Development of soil moisture indices from differences in water absorption between shortwave-infrared bands

Soil moisture (SM) controls the exchange of water and heat energy between the land surface and the atmosphere through evaporation and plant transpiration, and timely and accurate estimates of soil moisture are crucial for many studies. Although remote sensing provides many algorithms to obtain bare-soil moisture on large scales (e.g., Soil Moisture and Ocean Salinity (SMOS) and Soil Moisture Active and Passive (SMAP) products), these algorithms are restricted to studies with low ground resolution. In the present study, we propose and evaluate three normalized shortwave-infrared (SWIR) difference bare soil moisture indices [NSDSI1 = (B_SWIR1-B_SWIR2)/B_SWIR1, NSDSI2 = (B_SWIR1 − B_SWIR2)/B_SWIR2, NSDSI3 = (B_SWIR1 − B_SWIR2)/(B_SWIR1 + B_SWIR2), where, SWIR1 = 1550–1750 nm, SWIR2 = 2100–2300 nm] to estimate the bare-soil moisture content and, based on the water absorption difference between shortwave-infrared bands, use them to map bare-soil moisture with high ground resolution Sentinel-2 MSI images. The three proposed bare-soil moisture indices are obtained by using the different water absorption in shortwave-infrared bands. Four traditional hyperspectral-based bare-soil moisture indices (such as the water index SOIL, or WISOIL, the normalized soil moisture index, or NSMI, etc.) were used as benchmark. The results show that (i) the differences in water absorption between shortwave-infrared bands is linear in soil moisture content; (ii) the combined use of two shortwave-infrared bands from four soils provides more accurate bare-soil moisture estimates than do single shortwave-infrared (SWIR) bands, and (iii) our proposed bare-soil moisture indices can be applied on broadband remote-sensing images (such as Landsat, Sentinel-2 MSI). The bare-soil moisture estimates obtained by using the proposed bare-soil moisture indices may help to obtain bare-soil moisture maps with high ground resolution.