基于PlanetScope影像的典型绿洲土壤盐渍化数字制图

Subjects: Geosciences >> Geography submitted time 2023-09-19 Cooperative journals: 《干旱区地理》

李科 丁建丽 韩礼敬 葛翔宇 顾永昇 周 倩 吕阳霞

Abstract： High- resolution soil salinity maps are urgently needed in arid and semi-arid regions to visualize thesubtle spatial variations in salinity distribution. These maps are crucial for guiding the development of land resource management policies and water resource management policies in salt-affected and potentially salt-affectedareas, aiming to prevent further soil degradation and ensure sustainable agricultural economic development andfood security. Based on PlanetScope imagery, vegetation spectral indices and soil salinity indices were extracted,resulting in a total of 21 variables. These variables were used as input for the Bagging algorithm to construct asoil salinity prediction model, referred to as ModelⅠ- . The max-relevance and min-redundancy (mRMR) methodwas employed to select relevant feature variables, which were then inputted into the Bagging algorithm to build asoil salinity prediction model, referred to as Model-Ⅱ. Field sampling data were used to assist in model buildingand validation. Model-Ⅰ and Model-Ⅱ were evaluated using model evaluation metrics. The results indicate thatthe prediction performance of Model-Ⅱ is better than that of Moedl-Ⅰ (mean R2=0.66, mean RMSE=18.00 dS·m-1,mean PRIQ=3.21 for the validation set), and that mRMR effectively reduces the multidimensional feature redundancy. PlanetScope images combined with the mRMR method successfully mapped high-resolution soil salinity,which provided more detailed information on the spatial distribution of soil salinity, and the results of the studypromoted the use of PlanetScope data to monitor soil salinity information.

 Hits 576  Downloads 139  Comment