Mapping multiple crops of multiple seasons: a 30 m national crop map of Paraguay derived from the Harmonized Landsat and Sentinel-2 (HLS) data and stratified random sampling

Crop maps are essential data for a wide range of applications such as crop growth monitoring, crop production forecasting and natural disaster assessments. However, many countries lack the capacity to produce annual crop updates. The Harmonized Landsat and Sentinel 2 (HLS) product, with its 30-m spatial resolution and 2–3-day revisit frequency, offers an unprecedented opportunity for agricultural remote sensing. We implemented an end-to-end operational workflow for estimating crop area and mapping crop type over the country of Paraguay, where multiple cropping seasons with diverse crop varieties exist during a calendar year. We developed an automated satellite data processing system on High-Performance Clusters (HPC) to convert satellite images into grided Analysis Ready Data (ARD) products, which were subsequently used as input for machine learning-based crop mapping. Based on the field sample, we estimated the first-season soybean area in Paraguay over 2022/2023 to be 2.96 million hectares (Mha) with a standard error (S.E.) of 0.20 Mha, the second-season soybean area to be 0.57 ± 0.13 Mha, the maize area to be 1.26 ± 0.10 Mha and the rice area to be 0.17 ± 0.04 Mha S.E. We produced two 30 m resolution crop classification maps for the two cropping seasons that matched the sample-based area estimates. The overall accuracies of the two maps were both 98% with high users’ and producer’s accuracies. Our results demonstrate that HLS data in conjunction with stratified random sampling are adequate to map multiple crops of multiple seasons on the national scale. This cost-effective method shows substantial potential for operational implementation by government agencies.

Song, X.P., Zalles, V., Adusei, B., Pickering, J., Hernandez-Serna, A., Li, H., Torales, E., Miranda, L., Stehman, S.V., Caballero, P. and Hansen, M. (2024). Mapping multiple crops of multiple seasons: a 30 m national crop map of Paraguay derived from the Harmonized Landsat and Sentinel-2 (HLS) data and stratified random sampling. AGU Fall Meeting, Abstract B11G-1389, December 9-13, Washington, D.C., USA.