2000-2020青藏高原湖泊叶绿素数据反演产品1套

利用Landsta-5和Landsat-7两个卫星的遥感影像，在Google Earth Engine（GEE）平台处理1986-2021年反射率数据，获取6个波段的反射率，运用传统的波段比值、多元线性回归、逐步回归和指数算法等方法建立了波段运算组合的200个参数；使用2012-2020年采集的青藏高原95个湖泊的实测Chl-a数据与波段运算组合的200个参数进行关系分析，筛选出较好的8种关系模型，再利用机器学习方法，选择最优的BP神经网络反演模型，得到青藏高原318个>10 km2湖泊的叶绿素数据（Chl-a）

Using remote sensing images from Landsat-5 and Landsat-7 satellites, reflectance data from 1986 to 2021 were processed on the Google Earth Engine (GEE) platform to acquire reflectance values of 6 bands. 200 parameters based on combined band operations were established via traditional methods including band ratio, multiple linear regression, stepwise regression and index algorithms. Correlation analysis was then performed between the in-situ Chl-a data of 95 lakes on the Qinghai-Tibet Plateau collected during 2012-2020 and the 200 combined band operation parameters, and 8 preferable correlation models were screened out. Furthermore, machine learning approaches were employed to select the optimal backpropagation (BP) neural network inversion model, thereby obtaining chlorophyll-a (Chl-a) data for 318 lakes on the Qinghai-Tibet Plateau with an area greater than 10 km².

搜集汇总

背景与挑战

背景概述

该数据集是2000-2020年青藏高原湖泊叶绿素数据反演产品，基于Landsat-5和Landsat-7卫星遥感影像，利用Google Earth Engine平台处理反射率数据，并通过机器学习方法（BP神经网络）反演生成。数据集覆盖青藏高原318个面积大于10平方公里的湖泊，提供叶绿素浓度数据，用于湖泊生态监测研究，数据格式为png，总容量4.18 MB。