field of the quantitative remote sensing. Based on the requirement of quantitative remote sensing, this paper proposes an improved method which considers the anisotropic reflectance of land surface and the spatial heterogeneity, and carried out the proposed method in Heihe river basin of China to retrieve MODIS band surface reflectance. the proposed method first constructs the spatial distribution based on the corresponding BRDF and solar-viewing geometry; then, a geographically weighted regression (GWR) was introduced into the proposed method to derive the MODIS band surface reflectance at cloudy pixels one by one. And, the validation for the proposed method shows an accuracy (all of RMSE is 0.011, 4.9%), which is rational and good based on the criterions of the image processing and the quantitative remoter sensing.

Glacier thickness change is a sensitive factor in response to global climate change, its quantitative assessment is critical to evaluate the variation of glacier mass balance. This paper analyzed and improved the method of planes fitted to repeat-tracks which uses Geoscience Laser Altimeter System (GLAS) data to extract glacier thickness change and applied it in the Tibetan Plateau area. The original plane fitting method was revised by curve fitting. Two extra parameters were added in the improved method so that the influence of complex terrain and of uncertainty due to sparse GLAS data on the estimate of glacier thickness change can be depressed. The improved method was applied to Naimona'Nyi glacier and Karakoram glacier in Tibetan Plateau area and results show that the glacier thinning rates reached −0.68±0.02 m/yr and −0.014±0.34m/yr respectively.

The meteorological drought, caused by a shortage of precipitation over a certain period, is the root of a causal chain in drought series in agriculture, ecology and hydrology. Different precipitation datasets produced inconsistent conclusions in the characteristics and trends of drought events. This study analyzed the characteristics and trends of meteorological drought over China using the standardized precipitation index (SPI) derived from TRMM and CMORPH precipitation data. The annual precipitation over China was increased from 1998 to 2014, indicating a wetting trend over China in the past near twenty years. CMORPH data showed more significant wetting trend than TRMM data. The wetting trend mainly occurred in northern China, while southern China has experienced a drying trend. The drying trend over China in spring and summer is more obvious than in autumn and winter. However, all analysis showed that the drought area over China as a whole was decreasing from 1998 to 2014.

The state of the land surface and the water cycle over the South and East Asia can be determined by space observation. New or significantly improved algorithms have been developed and evaluated against ground measurements. Variables retrieved include land surface properties, i.e. NDVI, LAI, FPAR, albedo, soil moisture, glacier and lake levels. Based on these biophysical parameters derived from microwave and optical remote sensing observations, a hybrid remotely sensed evapotranspiration (ET) estimation model named ETMonitor was developed and applied to estimate the daily actual ET of the Southeast Asia at a spatial resolution of 1 km. The changes in glaciers and lakes on the Tibetan Plateau, and the drainage links between glaciers and lakes are determined in this climate-sensitive region.

Radiometric measurements taken at a distance from evaporating bodies have been used for about 50 years to obtain a measure of water use. The basic physics of determining water use is the principle of energy conservation at the evaporating surface, but energy flux densities need to be parameterized using the variables which can be actually captured by radiometric measurements. Both observations of surface temperature and of soil water content are used for this purpose. Models of energy and water exchange vary from semi-empirical relationships between latent heat flux density and surface temperature to fully three-dimensional models of the soil-foliage-atmosphere system. Several ET satellite data products are available and evaluations against ground measurements have been published. Results suggest a dependence of accuracy on climate, soil and vegetation. The review of documented performance is combined with the sensitivity analysis of widely used ET algorithms, e.g. SEBI, SEBS, SEBAL and ET Monitor.

Global rainfall interception loss information is essential to understand the dynamics of the water cycle. And it remains large uncertainty in its global variation, since its ground observation are scattered. The development of satellite earth observation provides good opportunity for global rainfall interception loss estimation to reveal its spatiotemporal variation. In current study, an analytical model, Gash model, was revised and applied for global rainfall interception estimation based mainly on satellite based remote sensing products, e.g. precipitation, leaf area index, canopy height. The ratio of interception loss to precipitation showed good agreement with in situ observations, with correlation factor and RMSE of 0.65 and 6.17%. Large spatial variation of rainfall interception loss were found with high value in tropical rainfall forest region and high latitude forest regions. However, the temporal variation was much less.

