Integration of Optical and Synthetic Aperture Radar Imagery for Improving Crop Mapping in Northwestern Benin, West Africa

Gerald Forkuor,Tobias Ullmann,Michael Thiel,Evence Zoungrana,Christopher Conrad

doi:10.3390/rs6076472

Abstract

Crop mapping in West Africa is challenging, due to the unavailability of adequate satellite images (as a result of excessive cloud cover), small agricultural fields and a heterogeneous landscape. To address this challenge, we integrated high spatial resolution multi-temporal optical (RapidEye) and dual polarized (VV/VH) SAR (TerraSAR-X) data to map crops and crop groups in northwestern Benin using the random forest classification algorithm. The overall goal was to ascertain the contribution of the SAR data to crop mapping in the region. A per-pixel classification result was overlaid with vector field boundaries derived from image segmentation, and a crop type was determined for each field based on the modal class within the field. A per-field accuracy assessment was conducted by comparing the final classification result with reference data derived from a field campaign. Results indicate that the integration of RapidEye and TerraSAR-X data improved classification accuracy by 10%–15% over the use of RapidEye only. The VV polarization was found to better discriminate crop types than the VH polarization. The research has shown that if optical and SAR data are available for the whole cropping season, classification accuracies of up to 75% are achievable.

Highlights

IntroductionAgricultural land use has experienced high expansion rates in many parts of the world [1]
In recent years, agricultural land use has experienced high expansion rates in many parts of the world [1]
Owing to the classification approach adopted, it was possible to identify the contribution of radar in improving classification accuracies

Summary

Introduction

Agricultural land use has experienced high expansion rates in many parts of the world [1]. Accurate and up-to-date information on agricultural land use is essential to appropriately monitor these changes and assess their impacts on water and soil quality, biodiversity and other environmental factors at various scales [2,3,4]. This is important considering the looming effects of climate change and variability. A wide range of biophysical and economic models can benefit from this information and improve decision-making based on their results

Objectives

Results

Conclusion