Evaluating Sugarcane Yield Estimation in Thailand Using Multi-Temporal Sentinel-2 and Landsat Data Together with Machine-Learning Algorithms

Jaturong Som-ard (), Savittri Ratanopad Suwanlee, Dusadee Pinasu, Surasak Keawsomsee, Kemin Kasa, Nattawut Seesanhao, Sarawut Ninsawat, Enrico Borgogno-Mondino and Filippo Sarvia ()
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Jaturong Som-ard: Department of Geography, Faculty of Humanities and Social Sciences, Mahasarakham University, Maha Sarakham 44150, Thailand
Savittri Ratanopad Suwanlee: Department of Geography, Faculty of Humanities and Social Sciences, Mahasarakham University, Maha Sarakham 44150, Thailand
Dusadee Pinasu: Technology and Informatics Institute for Sustainability, National Metal and Materials Technology Center, National Science and Technology Development Agency, Thailand Science Park, Pathum Thani 12120, Thailand
Surasak Keawsomsee: Department of Geography, Faculty of Humanities and Social Sciences, Mahasarakham University, Maha Sarakham 44150, Thailand
Kemin Kasa: Department of Geography, Faculty of Humanities and Social Sciences, Mahasarakham University, Maha Sarakham 44150, Thailand
Nattawut Seesanhao: Department of Geography, Faculty of Humanities and Social Sciences, Mahasarakham University, Maha Sarakham 44150, Thailand
Sarawut Ninsawat: Remote Sensing and GIS, School of Engineering and Technology, Asian Institute of Technology, Klong Luang, Pathum Thani 12120, Thailand
Enrico Borgogno-Mondino: Department of Agricultural, Forest and Food Sciences, University of Turin, Grugliasco L.go, 10095 Braccini, Italy
Filippo Sarvia: Department of Agricultural, Forest and Food Sciences, University of Turin, Grugliasco L.go, 10095 Braccini, Italy

Land, 2024, vol. 13, issue 9, 1-19

Abstract: Updated and accurate crop yield maps play a key role in the agricultural environment. Their application enables the support for sustainable agricultural practices and the formulation of effective strategies to mitigate the impacts of climate change. Farmers can apply the maps to gain an overview of the yield variability, improving farm management practices and optimizing inputs to increase productivity and sustainability such as fertilizers. Earth observation (EO) data make it possible to map crop yield estimations over large areas, although this will remain challenging for specific crops such as sugarcane. Yield data collection is an expensive and time-consuming practice that often limits the number of samples collected. In this study, the sugarcane yield estimation based on a small number of training datasets within smallholder crop systems in the Tha Khan Tho District, Thailand for the year 2022 was assessed. Specifically, multi-temporal satellite datasets from multiple sensors, including Sentinel-2 and Landsat 8/9, were involved. Moreover, in order to generate the sugarcane yield estimation maps, only 75 sampling plots were selected and surveyed to provide training and validation data for several powerful machine-learning algorithms, including multiple linear regression (MLR), stepwise multiple regression (SMR), partial least squares regression (PLS), random forest regression (RFR), and support vector regression (SVR). Among these algorithms, the RFR model demonstrated outstanding performance, yielding an excellent result compared to existing techniques, achieving an R-squared (R 2 ) value of 0.79 and a root mean square error (RMSE) of 3.93 t/ha (per 10 m × 10 m pixel). Furthermore, the mapped yields across the region closely aligned with the official statistical data from the Office of the Cane and Sugar Board (with a range value of 36,000 ton). Finally, the sugarcane yield estimation model was applied to over 2100 sugarcane fields in order to provide an overview of the current state of the yield and total production in the area. In this work, the different yield rates at the field level were highlighted, providing a powerful workflow for mapping sugarcane yields across large regions, supporting sugarcane crop management and facilitating decision-making processes.

Keywords: sugarcane; yield estimation; earth observation; Sentinel-2; Landsat; machine learning (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2024
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