Evapotranspiration (ET) is an important ecohydrological process especially in arid and semi-arid regions. In current study, a process-based model named ETMonitor was developed to estimate the ET, based mainly on the biophysical and hydrological parameters retrieved from satellite earth observations. And global daily ET from 2008 to 2012 with a spatial resolution of 1 km was estimated based on multi-source earth observations datasets. The estimated ET agreed well with the in situ observations at field scale, with R2= 0.74, Bias = -0.05 mm d-1, RMSE = 0.87 mm d-1. The spatial patterns of estimated ET also agree well with the current available global ET products such as MOD16 and GLEAM. The ET products provide critical information on global terrestrial water and energy cycles and environmental change.

The applications of Multi-temporal Normalized Difference Vegetation Index (NDVI), a critical proxy for analysing vegetation dynamics, have been long hindered by prevalent noise. The Harmonic ANalysis of Time Series (HANTS) has been widely applied to reconstruct noise- and cloud-free NDVI time series data set from regional to global scales. The reconstruction performance of HANTS is severely dependent on the inherent parameter settings in HANTS. However, most applications set the parameters of HANTS based on users' experience. This study analysed the sensitivity of the reconstruction performance to several critical parameters and weighting scheme of HANTS. The results document the optimized parameter settings for global NDVI time series reconstruction.

To validate satellite-based surface fluxes by ground measurements properly, several numerical simulations were carried out at a homogeneous alpine meadow site and mixed cropland site, considering various atmospheric conditions and different land cover distribution types. By comparing various pixel selection methods, the results showed that footprint was significant in insuring a consistent spatial scale between ground measurements and satellite-based surface fluxes, particularly for heterogeneous surface and high-resolution remote sensing data. Because large aperture scintillometer measurements cover larger areas than eddy covariance (EC) system measurements, the spatial heterogeneity at a subpixel scale in complicated surface should be further considered in validating coarse satellite data. Thus, more accurate validation data and scaling methods must be developed, such as measuring surface fluxes at the satellite pixel scale by a flux measurement matrix or airborne EC measurements.

Interception loss is an important component of the regional water balance for the Heihe River Basin which is an inland basin with limited precipitation. We used a modified Gash analytical model by combining remote sensing observations to estimate the interception loss of several vegetation types, e.g., grass, crop, forest and shrub for the years 2003-2012 in the Heihe River Basin. The estimated monthly interception ratio (in percent) was compared with field measurements made in Dayekou and Pailugou forest hydrology experimental sites and the results showed reasonable accuracy with RMSE of 5.0% and 4.3% at the two sites, respectively. The regional distribution of the interception loss showed strong spatial and temporal variability at monthly scale. At annual scale, the interception ratio could be treated as a stable indicator for long-term water balance research. The annual average interception loss is about 7.2% of gross rainfall for the vegetation covered area in the Heihe River Basin.

Spatial mapping of evapotranspiration (ET) is specifically critical for the semi-arid inland river basin with great heterogeneity in land-cover types. This letter estimates the spatial distribution of daily ET over the middle stream and downstream areas of the Heihe River Basin during the growing season of 2012 by using the Surface Energy Balance System algorithm with land surface temperature at high spatial resolution (300 m) derived from observations by the Chinese satellite HJ-1. The results demonstrate that ET estimates are consistent with ground-based measurements collected during the Heihe Watershed Allied Telemetry Experimental Research (HiWATER) with acceptable accuracy. The magnitude of daily ET in the downstream area is obviously lower than that in the middle stream area. Further analysis based on classification maps shows that there is significant temporal-spatial heterogeneity of daily ET over different land-cover surfaces and also within the same vegetation type. The temporal variation of ET in the middle stream area has clear seasonality with an obvious peak in July, whereas it is flat in the downstream area due to the dominating arid-region vegetation species and low soil water content in growing season. In addition, because of the abundant irrigation in the maize and irrigated orchard fields, the daily ET values of them are higher than that of wetland and even comparable with that of water surface in the middle stream area of the Heihe River Basin.

