Soil Salinity and Soil Water Content Estimation Using Digital Images in Coastal Field: A Case Study in Yancheng City of Jiangsu Province, China

Soil is the essential part for agricultural and environmental sciences, and soil salinity and soil water content are both the important influence factors for sustainable development of agriculture and ecological environment. Digital camera, as one of the most popular and convenient proximal sensing instruments, has its irreplaceable position for soil properties assessment. In this study, we collected 52 soil samples and photographs at the same time along the coast in Yancheng City of Jiangsu Province. We carefully analyzed the relationship between soil properties and image brightness, and found that soil salt content had higher correlation with average image brightness value than soil water content. From the brightness levels, the high correlation coefficients between soil salt content and brightness levels concentrated on the high brightness values, and the high correlation coefficients between soil water content and brightness levels focused on the low brightness values. Different significance levels (P) determined different brightness levels related to soil properties, hence P value setting can be an optional way to select brightness levels as the input variables for modeling soil properties. Given these information, random forest algorithm was applied to develop soil salt content and soil water content inversion models using randomly 70% of the dataset, and the rest data for testing models. The results showed that soil salt content model had high accuracy (R-v(2) = 0.79, RMSEv = 12 g/kg, and RPDv = 2.18), and soil water content inversion model was barely satisfied (R-v(2) = 0.47, RMSEv = 3.04%, and RPDv = 1.38). This study proposes a method of modeling soil properties with a digital camera. Combining unmanned aerial vehicle (UAV), it has potential popularization and application value for precise agriculture and land management.

Automated summary

This study used a digital camera to assess soil salt content and soil water content, and found that soil salt content had a closer relationship with image brightness. Inversion models for soil salt content and soil water content were developed using the random forest algorithm, and the results showed a high accuracy for the soil salt content model and mediocre performance for the soil water content inversion model.