Mid-season Crop Classification Using Dual-, Compact-, and Full-polarization in Preparation for the Radarsat Constellation Mission (RCM)

Salehi Salehi,Rezaee Rezaee,Homayouni Homayouni,Mohammadimanesh Mohammadimanesh,Davidson Davidson,Mahdianpari Mahdianpari,Mcnairn McNairn

doi:10.3390/rs11131582

Abstract

Despite recent research on the potential of dual- (DP) and full-polarimetry (FP) Synthetic Aperture Radar (SAR) data for crop mapping, the capability of compact polarimetry (CP) SAR data has not yet been thoroughly investigated. This is of particular concern, given the availability of such data from RADARSAT Constellation Mission (RCM) shortly. Previous studies have illustrated potential for accurate crop mapping using DP and FP SAR features, yet what contribution each feature makes to the model accuracy is not well investigated. Accordingly, this study examined the potential of the early- to mid-season (i.e., May to July) RADARSAT-2 SAR images for crop mapping in an agricultural region in Manitoba, Canada. Various classification scenarios were defined based on the extracted features from FP SAR data, as well as simulated DP and CP SAR data at two different noise floors. Both overall and individual class accuracies were compared for multi-temporal, multi-polarization SAR data using the pixel- and object-based random forest (RF) classification schemes. The late July C-band SAR observation was the most useful data for crop mapping, but the accuracy of single-date image classification was insufficient. Polarimetric decomposition features extracted from CP and FP SAR data produced relatively equal or slightly better classification accuracies compared to the SAR backscattering intensity features. The RF variable importance analysis revealed features that were sensitive to depolarization due to the volume scattering are the most important FP and CP SAR data. Synergistic use of all features resulted in a marginal improvement in overall classification accuracies, given that several extracted features were highly correlated. A reduction of highly correlated features based on integrating the Spearman correlation coefficient and the RF variable importance analyses boosted the accuracy of crop classification. In particular, overall accuracies of 88.23%, 82.12%, and 77.35% were achieved using the optimized features of FP, CP, and DP SAR data, respectively, using the object-based RF algorithm.

Highlights

Over the past 15 years, the Government of Canada—led by Agriculture and Agri-Food Canada, the Federal department responsible for Canada’s agriculture sector—has devoted considerable effort to better understanding how Earth Observation (EO) technologies can be used to operationally provide timely and repeatable observations of Canadian agriculture at a national scale
The capability of early- to mid-season (i.e., May to July) RADARSAT-2 Synthetic Aperture Radar (SAR) images were examined for crop mapping in an agricultural region in Manitoba, Canada
Various classification scenarios were defined based on extracted features from full polarimetry SAR data, as well as simulated dual and compact polarimetry SAR data

Summary

Introduction

The application of optical remote sensing is limited by its restriction to daytime monitoring through cloud-free skies [3]. This is of particular concern for the systematic operational monitoring of agricultural regions that experience frequent cloud cover, such as Canada’s east and west coasts. Synthetic Aperture Radar (SAR) sensors have the ability to penetrate clouds, smoke, haze and darkness, providing all-weather day-and-night imaging capability. This flexibility makes SAR a popular choice of national monitoring agencies for the operational monitoring of land, coastal and ocean environments

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