Juan Gu

Improving Estimation of Evapotranspiration under Water-Limited Conditions Based on SEBS and MODIS Data in Arid Regions

This study proposes a method for improving the estimation of surface turbulent fluxes in surface energy balance system (SEBS) model under water stress conditions using MODIS data. The normalized difference water index (NDWI) as an indicator of water stress is integrated into SEBS. To investigate the feasibility of the new approach, the desert-oasis region in the middle reaches of the Heihe River Basin (HRB) is selected as the study area. The proposed model is calibrated with meteorological and flux data over 2008–2011 at the Yingke station and is verified with data from 16 stations of the Heihe Watershed Allied Telemetry Experimental Research (HiWATER) project in 2012. The results show that soil moisture significantly affects evapotranspiration (ET) under water stress conditions in the study area. Adding the NDWI in SEBS can significantly improve the estimations of surface turbulent fluxes in water-limited regions, especially for spare vegetation cover area. The daily ET maps generated by the new model also show improvements in drylands with low ET values. This study demonstrates that integrating the NDWI into SEBS as an indicator of water stress is an effective way to improve the assessment of the regional ET in semi-arid and arid regions.

Land Cover Classification in Heihe River Basin with Time Series - MODIS NDVI Data

Normalized difference vegetation index (NDVI) is a very important vegetation index, which has been widely applied in research regarding global environmental and climatic change. In this work, 16-Day L3 Global 1 km SIN Grid NDVI data sets in Heihe River Basin from MODIS vegetation index (VI) products (MOD13A2) during 2003-2005 are extracted and used for generating a one-year new NDVI data based on a simple three-point smoothing technique which can generally capture the annual feature of vegetation change. Then we obtain the independent component images by performing independent component analysis (ICA) transform on the smoothing NDVI data as a feature extractor. Then a support vector machine (SVM) is utilized to construct classifiers based on the ICA-extracted new features for land cover classification and a land cover map of Heihe river basin was obtained. At last, the accuracy assessment results prove that the classification framework proposed in this paper is efficient.

Estimation of Regional Soil Moisture by Assimilating Multi-Sensor Passive Microwave Remote Sensing Observations based on Ensemble Kalman Filter

We have developed Chinese land data assimilation system (CLDAS). In this system, the Common Land Model (CoLM) is used to simulate land surface processes. The radiative transfer models of thawed and frozen soil, snow, and vegetation are used as observation operators to transfer model predictions into estimated brightness temperatures. The EnKF algorithm is implemented as data assimilation method to integrate modeling and observation. The system is capable of assimilating passive microwave remotely sensed data such as special sensor microwave/imager (SSM/I) and advanced microwave scanning radiometer enhanced for EOS (AMSR-E). In this study, we primarily compare the assimilation results of soil moisture with AMSR-E L3 surface soil moisture products and in situ observations from GAME-Tibet experimental fields. The results indicate that the relationship between the simulated and assimilated surface soil moisture with AMSR-E L3 surface soil moisture products is very low. In comparison with in situ observations from GAME-Tibet experimental fields, the assimilated results of soil moisture are better than the simulated results. Additionally, the assimilated results can describe the thawed-frozen cycle.

Mapping daily evapotranspiration using ASTER and MODIS images based on data fusion over irrigated agricultural areas

In this study, the continuous daily ET at 90 m spatial resolution was estimated from the Surface Energy Balance System (SEBS) by using the land surface temperature and land surface reflectance of VNIR combining the Moderate Resolution Imaging Spectroradiometer (MODIS) and the Advanced Space-borne Thermal Emission Reflectance Radiometer (ASTER) spatiotemporal characteristics obtained from the Temporal Adaptive Reflectance Fusion Model (STARFM). Performance of this scheme used to estimate ET at high spatiotemporal resolution was validate over a heterogeneous oasis-desert regions by using in-situ observations from automatic meteorological systems (AMS) and eddy covariance (EC) systems in the middle reach of Heihe River Basin of Northwest China. The error introduced during the data fusion process based on STARFM is in an acceptable range for the predicted LST at 90 m spatial resolution. The surface energy fluxes estimated from SEBS using predicted remotely sensed data combing MODIS and ASTER spatiotemporal characteristics are agree well with observed surface energy fluxes from EC systems for all land cover types. The continuous daily ET estimated based on SEBS and STARFM seems to produce the general ET trends reasonably well for all land covers.

