Yield Forecasting of Spring Maize Using Remote Sensing and Crop Modeling in Faisalabad-Punjab Pakistan
Yield Forecasting of Spring Maize Using Remote Sensing and Crop Modeling in Faisalabad-Punjab Pakistan
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DOI：
10.1007/s12524-018-0825-8
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发表时间：
2018-10-01
影响因子：
2.5
通讯作者：
Judge, Jasmeet
中科院分区：
文献类型：
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作者：
Ahmad, Ishfaq;Saeed, Umer;Judge, Jasmeet
真实的估产对决策者具有重要的参考价值。本研究旨在利用遥感和作物模型对春玉米进行估产。在作物建模方面，利用田间试验数据对CERES-Maize模型进行了校正和评价，并将校正和评价后的模型用于玉米产量预测。还在费萨拉巴德对64个农场进行了实地调查，以收集有关初始田间条件和作物管理数据的数据。利用这些数据，在农户田间利用作物模型进行玉米产量预测。在遥感方面，利用机器学习算法对Landsat 8旺季影像进行土地覆盖分类。分类后，时间序列归一化差异植被指数（NDVI）和地表温度（LST）的调查64个农场计算。运用主成分分析法对各指标与玉米产量进行相关分析。所选择的LST和NDVI被用来开发产量预测方程，使用最小绝对收缩和选择算子（LASSO）回归。CERES-玉米的校准和评价结果显示，所有记录变量的平均绝对%误差（MAPE）为0.35-6.71%。在遥感中，所有的机器学习算法都表现出大于90%的准确率，而支持向量机（SVM径向基）表现出更高的准确率为97%，这是用于玉米面积分类。通过SVM径向基估计的面积的准确性为91%，验证与作物报告服务。作物模型的产量预测精度为255 kg ha（-1），遥感预测精度为397 kg ha（-1）。估计和实际谷物产量之间的关系的整体强度是良好的，在这两种技术的R-2为0.94。对于区域产量预测，遥感可以使用由于更大的优势，更少的输入数据集，如果重点是评估特定的压力，植物遗传学与土壤和环境条件的相互作用比作物模型是非常有用的工具。
Real time, accurate and reliable estimation of maize yield is valuable to policy makers in decision making. The current study was planned for yield estimation of spring maize using remote sensing and crop modeling. In crop modeling, the CERES-Maize model was calibrated and evaluated with the field experiment data and after calibration and evaluation, this model was used to forecast maize yield. A Field survey of 64 farm was also conducted in Faisalabad to collect data on initial field conditions and crop management data. These data were used to forecast maize yield using crop model at farmers' field. While in remote sensing, peak season Landsat 8 images were classified for landcover classification using machine learning algorithm. After classification, time series normalized difference vegetation index (NDVI) and land surface temperature (LST) of the surveyed 64 farms were calculated. Principle component analysis were run to correlate the indicators with maize yield. The selected LSTs and NDVIs were used to develop yield forecasting equations using least absolute shrinkage and selection operator (LASSO) regression. Calibrated and evaluated results of CERES-Maize showed the mean absolute % error (MAPE) of 0.35-6.71% for all recorded variables. In remote sensing all machine learning algorithms showed the accuracy greater the 90%, however support vector machine (SVM-radial basis) showed the higher accuracy of 97%, that was used for classification of maize area. The accuracy of area estimated through SVM-radial basis was 91%, when validated with crop reporting service. Yield forecasting results of crop model were precise with RMSE of 255 kg ha(-1), while remote sensing showed the RMSE of 397 kg ha(-1). Overall strength of relationship between estimated and actual grain yields were good with R-2 of 0.94 in both techniques. For regional yield forecasting remote sensing could be used due greater advantages of less input dataset and if focus is to assess specific stress, and interaction of plant genetics to soil and environmental conditions than crop model is very useful tool.