Synergetic Use of Sentinel-1 and Sentinel-2 Data for Wheat-Crop Height Monitoring Using Machine Learning

Lwandile Nduku, C. Munghemezulu, Zinhle Mashaba-Munghemezulu, Phathutshedzo Eugene Ratshiedana, Sipho Sibanda, J. Chirima
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Abstract

Monitoring crop height during different growth stages provides farmers with valuable information important for managing and improving expected yields. The use of synthetic aperture radar Sentinel-1 (S-1) and Optical Sentinel-2 (S-2) satellites provides useful datasets that can assist in monitoring crop development. However, studies exploring synergetic use of SAR S-1 and optical S-2 satellite data for monitoring crop biophysical parameters are limited. We utilized a time-series of monthly S-1 satellite data independently and then used S-1 and S-2 satellite data synergistically to model wheat-crop height in this study. The polarization backscatter bands, S-1 polarization indices, and S-2 spectral indices were computed from the datasets. Optimized Random Forest Regression (RFR), Support Vector Machine Regression (SVMR), Decision Tree Regression (DTR), and Neural Network Regression (NNR) machine-learning algorithms were applied. The findings show that RFR (R2 = 0.56, RMSE = 21.01 cm) and SVM (R2 = 0.58, RMSE = 20.41 cm) produce a low modeling accuracy for crop height estimation with S-1 SAR data. The S-1 and S-2 satellite data fusion experiment had an improvement in accuracy with the RFR (R2 = 0.93 and RMSE = 8.53 cm) model outperforming the SVM (R2 = 0.91 and RMSE = 9.20 cm) and other models. Normalized polarization (Pol) and the radar vegetation index (RVI_S1) were important predictor variables for crop height retrieval compared to other variables with S-1 and S-2 data fusion as input features. The SAR ratio index (SAR RI 2) had a strong positive and significant correlation (r = 0.94; p < 0.05) with crop height amongst the predictor variables. The spatial distribution maps generated in this study show the viability of data fusion to produce accurate crop height variability maps with machine-learning algorithms. These results demonstrate that both RFR and SVM can be used to quantify crop height during the growing stages. Furthermore, findings show that data fusion improves model performance significantly. The framework from this study can be used as a tool to retrieve other wheat biophysical variables and support decision making for different crops.
利用机器学习将哨兵 1 号和哨兵 2 号数据协同用于小麦株高监测
监测不同生长阶段的作物高度为农民提供了宝贵的信息,对管理和提高预期产量非常重要。合成孔径雷达哨兵-1(S-1)和光学哨兵-2(S-2)卫星提供了有用的数据集,有助于监测作物生长情况。然而,探索如何协同使用合成孔径雷达 S-1 和光学 S-2 卫星数据监测作物生物物理参数的研究十分有限。在本研究中,我们独立使用了每月 S-1 卫星数据的时间序列,然后协同使用 S-1 和 S-2 卫星数据建立小麦作物高度模型。根据数据集计算了偏振反向散射波段、S-1 偏振指数和 S-2 光谱指数。应用了优化的随机森林回归(RFR)、支持向量机回归(SVMR)、决策树回归(DTR)和神经网络回归(NNR)机器学习算法。研究结果表明,RFR(R2 = 0.56,RMSE = 21.01 厘米)和 SVM(R2 = 0.58,RMSE = 20.41 厘米)对 S-1 SAR 数据进行作物高度估算的建模精度较低。S-1 和 S-2 卫星数据融合试验提高了精度,RFR(R2 = 0.93,RMSE = 8.53 厘米)模型优于 SVM(R2 = 0.91,RMSE = 9.20 厘米)和其他模型。归一化偏振(Pol)和雷达植被指数(RVI_S1)是作物高度检索的重要预测变量,而其他变量则以 S-1 和 S-2 数据融合作为输入特征。在预测变量中,合成孔径雷达比值指数(SAR RI 2)与作物高度呈显著正相关(r = 0.94;p < 0.05)。本研究生成的空间分布图表明,利用机器学习算法融合数据生成准确的作物高度变化图是可行的。这些结果表明,RFR 和 SVM 都可用于量化生长阶段的作物高度。此外,研究结果表明,数据融合可显著提高模型性能。这项研究的框架可用作检索其他小麦生物物理变量和支持不同作物决策的工具。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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