Lower Bua River In-situ water quality, GEE Sentinel 2 reflectance values and Fish CPUE data

The data has 4 categories which are In-situ water quality data obtained during the monthly monitoring surveys, fish catch per unit effort data obtained each month on landing sites, point type shapefile for the sampling stations and 2 reflectance values datasets obtained from sentinel 2 level 2A data which is already atmospherically corrected by Sen2Cor. The reflectance values were in two categories- one timeseries data from the year 2018 to 2022 which produced 557 observations processed at less than 3% cloudy cover percentage; and the other training data reflectance values from 5 stations processed at less than 1 to 3 % cloud cover percentage depending on the image availability with preference given to images which has the least cloud cover percentage and were colocated with the In-situ water quality surveys at +/- one day of sampling.
The procedure for obtaining the reflectance values and tiff images for the study area was done in google earth engine after importing the shapefile as an assert with the attached link , the same with obtaining the time series reflectance values. Timeseries reflectance data, in-situ data and fish CPUE data were then further processed in python notebook using machine learning methods. The attached .py file contains the detailed python procedure

本数据集包含4类数据，分别为月度监测调查中获取的原位水质数据（In-situ water quality data）、每月在卸鱼站点采集的单位捕捞努力量渔获量（Catch Per Unit Effort, CPUE）数据、采样站点的点类型形状文件（Shapefile），以及2组源自哨兵2号（Sentinel 2）L2A级数据的反射率值数据集——该数据集已通过Sen2Cor工具完成大气校正。上述反射率值可分为两类：第一类为2018年至2022年的时序反射率数据，共生成557条有效观测值，对应影像的云量占比均低于3%；第二类为来自5个采样站点的训练用反射率数据，其影像云量占比介于1%至3%之间（具体取决于可用影像资源），优先选取云量占比最低、空间位置与原位水质调查采样站点匹配且与采样时间相差不超过±1天的影像。本研究区域反射率值与TIFF影像的获取流程为：先将形状文件作为资产导入谷歌地球引擎（Google Earth Engine）并附上关联链接，时序反射率数据的获取流程与此一致。随后，研究人员通过Python Notebook利用机器学习方法对时序反射率数据、原位水质数据及单位捕捞努力量渔获量数据进行了进一步处理。附件中的.py文件包含完整的Python处理流程。