Monitoring permafrost soil moisture with multi-temporal TERRASAR-X data in northern tibet

2016 ◽  
Author(s):  
Chao Wang ◽  
Hong Zhang ◽  
Qingbai Wu ◽  
Zhengjia Zhang ◽  
Lei Xie
Keyword(s):  
Soil Moisture ◽  
Permafrost Soil ◽  
Northern Tibet ◽  
Multi Temporal

scholarly journals Permafrost Soil Moisture Monitoring Using Multi-Temporal TerraSAR-X Data in Beiluhe of Northern Tibet, China

2018 ◽  
Vol 10 (10) ◽  
pp. 1577 ◽  
Author(s):  
Chao Wang ◽  
Zhengjia Zhang ◽  
Simonetta Paloscia ◽  
Hong Zhang ◽  
Fan Wu ◽  
...  
Keyword(s):  
Time Series ◽  
Soil Moisture ◽  
Winter Season ◽  
Tibet Plateau ◽  
Permafrost Soil ◽  
Permafrost Region ◽  
Backscattering Coefficient ◽  
Sar Images ◽  
Northern Tibet ◽  
Variation Patterns

Global change has significant impact on permafrost region in the Tibet Plateau. Soil moisture (SM) of permafrost is one of the most important factors influencing the energy flux, ecosystem, and hydrologic process. The objectives of this paper are to retrieve the permafrost SM using time-series SAR images, without the need of auxiliary survey data, and reveal its variation patterns. After analyzing the characteristics of time-series radar backscattering coefficients of different landcover types, a two-component SM retrieval model is proposed. For the alpine meadow area, a linear retrieving model is proposed using the TerraSAR-X time-series images based on the assumption that the lowest backscattering coefficient is measured when the soil moisture is at its wilting point and the highest backscattering coefficient represents the water-saturated soil state. For the alpine desert area, the surface roughness contribution is eliminated using the dual SAR images acquired in the winter season with different incidence angles when retrieving soil moisture from the radar signal. Before the model implementation, landcover types are classified based on their backscattering features. 22 TerraSAR-X images are used to derive the soil moisture in Beiluhe, Northern Tibet with different incidence angles. The results obtained from the proposed method have been validated using in-situ soil moisture measurements, thus obtaining RMSE and Bias of 0.062 cm3/cm3 and 4.7%, respectively. The retrieved time-series SM maps of the study area point out the spatial and temporal SM variation patterns of various landcover types.

Minimizing soil moisture variations in multi-temporal airborne imaging spectrometer data for digital soil mapping

Geoderma ◽  
2019 ◽  
Vol 337 ◽  
pp. 607-621 ◽  
Author(s):  
Sanne Diek ◽  
Sabine Chabrillat ◽  
Marco Nocita ◽  
Michael E. Schaepman ◽  
Rogier de Jong
Keyword(s):  
Soil Moisture ◽  
Soil Mapping ◽  
Digital Soil Mapping ◽  
Imaging Spectrometer ◽  
Multi Temporal ◽  
Airborne Imaging ◽  
Spectrometer Data

scholarly journals MONITORING OF TIME-SERIES SOIL MOISTURE BASED ON ADVANCED DINSAR

2021 ◽  
Vol V-3-2021 ◽  
pp. 51-55
Author(s):  
C. H. Yang ◽  
A. Müterthies
Keyword(s):  
Time Series ◽  
Soil Moisture ◽  
Water Content ◽  
Large Scale ◽  
Pearson Correlation ◽  
Correlation Coefficients ◽  
Organic Soil ◽  
Underground Water ◽  
Multi Temporal ◽  
Mean Square Errors

Abstract. Understanding soil moisture is essential for earth and environmental sciences especially in geology, hydrology, and meteorology. Remote sensing techniques are widely applied to large-scale monitoring tasks. Among them, DInSAR using multi-temporal spaceborne SAR images is able to derive surface movement up to mm level over an area. One of the factors inducing the movement is variation of soil moisture. Based on this, a semi-empirical approach can be tailored to retrieve the underground water content. However, the derived movement is often contaminated with other irrelevant noise. Besides, a time-series analysis could not be simply implemented without additional fusion and calibration. In this paper, we propose a novel modelling based on advanced DInSAR to solve these problems. The irrelevant noise will be removed as parts of the modelled elements in the DInSAR processing. A forward model on a scene is built by regressing the measured soil moisture on the DInSAR-derived movement series. We tested our approach using Sentinel-1 images in the grasslands of organic soil within State of Brandenburg, Germany. The Pearson correlation coefficients between the measured soil moistures and the DInSAR-derived movements are up to 0.91. The mean square errors of the predicted soil moistures compared with the measurements reach 3.03 % (volumetric water content) at best. Our study shows a promising new concept to develop a global monitoring of soil moisture in the future.

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