Optimal Solar Zenith Angle Definition for Combined Landsat-8 and Sentinel-2A/2B Data Angular Normalization Using Machine Learning Methods
Optimal Solar Zenith Angle Definition for Combined Landsat-8 and Sentinel-2A/2B Data Angular Normalization Using Machine Learning Methods
复制标题
使用机器学习方法对 Landsat-8 和 Sentinel-2A/2B 数据角度归一化组合的最佳太阳天顶角定义
DOI：
10.3390/rs13132598
复制
发表时间：
2021-07
期刊：
影响因子：
5
通讯作者：
Baozhang Chen
中科院分区：
文献类型：
--
作者：
Jian Li;Baozhang Chen
大地卫星8号和哨兵-2A/2B号的数据由于光谱带相似，经常合并用于地面监测。在Landsat-8和Sentinel-2A/2B反射率数据中都观察到了双向反射分布函数效应。然而，目前还没有太阳天顶角（θsz）的定义，该定义适用于三个传感器组合数据的BRDF调整反射率的归一化。本文描述了使用四种机器学习（ML）模型来预测适用于2018年组合数据双向反射率归一化的全球θsz。选择全球范围内收集的观测θsz以及刚果民主共和国（26.622°E，0.356°N），美国德克萨斯州（99.406°W，30.751°N）和芬兰（25.194°E，61.653°N）的三个地点来比较ML模型的性能。在全球范围内，支持向量回归（SVR），多层感知（MLP）和高斯过程回归（GPR）的ML模型表现出优于多项式回归的性能，考虑中心纬度作为输入来预测全局θsz。考虑到中心纬度和采集时间作为输入，GPR实现了最佳的整体性能，均方根误差（RMSE）为1.390°，平均绝对误差（MAE）为0.689°，决定系数（R2）为0.994。SVR在GPR之后显示RMSE为1.396°，MAE为0.638°，R2为0.994。对于一个特定的位置，SVR和GPR模型有更高的精度比多项式回归，与GPR表现出最好的性能，当中心纬度和采集时间被认为是输入。建议使用三个传感器的组合数据来预测全球θsz。
Data from Landsat-8 and Sentinel-2A/2B are often combined for terrestrial monitoring because of their similar spectral bands. The bidirectional reflectance distribution function (BRDF) effect has been observed in both Landsat-8 and Sentinel-2A/2B reflectance data. However, there is currently no definition of solar zenith angle (θsz) that is suitable for the normalization of the BRDF-adjusted reflectance from the three sensors’ combined data. This paper describes the use of four machine learning (ML) models to predict a global θsz that is suitable for the normalization of bidirectional reflectance from the combined data in 2018. The observed θsz collected globally, and the three locations in the Democratic Republic of Congo (26.622°E, 0.356°N), Texas in the USA (99.406°W 30.751°N), and Finland (25.194°E, 61.653°N), are chosen to compare the performance of the ML models. At a global scale, the ML models of Support Vector Regression (SVR), Multi-Layer Perception (MLP), and Gaussian Process Regression (GPR) exhibit comparably good performance to that of polynomial regression, considering center latitude as the input to predict the global θsz. GPR achieves the best overall performance considering the center latitude and acquisition time as inputs, with a root mean square error (RMSE) of 1.390°, a mean absolute error (MAE) of 0.689°, and a coefficient of determination (R2) of 0.994. SVR shows an RMSE of 1.396°, an MAE of 0.638°, and an R2 of 0.994, following GPR. For a specific location, the SVR and GPR models have higher accuracy than the polynomial regression, with GPR exhibiting the best performance, when center latitude and acquisition time are considered as inputs. GPR is recommended for predicting the global θsz using the three sensors’ combined data.
登录
查看更多内容
使用超高分辨率图像比较基于对象的土地覆盖分类的机器学习分类器
影响因子：
5
作者：
Qian Yuguo;Zhou Weiqi;Yan Jingli;Li Weifeng;Han Lijian
通讯作者：
Han Lijian
影响因子：
13.5
作者：
Masek, Jeffrey G.;Wulder, Michael A.;Jenstrom, Del T.
通讯作者：
Jenstrom, Del T.
DOI：
10.4018/978-1-7998-1192-3.ch008
发表时间：
2020
期刊：
Advances in Systems Analysis, Software Engineering, and High Performance Computing
影响因子：
--
作者：
Menaga D.;R. S.
通讯作者：
Menaga D.;R. S.
DOI：
10.4018/978-1-5225-9096-5.ch007
发表时间：
2021-07
期刊：
Smart Computational Intelligence in Biomedical and Health Informatics
影响因子：
--
作者：
A. Sinha;S. Gupta;Anurag Tiwari;Amrita Chaturvedi
通讯作者：
A. Sinha;S. Gupta;Anurag Tiwari;Amrita Chaturvedi
DOI：
10.4018/978-1-5225-7862-8.ch007
发表时间：
2019
期刊：
Handbook of Research on Deep Learning Innovations and Trends
影响因子：
--
作者：
K. Lakhtaria;Darshankumar Modi
通讯作者：
K. Lakhtaria;Darshankumar Modi