Sensitivity of satellite microwave and infrared observations to soil moisture at a global scale: Relationship of satellite observations to in situ soil moisture measurements

Abstract

This study presents a systematic and integrated analysis of the sensitivity of the available satellite observations to in situ soil moisture measurements. Although none of these satellites is optimized for land surface characterization, before the launches of the SMOS‐ and HYDROS‐dedicated missions they are the only potential sources of global soil moisture measurements. The satellite observations include passive microwave emissivities, active microwave scatterometer data, and infrared estimates of the diurnal amplitude of the surface skin temperature. The Global Soil Moisture Data Bank provides in situ soil moisture measurements in five separate regions. This simultaneous analysis of various satellite observations and the large amount of in situ measurements has two major advantages. First, this analysis helps identify and separate the physical mechanisms that affect the satellite observations. For example, we show that the passive microwave polarization differences at 19 GHz and above are essentially sensitive to the vegetation and not to the soil moisture (i.e., the correlation between microwave observations and soil moisture is only indirect and comes from the statistical correlation between vegetation and soil moisture). Second, this analysis enables an objective comparison of the relative potential of the various satellite observations for soil moisture retrieval when other conditions are held constant. The second part of this study benefits from this synthesis to derive a relationship between satellite observations and soil moisture at a global scale.

Comparing the Performance of Neural Network and Deep Convolutional Neural Network in Estimating Soil Moisture from Satellite Observations

“…In the learning phase, we need to collect the collocated input and output data to construct a training set. We put SMOSL3 TB data, ASCAT backscattering coefficients, MODIS NDVI and soil temperature together to generate a 19-dimensional input vector for each grid all over the globe, where the first 16 , and the other three inputs are the average backscattering coefficient at the incidence angle 40 • , the MODIS NDVI product MYD13C1, as well as the soil temperature in the first layer of soil; meanwhile, we use the ERA-Interim SM as the 1-dimensional output value. This training set is used to train the regression model to learn the fitting coefficients.…”

“…However, it is difficult to train an effective model that can be applied to the global SM retrieval when using a few in situ measurements as the target data. Thus, some works chose the simulated values by the radiative transfer model [15] or the estimated ones by the global land surface model [16,17] as the target data to train NN. In [12,18,19], NN was trained to describe the link between the satellite observations and the target SM values come from the Medium-range Weather Forecasts (ECMWF) model.…”

Analysis of SMOS brightness temperature and vegetation optical depth data with coupled land surface and radiative transfer models in Southern Germany

“…This is especially important as SMOS follows a novel technological concept. Validation of passive microwave soil moisture products is challenging due to the mismatch in scale between satellite products and point scale in situ measurements that are typically used for validation of remote-sensing based soil moisture products (Bartalis et al, 2008;Prigent et al, 2005). In situ measurements for satellite validation are usually collected in field campaigns over extended areas and during short periods of time or over longer time spans at few selected measuring locations.…”

Validation of Satellite Soil Moisture Retrievals using Precipitation Records in India

“…The reasons could be attributed to the heterogeneous behaviour of soil moisture and geographic, financial constraints to establish a very dense station network. Also, one may not be able to generalize their findings to a larger area based on at site studies (Prigent et al, 2005). This lead to retrieval of soil moisture through satellite remote sensing.…”