A New Optical Remote Sensing Technique for High-Resolution Mapping of Soil Moisture

Ebrahim Babaeian; Paheding Sidike; Maria S Newcomb; Maitiniyazi Maimaitijiang; Scott A White; Jeffrey Demieville; Richard W Ward; Morteza Sadeghi; David S LeBauer; Scott B Jones; Vasit Sagan; Markus Tuller

doi:10.3389/fdata.2019.00037

A New Optical Remote Sensing Technique for High-Resolution Mapping of Soil Moisture

Front Big Data. 2019 Nov 5:2:37. doi: 10.3389/fdata.2019.00037. eCollection 2019.

Affiliations

¹ Department of Environmental Science, The University of Arizona, Tucson, AZ, United States.
² Department of Electrical and Computer Engineering, Purdue University Northwest, Hammond, IN, United States.
³ School of Plant Sciences, The University of Arizona, Tucson, AZ, United States.
⁴ Department of Earth and Atmospheric Science, Saint Louis University, St. Louis, MO, United States.
⁵ Department of Civil, Environmental and Geo-Engineering, University of Minnesota, Minneapolis, MN, United States.
⁶ Arizona Experiment Station, The University of Arizona, Tucson, AZ, United States.
⁷ Department of Plants, Soils and Climate, Utah State University, Logan, UT, United States.

Abstract

The recently developed OPtical TRApezoid Model (OPTRAM) has been successfully applied for watershed scale soil moisture (SM) estimation based on remotely sensed shortwave infrared (SWIR) transformed reflectance (TR_SWIR) and the normalized difference vegetation index (NDVI). This study is aimed at the evaluation of OPTRAM for field scale precision agriculture applications using ultrahigh spatial resolution optical observations obtained with one of the world's largest field robotic phenotyping scanners located in Maricopa, Arizona. We replaced NDVI with the soil adjusted vegetation index (SAVI), which has been shown to be more accurate for cropped agricultural fields that transition from bare soil to dense vegetation cover. The OPTRAM was parameterized based on the trapezoidal geometry of the pixel distribution within the TR_SWIR-SAVI space, from which wet- and dry-edge parameters were determined. The accuracy of the resultant SM estimates is evaluated based on a comparison with ground reference measurements obtained with Time Domain Reflectometry (TDR) sensors deployed to monitor surface, near-surface and root zone SM. The obtained results indicate an SM estimation error between 0.045 and 0.057 cm³ cm^-3 for the near-surface and root zone, respectively. The high resolution SM maps clearly capture the spatial SM variability at the sensor locations. These findings and the presented framework can be applied in conjunction with Unmanned Aerial System (UAS) observations to assist with farm scale precision irrigation management to improve water use efficiency of cropping systems and conserve water in water-limited regions of the world.

Keywords: OPTRAM; TERRA-REF; high-resolution; precision irrigation; remote sensing; soil moisture.