Spatial and Temporal Distribution of Soil Moisture at the Catchment Scale Using Remotely-Sensed Energy Fluxes

Abstract

Despite playing a critical role in the division of precipitation between runoff and infiltration, soil moisture (SM) is difficult to estimate at the catchment scale and at frequent time steps, as is required by many hydrological, erosion and flood simulation models. In this work, an integrated methodology is described to estimate SM at the root zone, based on the remotely-sensed evaporative fraction (Λ) and ancillary information on soil and meteorology. A time series of Terra MODIS satellite images was used to estimate SM maps with an eight-day time step at a 250-m spatial resolution for three diverse catchments in Europe. The study of the resulting SM maps shows that their spatial variability follows the pattern of land cover types and the main geomorphological features of the catchment, and their temporal pattern follows the distribution of rain events, with the exception of irrigated land. Field surveys provided in situ measurements to validate the SM maps' accuracy, which proved to be variable according to site and season. In addition, several factors were analyzed in order to explain the variation in the accuracy, and it was shown that the land cover type, the soil texture class, the temporal difference between the datasets' acquisition and the presence of rain events during the measurements played a significant role, rather than the often referred to scale difference between in situ and satellite observations. Therefore, the proposed methodology can be used operationally to estimate SM maps at the catchment scale, with a 250-m spatial resolution and an eight-day time step.

Soil Texture Estimation Using Radar and Optical Data from Sentinel-1 and Sentinel-2

“…In this context, although ground measurements have long been used to estimate the water status or soil texture, they are time-consuming and costly procedures, and are generally considered to be inadequate, since they do not allow spatio-temporal variations in soil moisture to be correctly monitored. The scientific community has thus devoted considerable effort to the development of remote sensing products, in an effort to improve the accuracy and spatio-temporal coverage of these observations when they are interpreted for hydrological applications and water resource management [4][5][6][7][8].…”

Recent Advances in Soil Moisture Estimation from Remote Sensing

“…For example, Alexandridis, et al [31] applied a simple approach to estimate root zone soil moisture based on remotely sensed energy balance fluxes and ancillary soil and meteorology data. Without the need of microwave data, the study mainly relies on the moderate resolution imaging spectroradiometer (MODIS) optical and thermal band data.…”

Contribution of Remote Sensing on Crop Models: A Review

“…Since the beginning, many scientists have used the available satellite data to retrieve canopy state variables over large areas. Among them, LAI is being monitored frequently across various scales and resolutions, and together with actual evapotranspiration and soil moisture estimations from thermal satellite images [67,68], they have been used with crop models. Remote sensing data are mostly used to determine light interception (e.g., LAI or fAPAR), and provide the spatial information of the actual growth status of the crop.…”