Land surface albedo is a very important parameter, which can be derived from a bidirectional reflectance distribution function (BRDF) model with angular integration of BRDF in a particular distribution of downward solar irradiance. The Algorithm for MODIS Bidirectional Reflectance Anisotropic of Land Surface (AMBRALS) utilizes a kernel-driven BRDF model to retrieve land surface albedo, but it does not take into account the topographic effect. This letter proposed an improved method by adding topographic factor to the AMBRALS, which removes the topographic effect on land surface reflectance and considers the topographic effect in retrieving land surface albedo. This method is applied to the HJ-1A/B CCD land surface reflectance data to retrieve the land surface albedo. The results are compared with those from the AMBRALS and with the ground measurements. The comparison results show that the proposed method performs well in general and better than the AMBRALS over rugged area.

Land surface albedo is derived from a bidirectional reflectance distribution function (BRDF) model with angular integration of BRDF. The kernel-driven model is based on the Algorithm for MODIS Bidirectional Reflectance Anisotropic of Land Surface (AMBRALS) to retrieve land surface albedo. However, at moderate/low spatial resolution scale, topography results in mutual shading of sub-pixels, the kernel-driven model does not deal explicitly with such topographic effects. This paper proposes an improved kernel-driven model by adding a topographic factor to the kernel-driven model. This model was applied to MODIS land surface reflectance and the results were compared with the ones obtained with AMBRALS. The results were evaluated against ground measurements.

One main objective of Soil Moisture and Ocean Salinity (SMOS) is to provide observations of global soil moisture. The resolution of L-band radiometer carried by SMOS is about 50 km resolution, which is rather coarse for many applications. The method based on `universal triangle' has been used on downscaling soil moisture retrieved by SMOS with L-band brightness temperature. Consider the senor carried by SMOS is the L-band multi-angular dual polarization radiometer, this paper presents an improved method for downscaling Centre Aval de Traitement des Données SMOS (CATDS) L3 soil moisture by using MODIS Land Surface Temperature (LST) /NDVI and SMOS dual polarization brightness temperature. Southwest of China is selected as the study area and ground based soil moisture from Maqu monitor network is used to compare with the SMOS CATDS soil moisture at 25 km and the downscaled soil moisture. The results show that the improved method could improve the result with R2from 0.15 to 0.25. High resolution brightness temperature used in the method is obtained from SMOS CATDS brightness temperature at 25 km and causes error during the experiment.

This paper presents a method to retrieve surface soil moisture at high-resolution from brightness temperature. To obtain high resolution brightness temperature, downscaling brightness temperature uses higher resolution visible/infrared satellite data and is based on the method of retrieving land surface temperature with passive microwave and the relationship between Microwave Polarization Difference Index (MPDI) and NDVL High resolution soil moisture is retrieved with downscaled brightness temperature using a single-channel algorithm (SCA), and the Qpmodel used to deal with the influence of roughness. Downscaled brightness temperature is compared with the aircraft data during the Experiment in the Heihe River Basin in China. The retrieved high resolution soil moisture is compared with in situ (0-6 cm depth) point measurements during SMEX02, obtaining R2= 0.74 and RMSE as 0.047 cm3cm-3.

Foliage and soil temperatures are key variables for assessing the exchanges of turbulent heat fluxes between vegetated land and the atmosphere. Using multiple-view-angle thermal-infrared (TIR) observations, the temperatures of soil and vegetation may be retrieved. However, particularly for sparsely vegetated areas, the soil and vegetation component temperatures in the sun and in the shade may be very different depending on the solar radiation, the physical properties of the surface, and the meteorological conditions. This may interfere with a correct retrieval of component temperatures, but it might also yield extra information related to canopy structure. Both are strong reasons to investigate this phenomenon in some more detail. To this end, the relationship between the TIR radiance directionality and the component temperatures has been analyzed. In this paper, we extend the four-stream radiative transfer (RT) formalism of the Scattering by Arbitrarily Inclined Leaves model family to the TIR domain. This new approach enables us to simulate the multiple scattering and emission inside a geometrically homogenous but thermodynamically heterogeneous canopy for optical as well as thermal radiation using the same modeling framework. In this way top-of-canopy thermal radiances observed under multiple viewing angles can be related to the temperatures of sunlit and shaded soil and sunlit and shaded leaves. In this paper, we describe the development of this unified optical-thermal RT theory and demonstrate its capabilities. A preliminary validation using an experimental data set collected in the Shunyi remote sensing field campaign in China is briefly addressed

