Rapid and Automated Approach for Early Crop Mapping Using Sentinel-1 and Sentinel-2 on Google Earth Engine; A Case of a Highly Heterogeneous and Fragmented Agricultural Region

Abstract

Accurate and rapid crop type mapping is critical for agricultural sustainability. The growing trend of cloud-based geospatial platforms provides rapid processing tools and cloud storage for remote sensing data. In particular, a variety of remote sensing applications have made use of publicly accessible data from the Sentinel missions of the European Space Agency (ESA). However, few studies have employed these data to evaluate the effectiveness of Sentinel-1, and Sentinel-2 spectral bands and Machine Learning (ML) techniques in challenging highly heterogeneous and fragmented agricultural landscapes using the Google Earth Engine (GEE) cloud computing platform. This work aims to map, accurately and early, the crop types in a highly heterogeneous and fragmented agricultural region of the Tadla Irrigated Perimeter (TIP) as a case study using the high spatiotemporal resolution of Sentinel-1, Sentinel-2, and a Random Forest (RF) classifier implemented on GEE. More specifically, five experiments were performed to assess the optical band reflectance values, vegetation indices, and SAR backscattering coefficients on the accuracy of crop classification. Besides, two scenarios were used to assess the monthly temporal windows on classification accuracy. The findings of this study show that the fusion of Sentinel-1 and Sentinel-2 data can accurately produce the early crop mapping of the studied area with an Overall Accuracy (OA) reaching 95.02%. The scenarios prove that the monthly time series perform better in terms of classification accuracy than single monthly windows images. Red-edge and shortwave infrared bands can improve the accuracy of crop classification by 1.72% when compared to only using traditional bands (i.e., visible and near-infrared bands). The inclusion of two common vegetation indices (The Normalized Vegetation Index (NDVI), the Enhanced Vegetation Index (EVI)) and Sentinel-1 backscattering coefficients to the crop classification enhanced the overall classification accuracy by 0.02% and 2.94%, respectively, compared to using the Sentinel-2 reflectance bands alone. The monthly windows analysis indicated that the improvement in the accuracy of crop classification is the greatest when the March images are accessible, with an OA higher than 80%.

Keywords

• crop type mapping
• Sentinel-1
• Sentinel-2
• machine learning
• Google Earth Engine
• time-series