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C2: ID.10649 Water Cycle & River Basins
Close Water Cycle at the River Basin Scale Using Remote Sensing Data: Recent Advances and Final Results
1Chinese Academy of Sciences Center for Excellence in Tibetan Plateau Earth Sciences; 2Key Laboratory of Remote Sensing of Gansu Province, Cold and Arid Regions Environmental and Engineering Research Institute, Chinese Academy of Sciences; 3Beijing Normal University; 4Institute of Earth Environment, Chinese Academy of Sciences; 5Jiangsu Normal University;
Unprecedented advances on the use of remotely sensed observations in hydrology research have been witnessed globally, particularly at the river basin scale requires reliable remote sensing products (RSPs) of land surface variables with middle to high resolution. This paper presents the progress we have made in the development and validation of RSPs toward several key water budget components, mainly addressing precipitation, snow cover area (SCA), soil moisture (SM), evapotranspiration (ET) and groundwater variation in northwest China, in order to close water cycle at the river basin scale.
As an essential input source of land water, precipitation is highly stressed in our study. Synergetic efforts of observation, modeling and analysis have been performed. Such as 4DVar data assimilation (DA) technique was adopted in regional climate model to assimilate Tropical Rainfall Measuring Mission (TRMM) and Fengyun (FY) series remote sensing precipitation products into a Weather Research and Forecasting (WRF) model to implement dynamical downscaling. In addition, we evaluated four regional precipitation products for the Heihe River Basin (HRB), including disaggregated Climate Prediction Center Merged Analysis of Precipitation (DCMAP), fusion product of Institute of Tibetan Plateau Research (ITP-F), WRF-P products and disaggregated CMAP products downscaled by statistical meteorological model tool (DCMAP-MicoMet). With respect to solid precipitation, a long-term (2000-2015) dataset over the HRB at hourly temporal resolution with spatial resolution of 0.05 degree has been downscaled and simulated by WRF model.
High-resolution snow distributions are essential for studying cold region hydrology and environment. In the upstream of the HRB, the characteristics of ENVISAT-ASAR data in shallow dry snow cover area were analyzed based on microwave backscattering models and snow field measurements. And therefore snow water equivalent (SWE) was estimated. Based on the linear spectral mixture analysis (LSMA) technique, a coupled regional approach (CRA) was presented for estimating the fractional snow covered (FSC) area of the HRB using MODIS data. The retrieved FSC products were evaluated using high-resolution FSC maps retrieved from Landsat ETM+ images and MODIS FSC product (MOD10A1). We also presented a downscaling method based on simulated inhomogeneous snow ablation capacities that are driven by air temperature and solar radiation data. Using this method, FSC data with a resolution of 500 m were downscaled to a resolution of 30 m.
Soil moisture (SM) products were mainly derived from active and passive microwave remote sensing data. Firstly, a change detection method was used to estimate SM and its freeze/thaw status based on ENVISAT-ASAR global monitoring mode data with 1 km resolution in the upper reaches of the HRB. Comparison analysis and validation during the period from 2008 to 2011 were performed by using SM observations at the A’rou freeze/thaw observation station established during the Watershed Allied Telemetry Experimental Research (WATER) experiment. In addition, airborne Polarimetric L-band Multi-beam Radiometer (PLMR) data was used to retrieve soil moisture in the middle reaches of HRB. Besides, we proposed a Bayesian probabilistic inversion approach to quantify the uncertainty of SM inversion and improve SM estimation by using dual-polarized TerraSAR-X observations.
A flux observation matrix established in the middle reaches of the HRB facilitate the investigation of ET over heterogeneous land surfaces. Based on eddy covariance (EC) and stable oxygen and hydrogen isotope measurements, corn transpiration and soil evaporation were estimated (and therefore were validated) by the two-source energy balance method and the surface energy balance system (SEBS) at the footprint and pixel scales. Besides, a separated parameter estimation scheme using Bayesian inference was proposed to remotely sensed estimations of latent heat flux and yielded reasonable parameter values; using cluster analysis, the most-likely values of the parameters can be effectively applied to estimate remotely sensed λET. In addition, we also presented a revised temporal scaling method based on a detection algorithm for the temporal stability of the evaporative fraction (EF) to estimate total daytime evapotranspiration (ET) at a regional scale.
In addition, we explored using Gravity Recovery and Climate Experiment (GRACE) gravity satellite data to quantitatively investigate recent drought dynamic over arid regions of Northwest China. Spatiotemporal characteristics of terrestrial water storage changes (TWSCs) were first evaluated based on the GRACE satellite data. A drought index, the total storage deficit index (TSDI), was derived based on the TWSC. The spatiotemporal distributions of drought events from 2003 to 2012 were obtained using the TSDI, and showed the study region experienced a severe long-term drought from May 2008 to December 2009. Furthermore, a comparison between TSDI and standardized precipitation index implied that GRACE TSDI was a more reliable integrated drought indicator in terms of considering total terrestrial water storages for large regions.
