scholarly journals Variability of Surface Soil Moisture Observed from Multitemporal C-Band Synthetic Aperture Radar and Field Data

2010 ◽  
Vol 9 (4) ◽  
pp. 1014-1024 ◽  
Author(s):  
Christian N. Koyama ◽  
Wolfgang Korres ◽  
Peter Fiener ◽  
Karl Schneider
Keyword(s):  
Soil Moisture ◽  
Synthetic Aperture Radar ◽  
Field Data ◽  
Surface Soil ◽  
Synthetic Aperture ◽  
Surface Soil Moisture ◽  
Aperture Radar

scholarly journals Seven Years of Advanced Synthetic Aperture Radar (ASAR) Global Monitoring (GM) of Surface Soil Moisture over Africa

2014 ◽  
Vol 6 (8) ◽  
pp. 7683-7707 ◽  
Author(s):  
Alena Dostálová ◽  
Marcela Doubková ◽  
Daniel Sabel ◽  
Bernhard Bauer-Marschallinger ◽  
Wolfgang Wagner
Keyword(s):  
Soil Moisture ◽  
Synthetic Aperture Radar ◽  
Surface Soil ◽  
Synthetic Aperture ◽  
Surface Soil Moisture ◽  
Aperture Radar

Evaluation of the ERS 1/Synthetic Aperture Radar Capacity to Estimate Surface Soil Moisture: Two-Year Results Over the Naizin Watershed

1995 ◽  
Vol 31 (4) ◽  
pp. 975-982 ◽  
Author(s):  
Anne-Laure Cognard ◽  
Cécile Loumagne ◽  
Michel Normand ◽  
Philippe Olivier ◽  
Catherine Ottlé ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Synthetic Aperture Radar ◽  
Surface Soil ◽  
Synthetic Aperture ◽  
Surface Soil Moisture ◽  
Aperture Radar

Estimation of Synthetic Aperture Radar (SAR) soil moisture with the use of fractal roughness

2021 ◽  
Author(s):  
Ju Hyoung Lee ◽  
Notarnicola Claudia ◽  
Jeff Walker
Keyword(s):  
Spatial Distribution ◽  
Soil Moisture ◽  
Synthetic Aperture Radar ◽  
Synthetic Aperture ◽  
Accurate Estimation ◽  
Distribution Data ◽  
Fractal Surface ◽  
Fractal Method ◽  
Local Point ◽  
Aperture Radar

<p>To estimate surface soil moisture from Sentinel-1 backscattering, accurate estimation of soil roughness is a key. However, it is usually error source, due to complexity of surface heterogeneity. This study investigates the fractal methods that takes multi-scale roughness into account. Fractal models are widely recognized as one of the best approaches to depict soil roughness of natural system. Unlike the conventional approach of fractal method that uses local roughness measured in the field or Digital Elevation Model information seldom considering a stochastic characteristic of soil surface, fractal surface is generated with the roughness spatially inverted from Synthetic Aperture Radar (SAR) backscatter. Assuming that the land surface in study site is on small to intermediate scales, pseudo-roughness is spatially estimated by modelling SAR roughness with the one-sided power-law spectrum. In addition, it is assumed that both multiple and single scales of roughness affect SAR backscatter in an integrative way. For validation, soil moisture is retrieved with this time-varying roughness. Based upon local validation and cost minimization, as compared with an inversion approach of surface scattering models (Integral Equation Model), a fractal method seems geometrically more sensible than an inversion, based upon a spatial distribution and a priori knowledge in the field. Although inverted roughness is used as an input, fractal model does not reproduce the same roughness. Results will show local point validation, fractal surface, and estimation of coefficients, and various spatial distribution data. This study will be useful for future satellite missions such as NASA-ISRO SAR mission.</p>

scholarly journals Combined Use of GF-3 and Landsat-8 Satellite Data for Soil Moisture Retrieval over Agricultural Areas Using Artificial Neural Network

2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Qingyan Meng ◽  
Linlin Zhang ◽  
Qiuxia Xie ◽  
Shun Yao ◽  
Xu Chen ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Soil Moisture ◽  
Synthetic Aperture Radar ◽  
Synthetic Aperture ◽  
Landsat 8 ◽  
Combined Use ◽  
Artificial Neural ◽  
Agricultural Areas ◽  
Aperture Radar

Soil moisture is the basic condition required for crop growth and development. Gaofen-3 (GF-3) is the first C-band synthetic-aperture radar (SAR) satellite of China, offering broad land and ocean imaging applications, including soil moisture monitoring. This study developed an approach to estimate soil moisture in agricultural areas from GF-3 data. An inversion technique based on an artificial neural network (ANN) is introduced. The neural network was trained and tested on a training sample dataset generated from the Advanced Integral Equation Model. Incidence angle and HH or VV polarization data were used as input variables of the ANN, with soil moisture content (SMC) and surface roughness as the output variables. The backscattering contribution from the vegetation was eliminated using the water cloud model (WCM). The acquired soil backscattering coefficients of GF-3 and in situ measurement data were used to validate the SMC estimation algorithm, which achieved satisfactory results (R2 = 0.736; RMSE = 0.042). These results highlight the contribution of the combined use of the GF-3 synthetic-aperture radar and Landsat-8 images based on an ANN method for improving SMC estimates and supporting hydrological studies.

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