Alfalfa (Medicago sativa L.) crop vigor and yield characterization using high-resolution aerial multispectral and thermal infrared imaging technique
Alfalfa (Medicago sativa L.) crop vigor and yield characterization using high-resolution aerial multispectral and thermal infrared imaging technique
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DOI：
10.1016/j.compag.2021.105999
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发表时间：
2021-02-15
影响因子：
8.3
通讯作者：
Yu, Long-Xi
中科院分区：
文献类型：
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作者：
Chandel, Abhilash K.;Khot, Lav R.;Yu, Long-Xi
苜蓿（Medicago sativa L.）是一种重要的饲料作物，在世界范围内种植用于动物饲料、绿色肥料和作为土地覆盖物。然而，很少有方法存在及时的字段规模映射的作物状况，产量和质量属性的投入，收获和存储资源，预算管理，作物保险等。本研究的目的是应用高分辨率航空多光谱和热红外遥感（7厘米/像素）来表征上述作物属性。拍摄的是2018年季节的两个作物切割周期。八个作物活力指数（VI）和作物水分胁迫指数（CWSI）的功能来自收集的图像数据。修正非线性指数（MNLI）、修正简单比（MSR）和CWSI可靠地评价了作物活力和逆境性状的空间变异（变异系数[CV]在24-69%范围内）。然后通过9个简单线性回归（LR，变量：每个模型一个图像特征）、7个多元线性回归（MLR，变量：每个模型一个VI和CWSI）、逐步线性回归（SLR）、偏最小二乘回归（PLSR）和最小绝对收缩和选择算子（LASSO）模型，以指数作为预测变量预测产量。SLR、PLSR和LASSO最初使用所有图像特征进行模型训练。在简单模型中，MLR-4（变量：MNLI和CWSI）表现最好（均方根误差[RMSE] = 0.45 kg，R-2 = 0.64），LR-5（变量：MNLI）是第二好的模型（RMSE = 0.51 kg，R-2 = 0.54）。复杂的SLR、PLSR和LASSO模型预测产量的精度与MLR-4相似（RMSE在0.45-0.46 kg范围内，R-2在0.63-0.64范围内）。冠层活力（MNLI）和胁迫（CWSI）具有非饱和和非线性特征，可有效预测苜蓿作物生长状况和产量。总的来说，可见近红外和热红外领域的高分辨率航空遥感显示出对特定地点的作物监测的潜力。
Alfalfa (Medicago sativa L.) is an important forage crop grown worldwide for animal feed, green manure, and as a land cover. However, very few approaches exist for timely field scale mapping of crop status, yield and quality attributes for management of inputs, harvest and storage resources, budgeting, crop insurance, etc. This study aims to apply high-resolution aerial multispectral and thermal infrared remote sensing (7 cm/pixel) to characterize above crop attributers. Imaged were two crop cutting cycles in 2018 season. Eight crop vigor index (VI) and a Crop Water Stress Index (CWSI) features were derived from collected imagery data. Modified Non-Linear Index (MNLI), Modified Simple Ratio (MSR) and CWSI reliably evaluated the spatial variations in crop vigor and stress traits (Coefficient of variation [CV] in the ranges of 24-69%). Yield was then predicted with indices as predictor variables through nine simple linear regression (LRs, variable: one image feature per model), seven multiple linear regression (MLRs, variables: one VI and CWSI per model), a stepwise linear regression (SLR), a partial least square regression (PLSR) and a least absolute shrinkage and selection operator (LASSO) models. The SLR, PLSR and LASSO initially used all image features for model training. Amongst simple models, MLR-4 (Variables: MNLI and CWSI) performed the best (Root mean square error [RMSE] = 0.45 kg, R-2 = 0.64) and LR-5 (Variable: MNLI) was the second-best model (RMSE = 0.51 kg, R-2 = 0.54). The complex SLR, PLSR and LASSO models predicted yield with similar accuracy as MLR-4 (RMSE in the ranges of 0.45-0.46 kg, R-2 in the ranges of 0.63-0.64). MNLI (canopy vigor) and CWSI (stress) were significant and sufficient for effective alfalfa crop status and yield prediction for their non-saturation and non-linearity features. Overall, high-resolution aerial remote sensing in the visible-NIR and thermal infrared domain showed potential for site-specific crop monitoring.