As a conclusion, synergetic efforts on the development and validation of RSPs of hydrological variables can apparently improve our understanding of terrestrial water cycle. These products, in combination with hydrology and land surface modeling will be integrated by data assimilation methods to precisely close the land water budget at the river basin scale.
A Statistical Perspective on the Spatio-temporal Variability of Global Soil Moisture Products
Research Centre Jülich, Institute of Bio- and Geosciences: Agrosphere (IBG-3), Jülich, Germany;
Different soil moisture products have been delivered in the recent years, e.g. ESA CCI, H-SAF ASCAT, ESA SMOS, NASA SMAP, NASA/JAXA AMSR-E/AMSR2, GLDAS-NOAH, NASA WindSat. These products are based on sensors operating at different microwave frequencies (X-, C-, L-band), recording different variables (brightness temperatures, radar backscatter), operation concepts (interferometric radiometer, conical scan radiometer, scatterometer), etc. The processors use different observation models (LPRM, CMEM, WARP) and deliver soil moisture information at different product levels.
Typical validation activities focus on absolute per-pixel accuracy by comparison to in situ networks, where the scale mismatch between local point and spaceborne area observation is often ignored. However, more important for several applications is to evaluate the information content according to soil moisture variability in spatial and temporal domain. This includes the analysis of soil moisture variability across spatial scales, where the logarithmic variance shows a linear relationship to the logarithmic extent scale. Comparisons to in situ observations indicate a strong underestimation of the variance for subcontinental studies.
Moreover, the shape of the function of soil moisture standard deviation over soil moisture mean contains information about the porosity variance in larger areas. By solving a closed-form expression of an unsaturated gravitational flow model (here, the Mualem-van Genuchten model), it is possible to predict the typically convex parabolic shape function based on soil maps. Communalities and differences of several global soil moisture products will be identified by this method. The resulting functions of soil moisture standard deviation over soil moisture mean have strong implications for considering sub-grid texture variability during downscaling processes.
AATSR Land Surface Temperature Product Validation Using Ground Measurements in China and Implications for SLSTR
1European Space Agency, Italy; 2Serco / co ESA-ESRIN, Italy; 3University of Electronic Science and Technology of China, China;
Land surface temperature (LST) is one of the most important parameters at the interface between the earth’s surface and the atmosphere. It acts as a sensitive indicator of climate change, and it is an essential input parameter for land surface models. Because of the intense variability at different spatial and temporal scales, satellite remote sensing provides the sole opportunity to acquire LSTs over large regions. Thus, how to estimate LSTs with the required accuracies and spatio-temporal resolutions using satellite data has become an objective in the field of quantitative remote sensing over the last four decades.
In recent decades, satellite LST products have been released by institutions such as the European Space Agency (ESA) and the National Aeronautics and Space Administration (NASA). These products have played important roles in researches of climate, meteorology, hydrology, environment, and disasters. Validation of the LST products is the necessary step before their applications conducted by scientific community and it is essential for the developers to revise the LST algorithms and thus improve the LST products.
Recently, there have been numerous field experiments in China and most of them have provided ground measurements of LSTs, which are good data sources for validating the satellite LST products. Such validation has been conducted for LST products generated by NASA and NOAA (e.g., MODIS and VIIRS LST products), while similar researches for the ESA’s LST products are still very rare. In addition, validation of LST products has been hindered due to many uncertainties and it has been limited to homogeneous surfaces. In fact, it is extremely difficult to find homogeneous surfaces with comparable scales to the satellite pixels with low spatial resolutions (e.g., 1000 m or coarser). Thus, researches in heterogeneous surfaces will benefit the validation of LST products.
In this context, this study aims to validate the ENVISAT Advanced Along-Track Scanning Radiometer (AATSR) LST product at a 1000-m spatial resolution using ground measurements in China. The LST products from 2008 and 2010 are taken into account. The ground measurements are from the North Arid and Semi-Arid Area Cooperative Experimental Observation Integrated Research program in China and the Watershed Allied Telemetry Experimental Research. The selected ground sites have various land cover types and different heterogeneity, e.g., grassland, barren land, cropland, and meadow. Optical remote sensing images with medium spatial resolutions, e.g. Landsat TM/ETM+, are utilized to quantify the heterogeneity of AATSR pixels.
Results demonstrate that the AATSR LST product has acceptable accuracies when validated with the ground measurements in China. However, its accuracy varies according to land cover types, surface heterogeneity, climatic background, and the considered season, because the land surface emissivity and atmospheric water vapor content have great influences on the estimated LSTs. Findings and the developed methods in this study will benefit the validation of LSTs provided by the upcoming Sea and Land Surface Temperature Radiometer (SLSTR) onboard the ESA’s Sentinel-3 satellite.
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Conference: 2016 Dragon 3 Final Results Symposium
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