Rice Inundation Assessment Using Polarimetric UAVSAR Data

Abstract

Rice cultivation practices in the United States typically include managing irrigation water for inundated conditions. In the Arkansas Delta region, for example, rice fields can require 5-20 cm of inundation for seeding, with intermittent or continuous flooding during growth phases until drainage near harvest, and between rotations for wildfowl habitat or land maintenance (Reba et al., 2020). Irrigation practices, access to water, regulatory policies, and infrastructure vary across the Midsouth USA resulting in many unique combinations of landforms, irrigation regimes, and water sources. When using flood irrigation, producers have challenges managing and measuring water quantities at the field or paddy level. One well or well outlet can irrigate multiple fields, water is gravity-driven from field to field, underground pipe and plumbing infrastructure can use a combination of ground and surface water, and surface poly-pipe can move water from field to field. In Arkansas, total water use throughout the growing season can range from 382 mm on zero grade (fields without of slope) to 1,034 mm for contour levees (Smith et al., 2007). Regardless of the approach to irrigation, the ground water supply is depleting faster than recharge (Kresse et al., 2014), and public private partnerships are investigating tradeoffs across irrigation regimes, greenhouse gas emissions, water quantity, and management practices (Nalley et al., 2015; Reba et al., 2013; Reba & Massey, 2020). To scale these field experiments the science community and public and private sectors can benefit from synoptic, large area metrics on rice field inundation conditions. Traditionally, optical satellite remote sensing tools have focused on identifying surface or open water conditions. These sensors and techniques struggle when postharvest residues are present or once crop emergence

Flood Depth Estimation during Hurricane Harvey Using Sentinel-1 and UAVSAR Data

“…The JPL UAVSAR observations covered the entire city and surrounding areas operating at a 1.26 GHz frequency (https://uavsar.jpl.nasa.gov/cgi-bin/data.pl accessed on 1 October 2017). UAVSAR is used in this study as it has a high spatial resolution (1.8 m × 0.8 m) and long wavelength (23.8 cm) [19]. The UAVSAR data from the NASA L-band airborne SAR provide repeat-track interferometric observations from a pod-mounted polarimetric instrument originally designed to operate on an unmanned aerial vehicle (UAV) at an altitude of 13,800 m with a bandwidth of 80 MHz (https://directory.eoportal.org/web/ eoportal/airborne-sensors/uavsar accessed on 1 October 2017).…”

Responses of red rice paddy under low nutrient supply in type B tidal swamp

“…The rice varieties have elongated properties during submersion, so food reserves are reduced and affect dry weight so that sufficient nutrients are needed 42,43 . Susceptible varieties will experience physiological disturbances due to inundation, affecting growth in both the vegetative and generative phases 44 . Low nutrient supply also affects the absorption of Fe levels in the roots and canopy (Table 4); tolerant varieties have higher levels of Fe absorbed by roots than susceptible varieties, as well as the levels of Fe in the crown for sensitive varieties will absorb more Fe.…”

Random Forest Classification of Inundation Following Hurricane Florence (2018) via L-Band Synthetic Aperture Radar and Ancillary Datasets

“…The radar emits energy in one phase and detects backscatter from the same or opposite orientation, and these components are used to derive a complex scattering matrix [29]. Compared to single-and dual-pol configurations, the scattering mechanisms present in fully polarimetric SAR provide richer detail on surface structures [30]. Different types of ground cover cause the energy transmitted by the sensor to be returned in single, double, and volumetric scattering mechanisms, visualized in Figure 2.…”

“…In this study, an overall RF classification accuracy of 87.62% was attained using the Freeman-Durden decomposition and ancillary vegetation data. Inundation in rice fields was detected with a UA and PA of 86.35% and 74.85%, respectively [30]. The scikit-learn RF algorithm uses a unitless Gini Index to estimate the importance of input features used in the decision-making process by measuring the decrease in accuracy observed from removing each element [47].…”