Digital mapping of soil salinization in a typical oasis based on PlanetScope images
Received date: 2022-09-30
Revised date: 2022-10-29
Online published: 2023-09-21
High-resolution soil salinity maps are urgently needed in arid and semi-arid regions to visualize the subtle 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-affected areas, aiming to prevent further soil degradation and ensure sustainable agricultural economic development and food 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 a soil salinity prediction model, referred to as Model-Ⅰ. The max-relevance and min-redundancy (mRMR) method was employed to select relevant feature variables, which were then inputted into the Bagging algorithm to build a soil salinity prediction model, referred to as Model-Ⅱ. Field sampling data were used to assist in model building and validation. Model-Ⅰ and Model-Ⅱ were evaluated using model evaluation metrics. The results indicate that the 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 study promoted the use of PlanetScope data to monitor soil salinity information.
Key words： PlanetScope; mRMR; Bagging; soil salinization; digital mapping techniques
Ke LI , Jianli DING , Lijing HAN , Xiangyu GE , Yongsheng GU , Qian ZHOU , Yangxia LYU . Digital mapping of soil salinization in a typical oasis based on PlanetScope images[J]. Arid Land Geography, 2023 , 46(8) : 1291 -1302 . DOI: 10.12118/j.issn.1000-6060.2022.496
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