SWAT‐Based Hydrological Data Assimilation System (SWAT‐HDAS): Description and Case Application to River Basin‐Scale Hydrological Predictions

This paper presents the development and application of a physically based hydrological data assimilation system (HDAS) using the gridded and parallelized Soil and Water Assessment Tool (SWATGP) distributed hydrological model. This SWAT-HDAS software integrates remotely sensed data, including the leaf area index (LAI), snow cover fraction, snow water equivalent, soil moisture, and ground-based observational data (e.g., from discharge and ground sensor networks), with SWATGP and the Parallel Data Assimilation Framework (PDAF) to accurately characterize watershed hydrological states and fluxes. SWAT-HDAS employs high-performance computational technologies to address the computational challenges of high-resolution and/or large-area modeling. Multiple observational system simulation experiments (OSSEs), including soil moisture assimilation experiments, snow water equivalent assimilation experiments, and streamflow assimilation experiments, were designed to validate the assimilation efficiency of various types of observations within SWAT-HDAS using an ensemble Kalman filter (EnKF) algorithm. Both the temporal and spatial correlations in the trend/pattern and the magnitudes of improvement between the simulated and “true” states (i.e., for soil moisture, snow water equivalent, and discharge) were satisfactory using the integrated assimilation, which suggests the reliability of SWAT-HDAS for regional hydrology studies. The streamflow assimilation experiment also showed that the observation location dramatically influences the assimilation efficiency. The quantity and quality of observations have effects of varying degrees on the streamflow predictions. SWAT-HDAS is a promising tool for hydrological studies and applications under climate and environmental change scenarios.

Estimating regional evapotranspiration under water-limited conditions based on SEBS and MODIS data in arid regions

This study proposes a method for improving the estimation of surface turbulent fluxes in surface energy balance system (SEBS) model under water stress conditions using MODIS data. The normalized difference water index (NDWI) as an indicator of water stress is integrated into SEBS. To investigate the feasibility of the new approach, the desert-oasis region in the middle reaches of the Heihe River Basin (HRB) is selected as the study area. The proposed model is calibrated with meteorological and flux data over 2008- 2011 at the Yingke station and is verified with data from 16 stations of the Heihe Watershed Allied Telemetry Experimental Research (HiWATER) project in 2012. The results show that soil moisture significantly affects ET under water stress conditions in the study area. Adding the NDWI in SEBS can significantly improve the estimations of surface turbulent fluxes in water-limited regions especially for spare vegetation cover area. The daily ET maps generated by the new model also show improvements in drylands with low ET values. This study demonstrates that integrating the NDWI into SEBS as an indicator of water stress is an effective way to improve the assessment of the regional ET in semi-arid and arid regions.

Changes in satellite-derived vegetation growth trend in China from 2002 to 2010

Net primary production (NPP) is the production of organic compounds from atmospheric or aquatic carbon dioxide, principally through the process of photosynthesis. Climate changes of this magnitude are expected to affect the NPP of the world’s land ecosystems. In this study, we used a light-use efficiency model and linear regression model to describe and analyze the spatial and temporal patterns of terrestrial net primary productivity (NPP) in China during 2002-2010. First, we used the reconstructed 16-day 0.05°MODIS NDVI product (MOD13C1), 0.05°gridded GLDAS (Global Land Data Assimilation System) meteorological data and land use map to estimate the NPP in China. The spatial variability of NPP was analyzed during all periods, growing seasons and different seasons, respectively. Based on regression analysis method, we quantified the trend of NPP change in China during 2002-2010.

A Simplified Data Assimilation Method for Reconstructing Time-Series MODIS NDVI Data

Normalized difference vegetation index (NDVI) is the most widely used vegetation index due to its simplicity, ease of application, and wide-spread familiarity. Time-series NDVI products have been proven to be a powerful tool to learn from past events, monitor current natural-resource conditions, extract canopy biophysical parameters and forecast terrestrial ecosystems on different scales. However, the current NDVI product is still spatiotemporally discontinuous mainly due to cloud cover, seasonal snow and atmospheric variability. In this work, a simplified data assimilation method is proposed to reconstruct high-quality time-series MODIS NDVI data. Results indicate that the newly developed method is easy and effective in reconstructing high-quality MODIS NDVI time series.

ICA-based multi-temporal multi-spectral remote sensing images change detection

Change detection is the process of identifying difference in the scenes of an object or a phenomenon, by observing the same geographic region at different times. Many algorithms have been applied to monitor various environmental changes. Examples of these algorithms are difference image, ratio image, classification comparison, and change vector analysis. In this paper, a change detection approach for multi-temporal multi-spectral remote sensing images, based on Independent Component Analysis (ICA), is proposed. The environmental changes can be detected in reduced second and higher-order dependencies in multi-temporal remote sensing images by ICA algorithm. This can remove the correlation among multi-temporal images without any prior knowledge about change areas. Different kinds of land cover changes are obtained in these independent source images. The experimental results in synthetic and real multi-temporal multi-spectral images show the effectiveness of this change detection approach.