Combining ASNARO-2 XSAR HH and Sentinel-1 C-SAR VH/VV Polarization Data for Improved Crop Mapping

Rei Sonobe

doi:10.3390/rs11161920

Abstract

The Advanced Satellite with New system ARchitecture for Observation-2 (ASNARO-2), which carries the X-band Synthetic Aperture Radar (XSAR), was launched on 17 January 2018 and is expected to be used to supplement data provided by larger satellites. Land cover classification is one of the most common applications of remote sensing, and the results provide a reliable resource for agricultural field management and estimating potential harvests. This paper describes the results of the first experiments in which ASNARO-2 XSAR data were applied for agricultural crop classification. In previous studies, Sentinel-1 C-SAR data have been widely utilized to identify crop types. Comparisons between ASNARO-2 XSAR and Sentinel-1 C-SAR using data obtained in June and August 2018 were conducted to identify five crop types (beans, beetroot, maize, potato, and winter wheat), and the combination of these data was also tested. To assess the potential for accurate crop classification, some radar vegetation indices were calculated from the backscattering coefficients for two dates. In addition, the potential of each type of SAR data was evaluated using four popular supervised learning models: Support vector machine (SVM), random forest (RF), multilayer feedforward neural network (FNN), and kernel-based extreme learning machine (KELM). The combination of ASNARO-2 XSAR and Sentinel-1 C-SAR data was effective, and overall classification accuracies of 85.4 ± 1.8% were achieved using SVM.

Highlights

Agricultural practices determine the level of food production, and increases in agricultural output are essential for global political and social stability and equity [1]
Cropland mapping is necessary for estimating the amount and type of crops harvested and supporting the management of agricultural fields
Landsat series data have proven effective for identifying crop types with a high level of accuracy [7,8], and red-edge and shortwave infrared reflectance data are useful for improving crop monitoring over large areas [8,9,10]

Summary

Introduction

Agricultural practices determine the level of food production, and increases in agricultural output are essential for global political and social stability and equity [1]. Cropland mapping is necessary for estimating the amount and type of crops harvested and supporting the management of agricultural fields. A system of individual income support for farmers has been adopted in Japan, and some local governments use manual surveys to document field properties such as crop type and location [3]. More efficient methods of cropland mapping have become necessary to reduce costs, and as a result the application of remote sensing techniques based on satellite data has received considerable attention. A number of studies have shown that optical remote sensing data can be used to produce maps with high spatial and spectral resolutions [4] and are effective for gathering various types of biomass information, such as leaf chlorophyll content [5] and leaf area index (LAI) [6]. The quality of optical remote sensing data depends on atmospheric influences and weather conditions

Methods

Results

Conclusion