Assessing the Variability of Corn and Soybean Yields in Central Iowa Using High Spatiotemporal Resolution Multi-Satellite Imagery
Assessing the Variability of Corn and Soybean Yields in Central Iowa Using High Spatiotemporal Resolution Multi-Satellite Imagery
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DOI：
10.3390/rs10091489
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发表时间：
2018-09-01
期刊：
影响因子：
5
通讯作者：
Johnson, David
中科院分区：
文献类型：
--
作者：
Gao, Feng;Anderson, Martha;Johnson, David
遥感数据在作物产量建模中的效用通常使用粗分辨率（>250 m）数据在区域或州一级进行评估。由于这些传感器的低时间采样频率特性，使用中等分辨率数据（10-100 m）进行田间产量估算受到限制。然而，当组合使用多个遥感数据源时，中等分辨率的时间采样可以得到显着改善。此外，已经开发了数据融合方法来混合来自不同空间和时间分辨率的数据。本文研究了多源数据集提供的改进时间采样对我们解释爱荷华州中部（美国玉米带的一部分）作物产量时空变化的能力的影响。使用 2001 年至 2015 年的 Landsat-MODIS 融合数据以及 2016 年和 2017 年的 Landsat-Sentinel2-MODIS 融合数据评估了源自植被指数 (VI) 时间序列的几个指标。融合数据比单独使用单个数据源具有更高的决定系数 (R-2 ) 和更小的相对平均绝对误差，更好地解释了产量变异性。本研究区玉米和大豆产量预测的最佳时期为生长季中期192～236天（7月初至8月下旬，收获前1～3个月）。这些发现强调了高时空分辨率遥感数据在农业应用中的重要性。
The utility of remote sensing data in crop yield modeling has typically been evaluated at the regional or state level using coarse resolution (>250 m) data. The use of medium resolution data (10-100 m) for yield estimation at field scales has been limited due to the low temporal sampling frequency characteristics of these sensors. Temporal sampling at a medium resolution can be significantly improved, however, when multiple remote sensing data sources are used in combination. Furthermore, data fusion approaches have been developed to blend data from different spatial and temporal resolutions. This paper investigates the impacts of improved temporal sampling afforded by multi-source datasets on our ability to explain spatial and temporal variability in crop yields in central Iowa (part of the U.S. Corn Belt). Several metrics derived from vegetation index (VI) time-series were evaluated using Landsat-MODIS fused data from 2001 to 2015 and Landsat-Sentinel2-MODIS fused data from 2016 and 2017. The fused data explained the yield variability better, with a higher coefficient of determination (R-2 ) and a smaller relative mean absolute error than using a single data source alone. In this study area, the best period for the yield prediction for corn and soybean was during the middle of the growing season from day 192 to 236 (early July to late August, 1-3 months before harvest). These findings emphasize the importance of high temporal and spatial resolution remote sensing data in agricultural applications.