Monitoring Soil Moisture in an Agricultural Test Site Using SAR Data: Design and Test of a Pre-Operational Procedure

Abstract

An algorithm for pre-operational high resolution soil moisture mapping using Synthetic Aperture Radar (SAR) data is presented. It has been conceived to be inserted in the operational weather alert system of the Italian Department of Civil Protection. The Maximum A Posteriori (MAP) probability criterion is applied to retrieve soil moisture by inverting a forward backscattering model, and ancillary data such as optical images and land cover maps are also used to identify areas in which the retrieval can be carried out. The well-established semiempirical water cloud model is adopted to correct for the effect of vegetation on SAR data. In anticipation of the use of the algorithm in an operational system, in which the SAR-derived high resolution soil moisture product can be assimilated within weather prediction models or hydrological ones, an uncertainty index is associated to each estimate. The algorithm has been tested on a dataset consisting of ground data gathered for seven years (2003-2010) on an agricultural test site in Northern Italy and radar data provided by the C-band ENVISAT/ASAR instrument. A comparison, performed at field scale, between estimated and in situ soil moisture data has shown that, by discarding the estimates with the largest uncertainty, the correlation coefficient can exceed 0.80 and the root mean square estimation error is less than 0.05 m(3)/m(3). Moreover, the uncertainty index has turned out to be fairly correlated to the actual estimation error

Analysis of two years of ASCAT-and SMOS-derived soil moisture estimates over Europe and North Africa

“…Sensors operating at C-band turned out to be useful for SMC mapping; in particular, sensitivity to SMC was demonstrated by the Advanced Microwave Scanning Radiometer on Earth Observing System (AMSR-E) aboard the AQUA satellite [e.g. Gruhier et al, 2010] and by active instruments, such as the scatterometer onboard ERS [Wagner et al, 1999], as well as by the ENVISAT/Advanced Synthetic Aperture Radar (ASAR) [Mattia et al, 2006;Pathe et al, 2007;Paloscia et al, 2008;Pierdicca et al, 2013b]. A great potential is offered by polarimetric sensors, as demonstrated by the Airborne Synthetic Aperture Radar (AirSAR) [Pierdicca et al, 2010] and the SIR-C radar [Pierdicca et al, 2008a].…”

An evaluation of the potential of Sentinel 1 for improving flash flood predictions via soil moisture–data assimilation

“…the plant water content, to describe vegetation. This parameter was estimated through empirical relationships from both the Normalized Difference Vegetation Index (NDVI) obtained from Landsat 8 (Pierdicca et al, 2013) and from CSK ® acquisitions (Paloscia et al, 2014). The WCM introduces two parameters (A and B) that are generally crop dependent, so that a rigorous determination of A and B would imply taking into account both the crop type and its phenological cycle.…”

Empirical fitting of forward backscattering models for multitemporal retrieval of soil moisture from radar data at L-band

“…The subscripts (1, 2, 3) are related to the HH, VV, and HV polarizations, respectively. Subscript mono is used because it represents the cost function for a standard monotemporal MAP algorithm, 13 whereas the summation is related to the multitemporal approach. The minimum of d is found using a Monte Carlo approach.…”

“…In most of the works in the literature, soil moisture retrieval over vegetated surfaces is based on the water cloud model (WCM), 13 which does not consider the multiple scattering contribution from vegetation-soil interaction, and models the canopy as a water cloud whose droplets are randomly distributed within the layer. For instance, the WCM was used by DabrowskaZielinska et al 21 to analyze the C-and L-band backscatter of several crops, such as wheat, grassland, and rape, showing a correlation coefficient between modeled and measured backscattering around 0.8.…”

“…3,4 Soil moisture maps obtained from synthetic aperture radar (SAR) data are characterized by high spatial resolution, but SAR temporal resolution was generally considered a critical aspect for operational soil moisture applications. However, new SAR missions have been developed to provide data with short revisit time, such as the European Space Agency (ESA) Sentinel-1 (S-1) 5 and the Soil Moisture Active/Passive (SMAP) mission of the National Aeronautics and Space Administration (NASA).…”