Welcome to Smart Agriculture

Smart Agriculture ›› 2022, Vol. 4 ›› Issue (4): 61-73.doi: 10.12133/j.smartag.SA202212001

• Topic--Smart Farming of Field Crops • Previous Articles     Next Articles

Machine Learning Inversion Model of Soil Salinity in the Yellow River Delta Based on Field Hyperspectral and UAV Multispectral Data

FAN Chengzhi1(), WANG Ziwen1, YANG Xingchao1, LUO Yongkai2, XU Xuexin3, GUO Bin1, LI Zhenhai1()   

  1. 1.College of Geodesy and Geomatics Information Technology, Shandong University of Science and Technology, Qingdao 266590, China
    2.Shandong Key Laboratory of Eco-Environmental Science for Yellow River Delta, Binzhou University, Binzhou 256603, China
    3.College of Agronomy, Qingdao Agricultural University, Qingdao 266109, China
  • Received:2022-12-13 Online:2022-12-30
  • corresponding author: LI Zhenhai, E-mail:lizh323@126.com
  • About author:FAN Chengzhi, E-mail:fancz98@163.com
  • Supported by:
    Shandong Provincial Natural Science Foundation (ZR2022MD017); Hebei Province Key Research and Development Program Project (22326406D)


Soil salinization in the Yellow River Delta is a difficult and miscellaneous disease to restrict the development of agricultural economy, and further hinders agricultural production. To explore the retrieval of soil salt content from remote sensing images under the condition of no vegetation coverage, the typical area of the Yellow River Delta was taken as the study area to obtain the hyperspectral of surface features, the multispectral of UAVs and the soil salt content of sample points. Three representative experimental areas with flat terrain and obvious soil salinization characteristics were set up in the study area, and 90 samples were collected in total. By optimizing the sensitive spectral parameters, machine learning algorithms of partial least squares regression (PLSR) and random forest (RF) for inversion of soil salt content were used in the study area. The results showed that: (1) Hyperspectral band of 1972 nm had the highest sensitivity to soil salt content, with correlation r of -0.31. The optimized spectral parameters of shortwave infrared can improve the accuracy of estimating soil salt content. (2) RF model optimized by two different data sources had better stability than PLSR model. RF model performed well in terms of generalization ability and balance error, but it had some over-fitting problems. (3) RF model based on ground feature hyperspectral (R2 =0.54, verified RMSE=3.30 g/kg) was superior to the random forest model based on UAV multispectral (R2 =0.54, verified RMSE=3.35 g/kg). The combination of image texture features improved the estimation accuracy of multispectral model, but the verification accuracy was still lower than that of hyperspectral model. (4) Soil salt content based on UAV multi-spectral imageries and RF model was mapped in the study area. This study demonstrates that the level of soil salinization in the Yellow River Delta region is significantly different in geographical location. The cultivated land in the study area is mainly light and moderate salinized soil with has certain restrictions on crop cultivation. Areas with low soil salt content are suitable for planting crops in low salinity fields, and farmland with high soil salt content is suitable for planting crops with high salinity tolerance. This study constructed and compared the soil salinity inversion models of the Yellow River Delta from two different sources of data, optimized them based on the advantages of each data source, explored the inversion of soil salinity content without vegetation coverage, and can provide a reference for more accurate inversion of land salinization.

Key words: soil salinity, remote sensing, ground feature hyperspectral, UAV multispectral, partial least squares regression, random forest, machine learning

CLC Number: