Soil moisture mapping using Sentinel-1 images: Algorithm and preliminary validation S. Paloscia a, ⁎, S. Pettinato a , E. Santi a , C. Notarnicola b , L. Pasolli b , A. Reppucci c a Institute of Applied Physics, National Research Council (IFAC-CNR), via Madonna del Piano, 10, 50019 Florence, Italy b EURAC Research, Bolzano, Italy c Starlab, Barcelona, Spain abstract article info Article history: Received 21 September 2012 Received in revised form 11 January 2013 Accepted 28 February 2013 Available online 9 April 2013 Keywords: Sentinel-1 Backscattering coefficient Soil moisture maps Inversion algorithms Artificial Neural Network The main objective of this research is to develop, test and validate a soil moisture content (SMC) algorithm for GMES Sentinel-1 characteristics. The SMC product, which is to be generated from Sentinel-1 data, requires an algorithm capable of processing operationally in near-real-time and delivering the product to the GMES services within 3 h from observation. An approach based on an Artificial Neural Network (ANN) has been proposed that represents a good compromise between retrieval accuracy and processing time, thus enabling compliance with the timeliness requirements. The algorithm has been tested and subsequently validated in several test areas in Italy, Australia, and Spain. In all cases the validation results were very much in line with GMES requirements (with RMSE generally b 4%SMC – between 1.67%SMC and 6.68%SMC – and very low bias), except for the case of the test area in Spain, where the validation results were penalized by the availability of only VV polarized SAR images and MODIS low-resolution NDVI. Nonetheless, the obtained RMSE was slightly higher than 4%SMC. © 2013 Elsevier Inc. All rights reserved. 1. Introduction Soil moisture content (SMC) is a key hydrological and climatic var- iable in various application domains (e.g. Entekhabi et al., 1994; Jackson, 1993). Unfortunately, the retrieval from local direct measure- ments of distributed, quantitative and accurate information relative to the moisture level of soils on a global scale is almost impracticable, due to the high spatial variability of the target variable. Such methods are moreover time consuming and expensive. In recent decades, many spatially-distributed hydrological models have been developed and successfully applied at scales ranging from small catchments to the globe (e.g. Entekhabi & Eagleson, 1989; Famiglietti & Wood, 1994). However, accurate spatial prediction of soil moisture requires an appropriate accounting for the variability of soil characteristics and climate forcing. Moreover, an accurate as- sessment cannot be made without adequate spatially-distributed soil moisture measurements at the scale of interest. The possibility of measuring SMC on a large scale from satellite sen- sors, with complete, repeated and frequent coverage of the Earth's sur- face is, therefore, extremely enticing (Beaudoin et al., 1990; Benallegue et al., 1995). Research activities carried out worldwide in the past have demonstrated that sensors operating in the low-frequency portion of the microwave spectrum (P- to L-band) are sensitive to variations in the moisture level of a soil layer, the depth of which depends on the soil characteristics, the moisture profile and the signal wavelength (Macelloni et al., 1999; Shi et al., 1992, 1997). However, at present, most SAR systems onboard remote sensing satellites (e.g., RADARSAT2, COSMO SkyMed and TerraSAR-X) operate at C- and X-bands, which, in terms of sensitivity to soil moisture variations over vegetated terrains, are not the best suited ones for retrieving SMC. Although some prelim- inary studies indicate the feasibility of retrieving soil moisture also by using the new generation X-band SAR sensors (Baghdadi et al., 2012), working at such high frequencies implies the challenge of coping with the interfering effects introduced by surface roughness and, above all, by vegetation coverage on the backscattering coefficient. Under these operational conditions, an estimate of spatial variations of moisture with the accuracy requirements of the end-user is still problematic and challenging. Even when a priori knowledge of the meteorological conditions, soil properties, and surface coverage are exploited together with correcting procedures for the effects of soil roughness and vegeta- tion, the retrieval of soil moisture remains a challenge. From an analytical point of view, the retrieval of soil parameters from radar measurements falls within the category of ill-posed prob- lems, because, in general, more than one combination of soil character- istics (in terms of SMC, roughness, vegetation coverage, etc.) leads to the same electromagnetic response at the sensor. Multi-sensor tech- niques have a certain potential in distinguishing between different con- tributions of the soil features to the global system response. The rationale is that soil characteristics affect the microwave signal differ- ently and to a different extent, depending on the sensor configuration. By using sensors at different frequencies, polarizations, and incidence Remote Sensing of Environment 134 (2013) 234–248 ⁎ Corresponding author. Tel.: +39 0555226494. E-mail address: S.Paloscia@ifac.cnr.it (S. Paloscia). 0034-4257/$ – see front matter © 2013 Elsevier Inc. All rights reserved. http://dx.doi.org/10.1016/j.rse.2013.02.027 Contents lists available at SciVerse ScienceDirect Remote Sensing of Environment journal homepage: www.elsevier.com/locate/rse