Estimating soil moisture in the agricultural areas using RADARSAT-2 Quad-olarization SAR data

2016 ◽  
Author(s):  
Jianwei Ma ◽  
Shifeng Huang ◽  
Jiren Li ◽  
Xiaotao Li ◽  
Xiaoning Song ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Sar Data ◽  
Agricultural Areas

scholarly journals The Potential Use of Multi-Band SAR Data for Soil Moisture Retrieval over Bare Agricultural Areas: Hebei, China

2015 ◽  
Vol 8 (1) ◽  
pp. 7 ◽  
Author(s):  
Xiang Zhang ◽  
Baozhang Chen ◽  
Hongdong Fan ◽  
Jilei Huang ◽  
Hui Zhao
Keyword(s):  
Soil Moisture ◽  
Sar Data ◽  
Agricultural Areas ◽  
Potential Use ◽  
Multi Band

scholarly journals Multitemporal Soil Moisture Retrieval over Bare Agricultural Areas by Means of Alpha Model with Multisensor SAR Data

2018 ◽  
Vol 2018 ◽  
pp. 1-17 ◽  
Author(s):  
Xiang Zhang ◽  
Xinming Tang ◽  
Xiaoming Gao ◽  
Hui Zhao
Keyword(s):  
Time Series ◽  
Soil Moisture ◽  
Root Mean Square Error ◽  
Field Measurements ◽  
Mean Square ◽  
Approximation Model ◽  
Unknown Parameters ◽  
Sar Data ◽  
Agricultural Areas ◽  
Moisture Estimation

The objective of this research is to optimize the Alpha approximation model for soil moisture retrieval using multitemporal SAR data. The Alpha model requires prior knowledge of soil moisture range to constrain soil moisture estimation. The solution of the Alpha model is an undetermined problem due to the fact that the number of observation equations is less than the number of unknown parameters. This research primarily focused on the optimization of Alpha model by employing multisensor and multitemporal SAR data. The disadvantage of the Alpha model can be eliminated by the combination of multisensor SAR data. The optimized Alpha model was evaluated on the basis of a comprehensive campaign for soil moisture retrieval, which acquired multisensor time series SAR data and coincident field measurements. The agreement between the estimated and measured soil moisture was within a root mean square error of 0.08 cm3/cm3 for both methods. The optimized Alpha model shows an obvious improvement for soil moisture retrieval. The results demonstrated that multisensor and multitemporal SAR data are favorable for time series soil moisture retrieval over bare agricultural areas.

Physical-based soil moisture retrieval method over bare agricultural areas by means of multi-sensor SAR data

2018 ◽  
Vol 39 (12) ◽  
pp. 3870-3890 ◽  
Author(s):  
Xiang Zhang ◽  
Baozhang Chen ◽  
Hui Zhao ◽  
Tao Li ◽  
Qianfu Chen
Keyword(s):  
Soil Moisture ◽  
Retrieval Method ◽  
Sar Data ◽  
Agricultural Areas

Potential of soil moisture estimation using C-band polarimetric SAR data in arid regions

2018 ◽  
Vol 40 (5-6) ◽  
pp. 2138-2150 ◽  
Author(s):  
Liping Yang ◽  
Xiaodong Feng ◽  
Fei Liu ◽  
Jing Liu ◽  
Xiaohui Sun
Keyword(s):  
Soil Moisture ◽  
Arid Regions ◽  
Polarimetric Sar ◽  
Sar Data ◽  
Moisture Estimation

scholarly journals Potential Applications of GNSS-R Observations over Agricultural Areas: Results from the GLORI Airborne Campaign

2018 ◽  
Vol 10 (8) ◽  
pp. 1245 ◽  
Author(s):  
Mehrez Zribi ◽  
Erwan Motte ◽  
Nicolas Baghdadi ◽  
Frédéric Baup ◽  
Sylvia Dayau ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Signal To Noise Ratio ◽  
Soil Surface ◽  
Satellite System ◽  
Area Index ◽  
Vegetation Height ◽  
Study Sites ◽  
Global Navigation Satellite ◽  
Vegetation Characteristic ◽  
Agricultural Areas

The aim of this study is to analyze the sensitivity of airborne Global Navigation Satellite System Reflectometry (GNSS-R) on soil surface and vegetation cover characteristics in agricultural areas. Airborne polarimetric GNSS-R data were acquired in the context of the GLORI’2015 campaign over two study sites in Southwest France in June and July of 2015. Ground measurements of soil surface parameters (moisture content) and vegetation characteristics (leaf area index (LAI), and vegetation height) were recorded for different types of crops (corn, sunflower, wheat, soybean, vegetable) simultaneously with the airborne GNSS-R measurements. Three GNSS-R observables (apparent reflectivity, the reflected signal-to-noise-ratio (SNR), and the polarimetric ratio (PR)) were found to be well correlated with soil moisture and a major vegetation characteristic (LAI). A tau-omega model was used to explain the dependence of the GNSS-R reflectivity on both the soil moisture and vegetation parameters.

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