Assessing the benefit of satellite-based Solar-Induced Chlorophyll Fluorescence in crop yield prediction
Assessing the benefit of satellite-based Solar-Induced Chlorophyll Fluorescence in crop yield prediction
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DOI：
10.1016/j.jag.2020.102126
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发表时间：
2020-08-01
影响因子：
7.5
通讯作者：
Kohler, Philipp
中科院分区：
文献类型：
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作者：
Peng, Bin;Guan, Kaiyu;Kohler, Philipp
大规模农作物产量预测对于粮食安全预警、农业供应链管理和经济市场化具有重要意义。基于卫星的太阳诱导叶绿素荧光(SIF)产品揭示了全球农田的光合作用热点，例如在美国中西部。然而，在与其他现有卫星数据进行基准比较的情况下，这些基于卫星的SIF产品在多大程度上可以提高作物产量预测的性能。在这里，我们评估了使用三种基于卫星的SIF产品预测美国中西部玉米和大豆产量的好处：来自轨道碳观测站2(OCO-2)的填补缺口的SIF，从对流层监测仪(Tropomi)新的SIF反演，以及从全球臭氧监测实验2(GOME-2)的粗分辨率SIF反演。将SIF数据与基于卫星的植被指数(VIS)的产量预测性能进行了比较，包括归一化植被指数(NDVI)、增强植被指数(EVI)、植被近红外反射率(NIRv)和地表温度(LST)。五种机器学习算法被用来建立既有遥感变量又有气候遥感变量的产量预测模型。我们发现，来自OCO-2和Tropomi的高分辨率SIF产品在作物产量预测方面优于高分辨率GOME-2 SIF产品。利用高分辨率SIF产品对2018年玉米和大豆产量进行了最好的正演预测，表明利用卫星高分辨率SIF产品进行作物产量预测的潜力很大。然而，在所有评价的情况下，使用目前可用的高分辨率SIF产品并不能保证始终比使用其他基于卫星的遥感变量具有更好的产量预测性能。不同遥感变量在产量预测中的相对表现取决于作物类型(玉米或大豆)、样本外测试方法(五次交叉验证或正向验证)和训练数据的记录长度。我们还发现，使用NIRV通常可以获得比使用NDVI、EVI或LST更好的产量预测性能，使用NIRV可以获得与使用OCO-2或Tropomi SIF产品相似甚至更好的产量预测性能。我们认为，随着未来积累更多的高分辨率和高质量的SIF产品，卫星SIF产品将有利于作物产量的预测。
Large-scale crop yield prediction is critical for early warning of food insecurity, agricultural supply chain management, and economic market. Satellite-based Solar-Induced Chlorophyll Fluorescence (SIF) products have revealed hot spots of photosynthesis over global croplands, such as in the U.S. Midwest. However, to what extent these satellite-based SIF products can enhance the performance of crop yield prediction when benchmarking against other existing satellite data remains unclear. Here we assessed the benefits of using three satellite-based SIF products in yield prediction for maize and soybean in the U.S. Midwest: gap-filled SIF from Orbiting Carbon Observatory 2 OCO-2), new SIF retrievals from the TROPOspheric Monitoring Instrument (TROPOMI), and the coarse-resolution SIF retrievals from the Global Ozone Monitoring Experiment-2 (GOME-2). The yield prediction performances of using SIF data were benchmarked with those using satellite-based vegetation indices (VIs), including normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), and near-infrared reflectance of vegetation (NIRv), and land surface temperature (LST). Five machine-learning algorithms were used to build yield prediction models with both remote-sensing-only and climate-remote-sensing-combined variables. We found that high-resolution SIF products from OCO-2 and TROPOMI outperformed coarse-resolution GOME-2 SIF product in crop yield prediction. Using high-resolution SIF products gave the best forward predictions for both maize and soybean yields in 2018, indicating the great potential of using satellite-based high-resolution SIF products for crop yield prediction. However, using currently available high-resolution SIF products did not guarantee consistently better yield prediction performances than using other satellite-based remote sensing variables in all the evaluated cases. The relative performances of using different remote sensing variables in yield prediction depended on crop types (maize or soybean), out-of-sample testing methods (fivefold-cross-validation or forward), and record length of training data. We also found that using NIRv could generally lead to better yield prediction performance than using NDVI, EVI, or LST, and using NIRv could achieve similar or even better yield prediction performance than using OCO-2 or TROPOMI SIF products. We concluded that satellite-based SIF products could be beneficial in crop yield prediction with more high-resolution and good-quality SIF products accumulated in the future.