An assessment of pre- and within-season remotely sensed variables for forecasting corn and soybean yields in the United States
An assessment of pre- and within-season remotely sensed variables for forecasting corn and soybean yields in the United States
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DOI：
10.1016/j.rse.2013.10.027
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发表时间：
2014-02-05
影响因子：
13.5
通讯作者：
Johnson, David M.
中科院分区：
文献类型：
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作者：
Johnson, David M.
对四个及时和广泛可用的遥感数据集进行了评估，以纳入以美国中部玉米带地区为重点的县级玉米和大豆产量预测工作。这些数据集是：（1）从Terra卫星的中分辨率成像光谱仪（MODIS）获得的归一化植被指数（NDVI），（2）从Aqua卫星的MODIS获得的白天和（3）夜间地表温度（LST），以及（4）从国家气象局（NWS）基于Nexrad的网格数据产品获得的降水量。所使用的原始MODIS数据是全球生产的8天晴空合成科学产品（MOD 09 Q1和MYD 11 A2），而美国范围内的NWS数据通过重新网格化和汇总其他每日测量值，在空间和时间上与MODIS图像相吻合。分析了2006-2011年的作物生长季节，每年从2月中旬到10月下旬，以32个8天的周期为界。国家农业统计局（NASS）每年制作的土地覆盖分类（称为耕地数据层）用于隔离相应年份玉米和大豆的输入数据集像素。然后，按作物和时间段对相关像素进行平均，以产生对NDVI、LST和降水量的县级估计。它们反过来又与官方年度NASS县级产量统计数据有关。对于玉米带地区作为一个整体，玉米和大豆产量被发现是正相关的植被指数在夏季中期和白天LST在同一时间呈负相关。夜间LST和降水量没有表现出相关性的产量，无论时间之前或在生长季节。也有轻微的建议，在春季的低NDVI和高白天LST正相关的最终产量，再次为两种作物。仅以2006-2011年数据集的NDVI和白天LST作为输入，建立了基于回归树的模型，发现两种作物的县级样本内决定系数（R-2）为0.93。通过系统地删除季节后期数据来限制模型，表明模型性能即使在季节中期也保持强劲，甚至在更早的时候仍然可行。最后，导出的模型被用来预测2012年季节的样本外，最终出现了异常干旱。然而，县级结果与官方统计数据相比相当不错，玉米的R-2 = 0.77，大豆的R-2 = 0.71。均方根误差分别为每公顷1.26和0.42公吨。(C)2013 Elsevier Inc. All rights reserved.
Four timely and broadly available remotely sensed datasets were assessed for inclusion into county-level corn and soybean yield forecasting efforts focused on the Corn Belt region of the central United States (US). Those datasets were the (1) Normalized Difference Vegetation Index (NDVI) as derived from the Terra satellite's Moderate Resolution Imaging Spectroradiometer (MODIS), (2) daytime and (3) nighttime land surface temperature (LST) as derived from Aqua satellite's MODIS, and (4) precipitation from the National Weather Service (NWS) Nexrad-based gridded data product. The originating MODIS data utilized were the globally produced 8-day, clear sky composited science products (MOD09Q1 and MYD11A2), while the US-wide NWS data were manipulated to mesh with the MODIS imagery both spatially and temporally by regridding and summing the otherwise daily measurements. The crop growing seasons of 2006-2011 were analyzed with each year bounded by 32 8-day periods from mid-February through late October. Land cover classifications known as the Cropland Data Layer as produced annually by the National Agricultural Statistics Service (NASS) were used to isolate the input dataset pixels as to corn and soybeans for each of the corresponding years. The relevant pixels were then averaged by crop and time period to produce a county-level estimate of NDVI, the LSTs, and precipitation. They in turn were related to official annual NASS county level yield statistics. For the Corn Belt region as a whole, both corn and soybean yields were found to be positively correlated with NDVI in the middle of the summer and negatively correlated to daytime LST at that same time. Nighttime LST and precipitation showed no correlations to yield, regardless of the time prior or during the growing season. There was also slight suggestion of low NDVI and high daytime LST in the spring being positively related to final yields, again for both crops. Taking only NDVI and daytime LST as inputs from the 2006-2011 dataset, regression tree-based models were built and county-level, within-sample coefficients of determination (R-2) of 0.93 were found for both crops. Limiting the models by systematically removing late season data showed the model performance to remain strong even at mid-season and still viable even earlier. Finally, the derived models were used to predict out-of-sample for the 2012 season, which ended up having an anomalous drought Yet, the county-level results compared reasonably well against official statistics with R-2 = 0.77 for corn and 0.71 for soybeans. The root-mean-square errors were 1.26 and 0.42 metric tons per hectare, respectively. (C) 2013 Elsevier Inc. All rights reserved.