Soil Salinity Estimation Based on Near-Ground Multispectral Imagery in Typical Area of the Yellow River Delta

This study chooses the core demonstration area of 'Bohai Barn' project as the study area, which is located in Wudi, Shandong Province. We first collected near-ground and multispectral images and surface soil salinity data using ADC portable multispectral camera and EC110 portable salinometer. Then three vegetation indices, namely NDVI, SAVI and GNDVI, were used to build 18 models respectively with the actual measured soil salinity. These models include linear function, exponential function, logarithmic function, exponentiation function, quadratic function and cubic function, from which the best estimation model for soil salinity estimation was selected and used for inverting and analyzing soil salinity status of the study area. Results indicated that all models mentioned above could effectively estimate soil salinity and models using SAVI as the dependent variable were more effective than the others. Among SAVI models, the linear model(Y=-0. 524x+0. 663, n=70) is the best, under which the test value of F is the highest as 141. 347 at significance test level, estimated R-2 0. 797 with a 93. 36% accuracy. Soil salinity of the study area is mainly around 2. 5 parts per thousand similar to 3. 5 parts per thousand, which gradually increases from southwest to northeast. This study has probed into soil salinity estimation methods based on near-ground and multispectral data, and will provide a quick and effective technical soil salinity estimation approach for coastal saline soil of the study area and the whole Yellow River Delta.