Tiwari Varun, Thorp Kelly, Tulbure Mirela G, Gray Joshua, Kamruzzaman Mohammad, Krupnik Timothy J, Sankarasubramanian A, Ardon Marcelo
Center for Geospatial Analytics, North Carolina State University (NCSU), Raleigh, NC, United States of America.
United States Department of Agriculture (USDA), Agricultural Research Service (ARS), Grassland Soil and Water Research Laboratory, Temple, Texas, United States of America.
PLoS One. 2024 Dec 12;19(12):e0309982. doi: 10.1371/journal.pone.0309982. eCollection 2024.
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.
及时、准确地估算水稻产量对于支持孟加拉国等水稻生产国的粮食安全管理、农业政策制定和气候变化适应至关重要。为满足这一需求，本研究引入了一种工作流程，以便在分区尺度（1000米空间分辨率）上及时、精确地估算水稻产量。然而，在利用遥感方法进行高空间分辨率的政府报告粮食安全管理水稻产量估算方面，存在显著差距。目前的方法仅限于特定区域，主要用于研究，缺乏与国家报告系统的整合。此外，在分区尺度上没有一致的年度冬稻产量图，这阻碍了本地化农业决策。该工作流程利用中分辨率成像光谱仪（MODIS）和年度县级产量数据，训练了一个随机森林模型，用于估算2002年至2021年1000米分辨率的冬稻产量。结果显示，分别使用报告的县级产量和作物实测产量数据进行验证时，平均百分比均方根误差（RMSE）为8.07%和12.96%。此外，孟加拉国冬稻的估计产量各不相同，每公顷的不确定性范围在0.40至0.45吨之间。此外，使用修正的曼-肯德尔趋势检验（95%置信区间，p<0.05）对2002年至2021年的冬稻估计产量数据进行了趋势分析。在孟加拉国，23%的水稻种植面积冬稻产量呈上升趋势，0.11%呈下降趋势，76.51%的面积水稻产量无趋势变化。鉴于这是孟加拉国二十年来首次尝试在1000米空间分辨率下估算冬稻产量，估计的季中冬稻产量在空间和时间上具有可扩展性，为加强孟加拉国的粮食安全管理提供了巨大潜力。此外，所提出的工作流程可轻松应用于估算世界其他地区的水稻产量。
