ABSTRACT The main goal of this research was to estimate heavy metals (HMs) (molybdenum (Mo), copper (Cu), nickel (Ni), cadmium (Cd)) contents in crop leaves through multispectral satellite imagery. During the acquisition of a SPOT 7 satellite image (28 July 2017) in situ sampling (38 samples) was done from the leaves of potatoes and beans growing close to the mining town of Kajaran (Armenia). To estimate HMs contents, multivariate regression (multiple linear regression (MLR), partial least squares regression (PLSR)), and artificial neural network (ANN) were used. As input data for the models raw, atmospherically corrected (Dark Object Subtraction (DOS)) and hyperspherical direction cosines (HSDC) normalized values of SPOT 7 spectral data in combination with one or combined log10, multiplicative scatter correction (MSC), standard normal variate transform (SNV) preprocessing methods were utilized. The best results were obtained for Cu using MLR (R2 cal. = 0.79, R2 CV = 0.70, RMSEcal. = 11.27, RMSECV = 13.47) and ANN (R2 Train ≈ 0.80, R2 Test ≈ 0.72, RMSETrain ≈ 11, RMSETest ≈ 13) models in case of bean leaves. The results are quite optimistic, however, further research with the use of high spatial/spectral resolution satellite images is needed to improve the accuracy of models.

中文翻译：

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使用卫星数据估算小块地块上生长的作物叶片中的 Mo、Cu、Ni、Cd 含量

摘要 本研究的主要目标是通过多光谱卫星图像估算作物叶片中的重金属 (HMs)（钼 (Mo)、铜 (Cu)、镍 (Ni)、镉 (Cd)）含量。在获取 SPOT 7 卫星图像（2017 年 7 月 28 日）期间，对 Kajaran（亚美尼亚）采矿小镇附近生长的马铃薯和豆类的叶子进行了原位采样（38 个样本）。为了估计HMs的含量，使用了多元回归（多元线性回归（MLR）、偏最小二乘回归（PLSR））和人工神经网络（ANN）。作为模型的输入数据，原始、大气校正（暗对象减法 (DOS)）和超球面方向余弦 (HSDC) 归一化的 SPOT 7 光谱数据值与一个或组合 log10、乘法散射校正 (MSC)、使用标准正态变量变换 (SNV) 预处理方法。使用 MLR（R2 cal. = 0.79，R2 CV = 0.70，RMSEcal. = 11.27，RMSECV = 13.47）和 ANN（R2 Train ≈ 0.80，R2 Test ≈ 0.72，RMSETrain ≈ 11，RMSETest 为 Cu 获得最佳结果) 豆叶情况下的模型。结果相当乐观，但是，需要使用高空间/光谱分辨率卫星图像进行进一步研究，以提高模型的准确性。