Spatiotemporal monitoring of surface soil moisture using optical remote sensing data: a case study in a semi-arid area. Issue 3 (1st September 2020)

• Record Type: Journal Article
• Authors: Khellouk, Rida; Barakat, Ahmed; Boudhar, Abdelghani; Hadria, Rachid; Lionboui, Hayat; El Jazouli, Aafaf; Rais, Jamila; El Baghdadi, Mohamed; Benabdelouahab, Tarik
• Is Part Of: Journal of spatial science. Volume 65:Issue 3(2020)
• Page Start: 481
• Page End: 499
• Publication Date: 2020-09-01
• Subjects: Remote sensing -- SSMC -- spectral indices -- Tadla irrigated perimeter; Geographic information systems -- Periodicals; Cartography -- Periodicals; Surveying -- Periodicals; Geodesy -- Periodicals; Photogrammetry -- Periodicals; Cartography; Geodesy; Geographic information systems; Photogrammetry; Surveying; Periodicals; 526.05
• DOI: 10.1080/14498596.2018.1499559
• Languages: English
• ISSNs: 1449-8596

Abstract

ABSTRACT: Surface soil moisture content (SSMC) monitoring constitutes an important parameter to estimate crop water requirements, especially in arid and semi-arid areas. Remote sensing became a useful tool for estimating SSMC. Two approaches were applied for monitoring the SSMC during the 2013/14 cropping season in the irrigated perimeter of Tadla (Morocco) using multispectral bands of Landsat-8 OLI images. The first approach examined the potential of visible and short-wave infrared drought index (VSDI), normalized multi-band drought index (NMDI) and short-wave infrared water stress index (SIWSI), to retrieve SSMC. The second approach attempted to develop a new SSMC model based on evaluation of the correlations between multispectral bands and measured SSMC using a stepwise multiple regression analysis. Results showed that the established model is highly correlated with the measured SSMC at all crop growth stages with R 2 of 0.87, 0.85 and 0.89, for bare soil, partially covered and entirely covered by vegetation, respectively.