Soil Moisture Inversion Based on AMSR-E and MODIS Data Fusion: A Case Study of Huaihe River Basin

2014 ◽  
Vol 716-717 ◽  
pp. 1064-1067
Author(s):  
Jing Wen Xu ◽  
Yu Peng Wang ◽  
Jun Fang Zhao ◽  
Fei Yu Pu ◽  
Peng Wang
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Data Fusion ◽  
River Basin ◽  
Surface Condition ◽  
Remote Sensing Data ◽  
Huaihe River Basin ◽  
Huaihe River ◽  
Sensing Data ◽  
Modis Data

In this paper, the correlation between fused data and original data, the measured soil and the precipitation data over Huaihe river basin by exploring the inversion of soil moisture from the time and space based on the method of multi-source remote sensing data fusion has been studied. In order to fuse the AMSR-E data which is all-day and all-weather and can penetrate the earth surface to some extent, with the MODIS data that can reflect the surface condition and temperature characteristics, the method of wavelet fusion was carried out in MATLAB. The conclusions of this study are listed as follows: (1) the inversion result of the fused data based on AMSE-E and MODIS is much better than a single remote sensing data inversion; (2) the fused data based on AMSE-E and MODIS is sensitive to soil moisture change trend when the seasons alternated every year, especially in the spring, summer and autumn.

scholarly journals Urban Expansion Assessment in Huaihe River Basin, China, from 1998 to 2013 Using Remote Sensing Data

2017 ◽  
Vol 2017 ◽  
pp. 1-10
Author(s):  
Yong Fan ◽  
Xi Zhu ◽  
Zongyi He ◽  
Shengdi Zhang ◽  
Junbo Gao ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Urban Area ◽  
River Basin ◽  
Urban Expansion ◽  
Remote Sensing Data ◽  
Urban System ◽  
Human Society ◽  
Huaihe River Basin ◽  
Huaihe River ◽  
Sensing Data

Urbanization reflects the overall behavior of human society; thus, characterization of its associated spatial and temporal trends has been extensively researched. This study examines the process of urban expansion in the Huaihe River Basin (HRB) which is a key transition region within China’s urban system. In order to grasp the urban expansion process in different temporal sequences objectively, rapidly, and accurately, we used remote sensing data to assess the urban expansion in time and space. Urban expansion rules were defined for the urban area, urbanization intensification, extended dynamic degree, and spatial pattern. The research findings show that the urban area expansion speed was at medium level throughout the entire HRB and within each province. Presently, the formation of a whole urban agglomeration or urban system is not complete in the HRB; urban expansion in the HRB displayed space-time disequilibrium tendencies during 1998–2013.

Construction of Drought Indices Based on Passive Microwave Remote Sensing AMSR-E Data over Huaihe River Basin

2013 ◽  
Vol 433-435 ◽  
pp. 1813-1816
Author(s):  
Jing Wen Xu ◽  
Peng Wang ◽  
Jun Fang Zhao ◽  
Shuang Liu
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
River Basin ◽  
Microwave Remote Sensing ◽  
Passive Microwave ◽  
Impact Factors ◽  
Drought Indices ◽  
Huaihe River Basin ◽  
Huaihe River ◽  
Passive Microwave Remote Sensing

On the basis of previous work, this paper aims to build several proper drought indices based on the basic computation for the band information of passive microwave remote sensing AMSR-E data in Huaihe river basin. Compared with measured soil moisture, optimal drought indices have been selected to explore the spatio-temporal variation laws of drought conditions and its impact factors. The results indicate that there are satisfactory negative correlations between MPDIs (Microwave Polarization Difference Index) and observed soil moisture on the whole, which means the more negative the index is, the more serious the drought will be. Besides, MPDIs at frequency 69GHz and 187GHz calculated by AMSR-E brightness temperature data are much closer to the variation trend of soil moisture than those obtained from other bands.

scholarly journals Hydrological real-time modeling using remote sensing data

2010 ◽  
Vol 7 (6) ◽  
pp. 8809-8835
Author(s):  
P. Meier ◽  
A. Frömelt ◽  
W. Kinzelbach
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Real Time ◽  
River Basin ◽  
Time Lag ◽  
Remote Sensing Data ◽  
Modeling Framework ◽  
Sensing Data ◽  
Soil Storage ◽  
Time Modeling

