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首页> 外文期刊>Remote Sensing >Assessing the Performance of MODIS NDVI and EVI for Seasonal Crop Yield Forecasting at the Ecodistrict Scale
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Assessing the Performance of MODIS NDVI and EVI for Seasonal Crop Yield Forecasting at the Ecodistrict Scale

机译：在生态区范围内评估MODIS NDVI和EVI在季节性作物产量预报中的性能

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Crop yield forecasting plays a vital role in coping with the challenges of the impacts of climate change on agriculture. Improvements in the timeliness and accuracy of yield forecasting by incorporating near real-time remote sensing data and the use of sophisticated statistical methods can improve our capacity to respond effectively to these challenges. The objectives of this study were (i) to investigate the use of derived vegetation indices for the yield forecasting of spring wheat (Triticum aestivum L.) from the Moderate resolution Imaging Spectroradiometer (MODIS) at the ecodistrict scale across Western Canada with the Integrated Canadian Crop Yield Forecaster (ICCYF); and (ii) to compare the ICCYF-model based forecasts and their accuracy across two spatial scales-the ecodistrict and Census Agricultural Region (CAR), namely in CAR with previously reported ICCYF weak performance. Ecodistricts are areas with distinct climate, soil, landscape and ecological aspects, whereas CARs are census-based/statistically-delineated areas. Agroclimate variables combined respectively with MODIS-NDVI and MODIS-EVI indices were used as inputs for the in-season yield forecasting of spring wheat during the 2000–2010 period. Regression models were built based on a procedure of a leave-one-year-out. The results showed that both agroclimate + MODIS-NDVI and agroclimate + MODIS-EVI performed equally well predicting spring wheat yield at the ECD scale. The mean absolute error percentages (MAPE) of the models selected from both the two data sets ranged from 2% to 33% over the study period. The model efficiency index (MEI) varied between −1.1 and 0.99 and −1.8 and 0.99, respectively for the agroclimate + MODIS-NDVI and agroclimate + MODIS-EVI data sets. Moreover, significant improvement in forecasting skill (with decreasing MAPE of 40% and 5 times increasing MEI, on average) was obtained at the finer, ecodistrict spatial scale, compared to the coarser CAR scale. Forecast models need to consider the distribution of extreme values of predictor variables to improve the selection of remote sensing indices. Our findings indicate that statistical-based forecasting error could be significantly reduced by making use of MODIS-EVI and NDVI indices at different times in the crop growing season and within different sub-regions.
机译：作物单产预报在应对气候变化对农业的挑战方面起着至关重要的作用。通过合并近实时遥感数据并使用复杂的统计方法来提高产量预测的及时性和准确性，可以提高我们有效应对这些挑战的能力。这项研究的目的是（i）利用中度分辨率成像分光光度计（MODIS）在加拿大西部的生态区范围内利用加拿大综合信息系统研究导出的植被指数在春季小麦（Triticum aestivum L.）产量预报中的应用作物产量预报员（ICCYF）; （ii）比较基于ICCYF模型的预测及其在两个空间尺度（生态区和人口普查农业区（CAR））中的准确性，即在先前报告的ICCYF性能较弱的CAR中。生态区是气候，土壤，景观和生态方面都不同的地区，而中非共和国是基于人口普查/统计划分的地区。在2000-2010年期间，分别将农业气候变量与MODIS-NDVI和MODIS-EVI指数结合起来，用作春季小麦季节单产预报的输入。回归模型是基于“请假一年”的程序构建的。结果表明，在ECD规模上，农气候+ MODIS-NDVI和农气候+ MODIS-EVI均能很好地预测春小麦的产量。在研究期间，从两个数据集中选择的模型的平均绝对误差百分比（MAPE）为2％至33％。对于农业气候+ MODIS-NDVI和农业气候+ MODIS-EVI数据集，模型效率指数（MEI）分别在-1.1和0.99之间以及-1.8和0.99之间变化。此外，与较粗糙的CAR尺度相比，在更精细的生态区空间尺度上，可以显着提高预测技巧（平均MAPE降低40％，MEI平均增加5倍）。预测模型需要考虑预测变量的极值分布，以改善遥感指数的选择。我们的发现表明，通过在作物生长期的不同时间和不同分区内使用MODIS-EVI和NDVI指数，可以显着减少基于统计的预测误差。

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