Next Article in Journal
A Prediction Smooth Method for Blending Landsat and Moderate Resolution Imagine Spectroradiometer Images
Next Article in Special Issue
Sensitivity of Sentinel-1 Backscatter to Vegetation Dynamics: An Austrian Case Study
Previous Article in Journal
On the Very High-Resolution Radar Image Statistics of the Exponentially Correlated Rough Surface: Experimental and Numerical Studies
Previous Article in Special Issue
Soil Moisture from Fusion of Scatterometer and SAR: Closing the Scale Gap with Temporal Filtering
Open AccessArticle

Using SAR-Derived Vegetation Descriptors in a Water Cloud Model to Improve Soil Moisture Retrieval

Canada Centre for Mapping and Earth Observation, Natural Resources Canada, Ottawa, ON K1A 0E4, Canada
*
Author to whom correspondence should be addressed.
Remote Sens. 2018, 10(9), 1370; https://doi.org/10.3390/rs10091370
Received: 26 June 2018 / Revised: 14 August 2018 / Accepted: 24 August 2018 / Published: 29 August 2018
The water cloud model (WCM) is a widely used radar backscatter model applied to SAR images to retrieve soil moisture over vegetated areas. The WCM needs vegetation descriptors to account for the impact of vegetation on SAR backscatter. The commonly used vegetation descriptors in WCM, such as Leaf Area Index (LAI) and Normalized Difference Vegetation Index (NDVI), are sometimes difficult to obtain due to the constraints in data availability in in-situ measurements or weather dependency in optical remote sensing. To improve soil moisture retrieval, this study investigates the feasibility of using all-weather SAR derived vegetation descriptors in WCM. The in-situ data observed at an agricultural crop region south of Winnipeg in Canada, RapidEye optical images and dual-polarized Radarsat-2 SAR images acquired in growing season were used for WCM model calibration and test. Vegetation descriptors studied include HV polarization backscattering coefficient ( σ H V ° ) and Radar Vegetation Index (RVI) derived from SAR imagery, and NDVI derived from optical imagery. The results show that σ H V ° achieved similar results as NDVI but slightly better than RVI, with a root mean square error of 0.069 m3/m3 and a correlation coefficient of 0.59 between the retrieved and observed soil moisture. The use of σ H V ° can overcome the constraints of the commonly used vegetation descriptors and reduce additional data requirements (e.g., NDVI from optical sensors) in WCM, thus improving soil moisture retrieval and making WCM feasible for operational use. View Full-Text
Keywords: soil moisture; Radarsat-2; SAR; water-cloud model; vegetation descriptor soil moisture; Radarsat-2; SAR; water-cloud model; vegetation descriptor
Show Figures

Figure 1

MDPI and ACS Style

Li, J.; Wang, S. Using SAR-Derived Vegetation Descriptors in a Water Cloud Model to Improve Soil Moisture Retrieval. Remote Sens. 2018, 10, 1370. https://doi.org/10.3390/rs10091370

AMA Style

Li J, Wang S. Using SAR-Derived Vegetation Descriptors in a Water Cloud Model to Improve Soil Moisture Retrieval. Remote Sensing. 2018; 10(9):1370. https://doi.org/10.3390/rs10091370

Chicago/Turabian Style

Li, Junhua; Wang, Shusen. 2018. "Using SAR-Derived Vegetation Descriptors in a Water Cloud Model to Improve Soil Moisture Retrieval" Remote Sens. 10, no. 9: 1370. https://doi.org/10.3390/rs10091370

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Search more from Scilit
 
Search
Back to TopTop