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%.

An Analysis of the Rice-Cultivation Dynamics in the Lower Utcubamba River Basin Using SAR and Optical Imagery in Google Earth Engine (GEE)

“…The highest precision results for both years were between 97.8% and 100%. This is similar to the findings of [50], who concluded that the combination of Sentinel-1 and Sentinel-2 data enables accurate early mapping of crops in the studied area, achieving an OA of up to 95.02%.…”

“…This combination produces more robust results compared to using a single data source, and it generally achieves higher accuracy. For example, a study by [50] classified a highly diverse agricultural region for 2020 and 2021, achieving accuracies of 95.2%. That research study concluded that integrating both satellite datasets enhanced overall accuracy by 2.94%.…”

Maize Crop Detection through Geo-Object-Oriented Analysis Using Orbital Multi-Sensors on the Google Earth Engine Platform

“…Yet, the lower accuracy relied on the "other land uses" class, and the higher corresponded to the second-harvest maize class, where UA for this class was 91.98%. [53][54][55]. From the classified data (Figures 6 and 7), the confusion matrices were generated with OA from 86.41% to 88.65% (Figures 8-11; Table 2).…”

Precise vegetation restoration in arid regions based on potential natural vegetation and potential normalized difference vegetation index

“…Five “COPERNICUS/S2_SR” images with complete coverage of the study area were acquired from 1 July 2020 to 30 September 2020. These imageries are atmospherically corrected and have a high geometric accuracy (Saad El Imanni et al 2022; Yilmaz et al 2023). These images were preprocessed in the GEE platform, including cloud removal, and the red (B4) and near‐infrared (B8) bands with a spatial resolution of 10 m were extracted to produce the NDVI in tiff format.…”