The Importance of Surface Canopy Water on Agricultural Monitoring using SAR

Abstract

The launch of sun-synchronized satellites such as ESA’s Sentinel-1 mission, the Radarsat Constellation Mission (RCM), and future missions such as NiSAR and ROSE-L improve the potential of near real-time agricultural monitoring. Furthermore, new SAR systems in Low Earth Orbit (LEO) such as those from Iceye and CapellaSpace could provide new opportunities for sub-daily monitoring of soil and vegetation. However, the presence of surface canopy water (SCW), dew or interception, during single acquisitions can affect the relationship between radar observables and crop biophysical variables and also can affect the attenuation of the microwave signal through the vegetation layer.

The aim of this study was to quantify the influence of surface canopy water (SCW) on radar observables and on the relationship between L-band radar backscatter and biophysical variables of interest in agricultural monitoring. In addition, the effect of SCW on vegetation optical depth (VOD) estimation and on the linear relationship between VOD and vegetation water content (VWC) was analyzed.

In order to conduct this analysis, an intensive fieldwork campaign was performed in Florida, USA, during a full growing season of corn. Fully polarimetric L-band data were collected 32 times per day using a truck-mounted scatterometer. To capture vegetation water dynamics and dry biomass, pre-dawn destructive sampling was conducted three times a week, and plant geometry was measured once a week for a full growing season. Three leaf wetness sensors, installed on different heights, were used for continuous monitoring of SCW. Soil moisture, meteorological data, and SCW were measured every 15 minutes for the entire growing season.

Results show that the presence of SCW can result in an increase in backscatter of up to 2-3 dB, and also affect the relationship between radar observables and crop biophysical variables. In corn, the spearman rank correlation between backscatter and biophysical variables is, on average, about 0.2 higher for dry vegetation compared to wet vegetation. The results presented here underscore the importance of considering the effect of SCW on the retrieval of biophysical variables in agricultural monitoring. In particular, they highlight the possible influence of overpass time on the interpretation of radar data for vegetation monitoring. The estimated VOD from vegetation with SCW were generally higher than those estimated from vegetation without SCW. Therefore the surface canopy water considerably affected the regression coefficient values (b-factor) of the linear relationship between VOD and VWC from dry and wet vegetation. This finding proposed that considering similar b-factor for the dry and the wet vegetation will introduce error in soil moisture retrieval and highlight the importance of considering canopy wetness condition when using tau omega model to estimate VOD from VWC.