Estimation of Crop Yield From Combined Optical and SAR Imagery Using Gaussian Kernel Regression
Estimation of Crop Yield From Combined Optical and SAR Imagery Using Gaussian Kernel Regression
复制标题
利用高斯核回归从光学和合成孔径雷达图像估计作物产量
DOI：
10.1109/jstars.2021.3118707
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发表时间：
2021-01-01
影响因子：
5.5
通讯作者：
Cheng, Tao
中科院分区：
文献类型：
--
作者：
Alebele, Yeshanbele;Wang, Wenhui;Cheng, Tao
合成孔径雷达（SAR）干涉相干性可以补充光学数据来估计作物生长参数，但尚未在预测作物产量方面进行研究。许多研究使用机器学习方法，例如神经网络、随机森林和高斯过程回归，来根据遥感数据估计作物产量。然而，它们的性能取决于可用的地面实况数据的数量。本研究提出了使用有限数量的地面实况数据根据光学和 SAR 图像进行水稻产量估计的高斯核回归。主要目标是通过高斯核回归研究 Sentinel-2 植被指数和 Sentinel-1 干涉相干数据的协同使用来估算水稻产量。利用 2019 年和 2020 年在中国江苏省兴化县收集的实地测量产量数据评估了预测准确性。在所有情况下，高斯核回归都优于概率高斯回归和贝叶斯线性推理。在独立使用光学和SAR数据的情况下，光学红边差植被指数（RDVI1）（r（2）= 0.65，RMSE = 0.61 t/ha）比干涉相干（r（2）= 0.52和RMSE = 0.79 t/ha）获得了更好的预测精度。在航向阶段将RDVI1与干涉相干相结合（r（2）可以达到最高的预测精度= 0.81，RMSE = 0.55 吨/公顷）。结果表明，卫星干涉相干性和光学指数之间的协同作用对于利用高斯核回归进行作物产量绘图具有重要价值。
The synthetic aperture radar (SAR) interferometric coherence can complement optical data for the estimation of crop growth parameters, but it has not been yet investigated for predicting crop yield. Many studies have used machine-learning methods, such as neural networks, random forest, and Gaussian process regression, to estimate crop yield from remotely sensed data. However, their performance depends on the amount of available ground truth data. This study proposed Gaussian kernel regression for rice yield estimation from optical and SAR imagery using a limited amount of ground truth data. The main objective was to investigate the synergetic use of Sentinel-2 vegetation indices and Sentinel-1 interferometric coherence data through Gaussian kernel regression for estimating rice grain yield. The prediction accuracy was assessed using in situ measured yield data collected in 2019 and 2020 over Xinghua county in Jiangsu Province, China. In all cases, Gaussian kernel regression outperformed the probabilistic Gaussian regression and Bayesian linear inference. With the independently used optical and SAR data, a better prediction accuracy was achieved with the optical red edge difference vegetation index (RDVI1) (r(2) = 0.65, RMSE = 0.61 t/ha) than with the interferometric coherence (r(2) = 0.52 and RMSE = 0.79 t/ha).The highest prediction accuracy can be achieved by combining RDVI1 with interferometric coherence at the heading stage (r(2) = 0.81 and RMSE = 0.55 t/ha). The results suggest the value of synergy between satellite interferometric coherence and optical indices for crop yield mapping with Gaussian kernel regression.