Advancing food security: Rice yield estimation framework using time-series satellite data & machine learning

• Published in: PloS one 2024-12, Vol.19 (12), p.e0309982
• Main Authors: Tiwari, Varun, Thorp, Kelly, Tulbure, Mirela G, Gray, Joshua, Kamruzzaman, Mohammad, Krupnik, Timothy J, Sankarasubramanian, A, Ardon, Marcelo
• Format: Article
• Language: English
• Subjects: Agricultural policy; Agricultural production; Agricultural research; Agriculture; Agriculture - methods; Bangladesh; Biology and Life Sciences; Climate adaptation; Climate Change; Climatic changes; Computer and Information Sciences; Confidence intervals; Crop yield; Crop yields; Crops, Agricultural - growth & development; Datasets; Decision making; Decision trees; Deep learning; Earth Sciences; Estimation; Food Security; Food supply; GDP; Gross Domestic Product; Machine Learning; Medicine and Health Sciences; MODIS; Oryza - growth & development; People and Places; Physiological aspects; Rain; Remote sensing; Research and Analysis Methods; Rice; Rice yield; Root-mean-square errors; Satellite data; Satellite Imagery - methods; Satellite imaging; Security management; Spatial discrimination; Spatial resolution; Testing; Trend analysis; Trends; Vegetation; Workflow
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0309982

Timely and accurately estimating rice yields is crucial for supporting food security management, agricultural policy development, and climate change adaptation in rice-producing countries such as Bangladesh. To address this need, this study introduced a workflow to enable timely and precise rice yield estimation at a sub-district scale (1,000-meter spatial resolution). However, a significant gap exists in the application of remote sensing methods for government-reported rice yield estimation for food security management at high spatial resolution. Current methods are limited to specific regions and primarily used for research, lacking integration into national reporting systems. Additionally, there is no consistent yearly boro rice yield map at a sub-district scale, hindering localized agricultural decision-making. This workflow leveraged MODIS and annual district-level yield data to train a random forest model for estimating boro rice yields at a 1,000-meter resolution from 2002 to 2021. The results revealed a mean percentage root mean square error (RMSE) of 8.07% and 12.96% when validation was conducted using reported district yields and crop-cut yield data, respectively. Additionally, the estimated yield of boro rice varies with an uncertainty range between 0.40 and 0.45 tons per hectare across Bangladesh. Furthermore, a trend analysis was performed on the estimated boro rice yield data from 2002 to 2021 using the modified Mann-Kendall trend test with a 95% confidence interval (p < 0.05). In Bangladesh, 23% of the rice area exhibits an increasing trend in boro rice yield, 0.11% shows a decreasing trend, and 76.51% of the area demonstrates no trend in rice yield. Given that this is the first attempt to estimate boro rice yield at 1,000-meter spatial resolution over two decades in Bangladesh, the estimated mid-season boro rice yield estimates are scalable across space and time, offering significant potential for strengthening food security management in Bangladesh. Furthermore, the proposed workflow can be easily applied to estimate rice yields in other regions worldwide.