Comparison between Dense L-Band and C-Band Synthetic Aperture Radar (SAR) Time Series for Crop Area Mapping over a NISAR Calibration-Validation Site

Simon Kraatz,Heather Mcnairn,Andrew Davidson,Michael H Cosh,Xiaodong Huang,Paul Siqueira,Xianfeng Jiao,Nathan Torbick,Laura Dingle Robertson

doi:10.3390/agronomy11020273

Abstract

Crop area mapping is important for tracking agricultural production and supporting food security. Spaceborne approaches using synthetic aperture radar (SAR) now allow for mapping crop area at moderate spatial and temporal resolutions. Multi-frequency SAR data is highly useful for crop monitoring because backscatter response from vegetation canopies is wavelength dependent. This study evaluates the utility of C-band Sentinel-1B (Sentinel-1) and L-band ALOS-2 (PALSAR) data, collected during the 2019 growing season, for generating accurate active crop extent (crop vs. non-crop) classifications over an agricultural region in western Canada. Evaluations were performed against the Agriculture and Agri-Food Canada satellite-based Annual Cropland Inventory (ACI), an open data product that maps land cover across the extent of Canada’s agricultural land. Classifications were performed using the temporal coefficient of variation (CV) approach, where an optimal crop/non-crop delineating CV threshold (CVthr) is selected according to Youden’s J-statistic. Results show that crop area mapping agreed better with the ACI when using Sentinel-1 data (83.5%) compared to PALSAR (73.2%). Analysis of performance by crop reveals that PALSAR’s poorer performance can be attributed to soybean, urban, grassland, and pasture ACI classes. This study also compared CV values to in situ wet biomass data for canola and soybeans, showing that crops with lower biomass (soybean) had correspondingly lower CV values.

Highlights

Global-scale crop area mapping is important for tracking agricultural production and addressing issues relating to food security [1,2]
Over the scene,biomass the Sentinel-1 and PALSAR-2 data have observed earlier in the andentire for lower canopies compared to PALSAR-2 mean
This study presents first results of the temporal coefficient of variation (CV) approach, an algorithm to be used for NISAR’s Cropland Area product, over a NISAR calibration-validation site located near Carman in Manitoba, Canada

Summary

Introduction

Global-scale crop area mapping is important for tracking agricultural production and addressing issues relating to food security [1,2]. A majority of agricultural fields are just over two hectares in size, making moderate resolution platforms such as MODIS (250 m) unsuitable for mapping these smaller fields [3] Optical sensors such as Landsat and Sentinel-2 have a less frequent revisit than MODIS, and as such cloud cover can create large temporal gaps in the data record. The increase in the availability of SAR data from Sentinel-1A and 1B has created new opportunities for integrating moderate resolution SAR into operational crop mapping These data are needed in cloud-prone regions, as well as during critical growth stages, to mitigate image gaps and establish robust monitoring programs [8,9,10]

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