Abstract. Reliable real-time forecasts of the discharge can provide valuable information for the management of a river basin system. Sequential data assimilation using the Ensemble Kalman Filter provides a both efficient and robust tool for a real-time modeling framework. One key parameter in a hydrological system is the soil moisture which recently can be characterized by satellite based measurements. A forecasting framework for the prediction of discharges is developed and applied to three different sub-basins of the Zambezi River Basin. The model is solely based on remote sensing data providing soil moisture and rainfall estimates. The soil moisture product used is based on the back-scattering intensity of a radar signal measured by the radar scatterometer on board the ERS satellite. These soil moisture data correlate well with the measured discharge of the corresponding watershed if the data are shifted by a time lag which is dependent on the size and the dominant runoff process in the catchment. This time lag is the basis for the applicability of the soil moisture data for hydrological forecasts. The conceptual model developed is based on two storage compartments. The processes modeled include evaporation losses, infiltration and percolation. The application of this model in a real-time modeling framework yields good results in watersheds where the soil storage is an important factor. For the largest watershed a forecast over almost six weeks can be provided. However, the quality of the forecast increases significantly with decreasing prediction time. In watersheds with little soil storage and a quick response to rainfall events the performance is relatively poor.

Construction of Drought Indices from Passive Microwave Remote Sensing AMSR-E Data in Huaihe River Basin

2013 ◽  
Vol 397-400 ◽  
pp. 2503-2506
Author(s):  
Rui Wang ◽  
Jing Wen Xu ◽  
Dan Wang ◽  
Xing Mei Xie ◽  
Peng Wang
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
River Basin ◽  
Microwave Remote Sensing ◽  
Passive Microwave ◽  
Drought Indices ◽  
Huaihe River Basin ◽  
Huaihe River ◽  
Passive Microwave Remote Sensing ◽  
Spatio Temporal

On the basis of previous work, this paper aims to build several proper drought indices based on passive microwave remote sensing AMSR-E data in Huaihe River Basin. Compared with measured soil moisture, optimal drought indices have been selected to explore the spatio-temporal variation of drought conditions. The results indicate that there are satisfactory negative correlations between MPDIs (Microwave Polarization Index) and observed soil moisture. Moreover, MPDIs calculated by bands of 69GHz and 187GHz are much closer to variation trend of soil moisture than those obtained by other bands.

scholarly journals Responses of Hydrological Processes under Different Shared Socioeconomic Pathway Scenarios in the Huaihe River Basin, China

Water ◽  
2021 ◽  
Vol 13 (8) ◽  
pp. 1053
Author(s):  
Yuan Yao ◽  
Wei Qu ◽  
Jingxuan Lu ◽  
Hui Cheng ◽  
Zhiguo Pang ◽  
...  
Keyword(s):  
Soil Moisture ◽  
River Basin ◽  
Climate Models ◽  
Global Climate Models ◽  
Hydrological Response ◽  
Huaihe River Basin ◽  
Huaihe River ◽  
Infiltration Capacity ◽  
Change Analysis ◽  
Average Annual Runoff

The Coupled Model Intercomparison Project Phase 6 (CMIP6) provides more scenarios and reliable climate change results for improving the accuracy of future hydrological parameter change analysis. This study uses five CMIP6 global climate models (GCMs) to drive the variable infiltration capacity (VIC) model, and then simulates the hydrological response of the upper and middle Huaihe River Basin (UMHRB) under future shared socioeconomic pathway scenarios (SSPs). The results show that the five-GCM ensemble improves the simulation accuracy compared to a single model. The climate over the UMHRB likely becomes warmer. The general trend of future precipitation is projected to increase, and the increased rates are higher in spring and winter than in summer and autumn. Changes in annual evapotranspiration are basically consistent with precipitation, but seasonal evapotranspiration shows different changes (0–18%). The average annual runoff will increase in a wavelike manner, and the change patterns of runoff follow that of seasonal precipitation. Changes in soil moisture are not obvious, and the annual soil moisture increases slightly. In the intrayear process, soil moisture decreases slightly in autumn. The research results will enhance a more realistic understanding of the future hydrological response of the UMHRB and assist decision-makers in developing watershed flood risk-management measures and water and soil conservation plans.

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