Simultaneous estimation of surface soil moisture and salinity during irrigation with the moisture-salinity-dependent spectral response model

Soil moisture and salinity are both important environmental variables for crop growth in agricultural production areas. Optical remote-sensing datasets from different sensors are available for estimating soil moisture and salinity from different spatial-temporal scales. Given the co-regulation of soil spectral reflectance (SR) by soil moisture and salinity, the simultaneous estimation of moisture and salinity in saline soil may result in great bias and uncertainty. To address this problem, soil samples were collected in the salinized area during irrigation. Synchronously, processed multi-spectral images were acquired from Sentinel-2 satellite. The spectrum mechanism responsive to soil moisture and salinity was verified by statistical tests, and its corresponding mathematical model (MSS model) was developed to identify the dominant factors affecting SR and to inverse moisture and salinity. The result showed that the effects of moisture and salinity were temporally constant (facilitation) and changing (from inhibition to facilitation), respectively, during the irrigation stages. The dominant factors in the variation of SR shifted from salinity and moisture-salinity interaction to moisture. Reliable accuracy was achieved in the moisture and salinity estimation using inverse MSS model. The profile from the series of estimations can further reveal the dynamic changes of soil moisture and salinity content during irrigation, and provide guidance for local irrigation management.

Automated summary

The study verified the spectrum mechanism responsive to soil moisture and salinity by statistical tests, and developed the MSS model to inverse the factors affecting SR and estimate moisture and salinity accurately. The results provide dynamic information on soil moisture and salinity content and guide local irrigation management.