A new mathematical formulation for remote sensing of soil moisture based on the Red-NIR space. Issue 20 (17th October 2020)

• Record Type: Journal Article
• Authors: Foroughi, Hassan; Naseri, Abd Ali; Boroomand Nasab, Saeed; Hamzeh, Saeid; Sadeghi, Morteza; Tuller, Markus; Jones, Scott B.
• Is Part Of: International journal of remote sensing. Volume 41:Issue 20(2020)
• Page Start: 8034
• Page End: 8047
• Publication Date: 2020-10-17
• Subjects: Remote sensing -- Periodicals; Télédétection -- Périodiques; 621.3678
• DOI: 10.1080/01431161.2020.1770365
• Languages: English
• ISSNs: 0143-1161

Abstract

ABSTRACT: Optical remote sensing of earth surface processes commonly relies on the red, green, blue (RGB), near-infrared (NIR) and shortwave-infrared (SWIR) electromagnetic bands. Most of the optical sensors mounted on unmanned aerial vehicles and satellites provide the RGB and NIR bands, but only a few offer SWIR output. The Red-NIR reflectance space has been widely applied for remote sensing of various land surface variables including soil moisture. The linear relationship between the Red-NIR reflectance of bare soil is established as the base and then moisture isolines are assumed perpendicular to the soil line. In this study, we show that this assumption is not consistent with the actual Red-NIR space geometry, which in many cases introduces soil moisture estimation errors. To overcome this limitation, we propose a new mathematical transformation to the original Red-NIR space followed by newly defined soil moisture isolines that are more consistent with the actual observations. This new Transformed Red-NIR (TRN) model is compared with the Conventional Red-NIR (CRN) model using data from a sugarcane field located in southwestern Iran. Twelve Land Remote-Sensing Satellite (Landsat)-8 images were acquired during the sugarcane growth season. For validation of the remotely sensed data, ground reference soil moisture was determined at 22 locations at five different depths via core sampling and oven-drying. Our results indicate that the TRN model significantly improves the accuracy of remotely sensed soil moisture.