Land surface parameterizations in numerical weather prediction describe the exchange of energy and water at the land-atmosphere interface. They are generally based on a coupled energy-water budget equation, and the results depend critically on (prescribed) land surface characteristics, notably albedo, soil water storage capacity and aerodynamic roughness. In a recent case-study bi-angular (nadir and forward) observations provided by the ATSR instrument were used to estimate the component soil and foliage temperature. For two images overlying the Netherlands on 14 May 1998 and South-East Spain on 9 September 1998 vegetation and bare ground temperatures were derived from ATSR data, and aggregated to a grid of 25/spl times/25 km, compatible with a NWP model. This NWP model was equipped with a land surface parameterization scheme in which vegetation and bare ground temperature were calculated separately. In a variational assimilation scheme, the NWP model was forced to match the observed component temperatures by changing both the soil moisture content and the aerodynamic roughness for the heat exchange of the bare ground component. It appeared that the optimal solution differed significantly for the two regions. For The Netherlands, changing the aerodynamic roughness did not affect the correspondence to observations, and soil moisture had to be changed to increase the overall model performance.

The exploration of the full potential of multi-angular thermal infrared measurements is hampered by the scarcity of accurate and representative data sets. Acquisition of field data has several constraints, including lack of a suitable airborne sensor system. Development and evaluation of algorithms requires detailed and accurate radiometric data, which have to be collected in a very short time to limit noise due to the temporal variability of surface temperature controlled by the local heat balance within the canopy. Several canopy properties have to be determined, e.g. leaf area index (LAI), leaf inclination distribution, in addition to soil and foliage temperature. A data set for validation studies should comprise a range of canopies and canopy conditions. We have coped with these difficulties by using a detailed radiative transfer model (CUPID) of the soil-plant--atmosphere system to produce synthetic data sets over a wide range of canopies and hydro-meteorological conditions. These data have been first used to assess the sensitivity of the anisotropy in TIR radiance to LAI and to the soil water content. It was shown that a dry top soil and a wet root zone imply that the brightness temperature is highest at nadir viewing, opposite to the condition with a wet top soil and a drier root zone. The relative magnitude of the directional change in surface temperature compared with the difference between soil and foliage temperature was evaluated in detail over a range of LAI values. Next, the radiance data base created by RT modeling was used to produce realistic complex scenes. Land cover of a complex, irrigated agriculture scene was mapped beforehand using a TM image.

A mixture of foliage and soil is thermally heterogeneous, so the radiometric temperature of the mixture depends on view direction. A simple linear mixture model was applied to estimate the component surface temperatures of foliage and soil temperatures. The potential of directional observations in the thermal infrared region for land surface studies is a largely uncharted area of research. The availability of the dual-view Along Track Scanning Radiometer (ATSR) observations led to explore new opportunities in this direction. In the context of studies on heat transfer at heterogeneous land surfaces, multiangular thermal infrared (TIR) observations offer the opportunity of overcoming fundamental difficulties in modeling sparse canopies. Three case studies were performed on the estimation of the component temperatures of foliage and soil. The first one included the use of multi-angular field measurements at view angles of 0/spl deg/, 23/spl deg/ and 52/spl deg/. The second and third one were done with directional ATSR observations at view angles of 0/spl deg/ and 53/spl deg/ only. Different models have been proposed in literature to interpret observations of directional exitance: (1) simple geometric (deterministic) models of the system, (2) radiative transfer within a complete canopy, and (3) radiative transfer in an inhomogeneous thick layer of vegetation. Our approach is based on the third modeling concept. A target comprising a mixture of foliage and soil is characterized by the gap fraction, and observed radiance is described as a weighted sum of foliage radiance and soil radiance, with the weights being the gap fraction and its complement, respectively.