Next Article in Journal
Impacts of Urbanization on Vegetation Phenology over the Past Three Decades in Shanghai, China
Previous Article in Journal
Developments in Landsat Land Cover Classification Methods: A Review
Article Menu
Issue 9 (September) cover image

Export Article

Open AccessFeature PaperArticle
Remote Sens. 2017, 9(9), 969; doi:10.3390/rs9090969

Calibration of the Water Cloud Model at C-Band for Winter Crop Fields and Grasslands

1
Institut National de Recherche en Sciences et Technologies Pour l’Environnement et l’Agriculture (IRSTEA), UMR TETIS, 500 rue François Breton, 34093 Montpellier CEDEX 5, France
2
French National Centre for Scientific Research (CESBIO), 18 av. Edouard Belin, bpi 2801, 31401 Toulouse CEDEX 9, France
3
Institut National Agronomique de Tunis, Université de Carthage, Tunis, Tunisia
*
Author to whom correspondence should be addressed.
Received: 21 August 2017 / Revised: 12 September 2017 / Accepted: 18 September 2017 / Published: 20 September 2017
(This article belongs to the Section Remote Sensing in Agriculture and Vegetation)
View Full-Text   |   Download PDF [2620 KB, uploaded 20 September 2017]   |  

Abstract

In a perspective to develop an inversion approach for estimating surface soil moisture of crop fields from Sentinel-1/2 data (radar and optical sensors), the Water Cloud Model (WCM) was calibrated from C-band Synthetic Aperture Radar (SAR) data and Normalized Difference Vegetation Index (NDVI) values collected over crops fields and grasslands. The soil contribution that depends on soil moisture and surface roughness (in addition to SAR instrumental parameters) was simulated using the physical backscattering model IEM (Integral Equation Model). The vegetation descriptor used in the WCM is the NDVI because it can be directly calculated from optical images. A large dataset consisting of radar backscattered signal in Vertical transmit and Vertical receive (VV) and Vertical transmit and Horizontal receive (VH) polarizations with wide range of incidence angle, soil moisture, surface roughness, and NDVI-values was used. It was collected over two agricultural study sites. Results show that the soil contribution to the total radar backscattered signal is lower in VH than in VV because VH is more sensitive to vegetation cover. Thus, the use of VH alone or in addition to VV for retrieving the soil moisture is not advantageous in presence of well-developed vegetation cover. View Full-Text
Keywords: water cloud model; integral equation model; SAR; C-band; crops; grasslands; soil moisture water cloud model; integral equation model; SAR; C-band; crops; grasslands; soil moisture
Figures

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Baghdadi, N.; El Hajj, M.; Zribi, M.; Bousbih, S. Calibration of the Water Cloud Model at C-Band for Winter Crop Fields and Grasslands. Remote Sens. 2017, 9, 969.

Show more citation formats Show less citations formats

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

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Remote Sens. EISSN 2072-4292 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top