用遥感与深度学习预测印度小麦产区气候风险，提前识别粮食危机热点。
Anticipatory Understanding of Resilient Agriculture to Climate
- 融合遥感、深度学习与作物模型，构建粮食安全预警框架。
- 在法国数据上验证深度学习迁移效果，提升小麦农田识别精度。
- 结合系统动力学模型，模拟印度粮食分配并定位高风险区域。
全球数十亿人面临中度或重度粮食不安全，气候变化与地缘政治事件使粮食供应韧性愈发关键。本文提出一种综合遥感、深度学习、作物产量建模与食物分配系统因果建模的框架，用于更精准识别粮食安全热点。虽方法可推广至其他地区，但研究聚焦于供应全球大量人口的北印度小麦主产区。基于法国的精选遥感数据，量化评估了深度学习领域自适应方法在小麦农田识别中的表现；利用现有作物模型WOFOST模拟气候变化对产量的影响，并通过纵向惩罚函数回归识别作物模拟误差的关键驱动因素；同时构建印度粮食分配系统的系统动力学模型，结合预测产量结果，实现粮食不安全区域的识别。
原文摘要 · Abstract (English)
With billions of people facing moderate or severe food insecurity, the resilience of the global food supply will be of increasing concern due to the effects of climate change and geopolitical events. In this paper we describe a framework to better identify food security hotspots using a combination of remote sensing, deep learning, crop yield modeling, and causal modeling of the food distribution system. While we feel that the methods are adaptable to other regions of the world, we focus our analysis on the wheat breadbasket of northern India, which supplies a large percentage of the world's population. We present a quantitative analysis of deep learning domain adaptation methods for wheat farm identification based on curated remote sensing data from France. We model climate change impacts on crop yields using the existing crop yield modeling tool WOFOST and we identify key drivers of crop simulation error using a longitudinal penalized functional regression. A description of a system dynamics model of the food distribution system in India is also presented, along with results of food insecurity identification based on seeding this model with the predicted crop yields.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。