光学和微波卫星数据的协同集成用于农作物估产
Synergistic Integration of Optical and Microwave Satellite Data for Crop Yield Estimation
摘要：
鉴于对粮食的需求日益增加，开发作物压力、物候和生产力的准确模型至关重要。地球观测遥感数据为以时间分辨和空间明确的方式监测作物提供了独特的信息来源。在这项研究中，我们提出了结合多传感器（光学和微波）遥感数据的作物产量估计和预测，使用两种新的方法。我们首先提出了增强植被指数来自MODIS和植被光学厚度来自SMAP作为一个新的联合度量相结合的信息从两个卫星传感器在一个独特的功能或描述符之间的滞后。我们的第二种方法避免了汇总统计数据，并使用机器学习来结合EVI和VOD的完整时间序列。本研究考虑了两种统计方法，正则化线性回归和其非线性扩展称为核岭回归直接估计县级调查总产量，以及该地区种植的主要作物的个体产量：玉米，大豆和小麦。研究区域包括美国玉米带，我们使用国家农业统计局（USDA-NASS）2015年的农业调查数据进行定量评估。
英文摘要：
Developing accurate models of crop stress, phenology and productivity is of paramount importance, given the increasing need of food. Earth observation remote sensing data provides a unique source of information to monitor crops in a temporally resolved and spatially explicit way. In this study, we propose the combination of multisensor (optical and microwave) remote sensing data for crop yield estimation and forecasting using two novel approaches. We first propose the lag between Enhanced Vegetation Index derived from MODIS and Vegetation Optical Depth derived from SMAP as a new joint metric combining the information from the two satellite sensors in a unique feature or descriptor. Our second approach avoids summarizing statistics and uses machine learning to combine full time series of EVI and VOD. This study considers two statistical methods, a regularized linear regression and its nonlinear extension called kernel ridge regression to directly estimate the county-level surveyed total production, as well as individual yields of the major crops grown in the region: corn, soybean and wheat. The study area includes the US Corn Belt, and we use agricultural survey data from the National Agricultural Statistics Service (USDA-NASS) for year 2015 for quantitative assessment.
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