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The purpose of this work is to investigate the possible use of MODIS and ENVISAT-ASAR Data to extract soil moisture (SM) field and validate the developed systems by means of in situ measurements.

The main idea of this study concerns the advantages that could arise from a continuous and almost real time SM monitoring, permitted by wide-swath passive optical sensors that allow evaluating the dynamic evolution of different phenomena on a wide range of application fields: from hydrological monitoring and management to agricultural applications and so on. Observations provided by MODIS have no direct physical relation with soil water content. On the other hand, the relationship between Land Surface temperature (LST), NDVI and in situ measurements have shown many potentialities to indirectly retrieve SM information.

The use of SAR data for validation purposes is envisaged but not yet applied.

Calibration and validation phases are performed over a test site where in situ measurements are available.

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Bibliography

  • Alvarez-Mozos J., Casali, J., Gonzalez-Audicana M., Verhoest N. E. C. 2006. Assessment of the operational applicability of RADARSAT-1 data for surface soil moisture estimation, in IEEE Transactions on Geoscience and Remote Sensing, n.44 (4): 913–924

    Article  Google Scholar 

  • Baghdadi N., Zribi M. 2006. Evaluation of radar backscatter models IEM, OH and Dubois using experimental observations, in International Journal of Remote Sensing, n.27 (18): 20 September 2006, 3831–3852

    Article  Google Scholar 

  • Bignami C., Pierdicca N., Pulvirenti L., Ticconi F. Risultati preliminari di campagne sperimentali sulla sensibilità dei dati Envisat-ASAR all'umidità del terreno, XV Riunione Nazionale di Elettromagnetismo RINEM2004, Cagliari, http://www.elettromagnetismo.it/ atti_rinem/2004S14A05.pdf

  • Boisvert J. B., Crevier Y., Pultz T. J. 1996. Regional estimation of soil moisture using remote sensing, in Canadian Journal of Soil Science, n.76 (3): 325–334

    Google Scholar 

  • Cai G., Wu J., Xue J., Hu Y., Guo J., Tang J. 2005. Soil Moisture Retrieval from MODIS data in Northern China Plain Using Thermal Inertia Model (SoA-TI), 0-7803-9050-4/05 2005 IEEE

    Google Scholar 

  • Cravey R. L., Jackson T. J., Hsu Ann Y. 1998. ERS-2 SAR Backscattering Coefficient and Soil Moisture for the Southern Great Plains 1997 Hydrology Experiment. Retrieval of Bio- and Geo-Physical Parameters from SAR Data for Land Applications Workshop, ESTEC, 21–23, October 1998

    Google Scholar 

  • Dubois P.C., Vanzyl J., Engman T. 1995. Measuring soil-moisture with imaging radars, in IEEE Transactions on Geoscience and Remote Sensing, n.33 (4): 915–926

    Article  Google Scholar 

  • Glenn, N. F., Carr J. R. 2004. Establishing a relationship between soil moisture and RADARSAT-1 SAR data obtained over the Great Basin, Nevada, USA, in Canadian Journal of Remote Sensing, n.30 (2): 176–181

    Google Scholar 

  • Liang Shunlin 2000. Narrowband to broadband conversion of land surface albedo I algorithms, in Remote Sensing of Environment 2000, n.76, 213–238

    Article  Google Scholar 

  • Ma A., Xue Y. 1990. A study of remote sensing: information model of soil moisture, in Proceedings of the 11th Asian Conference on Remote Sensing, n.1, 11.1–11.5

    Google Scholar 

  • MEEO SOIL MAPPER TM Report description document 2007. Issue 3.7, Date: 12/03/2007, http://www.meeo.it/docs/SOIL_MAPPER_report.pdf

  • Munro R. K., Lyons W. F., Shao Y., Wood M. S., Hood L. M., Leslie L. M. 1998. Modelling land surface—atmosphere interactions over the Australian continent with an emphasis on the role of soil moisture. Environmental Modelling and Software, n.13 (3–4): 333–339

    Article  Google Scholar 

  • Paloscia S., Pampaloni P., Pettinato S., Poggi P., Santi E. 2005. The retrieval of soil moisture from Envisat-ASAR data, in European Association of Remote Sensing Laboratories—EARSeL eProceedings, n.4 (1): 44–52, http://www.eproceedings.org/static/vol04_1/ 04_1_paloscia1.html

  • Portmann F. 2000. The Land-SAF Soil Moisture Product of MIUB and BfG. Paper presented at the CM-SAF (EUMETSAT Climate Monitoring Satellite Application Facility) Training Workshop, Dresden, Germany, 20–22 November 2000, pp. 6

    Google Scholar 

  • Tucker C. J. 1979. Red and photographic infrared linear combinations for monitoring vegetation, in Remote Sensing of Environment, n.8 (2): 127–150

    Article  Google Scholar 

  • Turner D. P., Ritts W. D., Cohen W. B., Maeirsperger T. K., Gower S., Kirschbaum A. A., Running S. W., Zhao M., Wofsy S. C., Dunn A. L., Law B. E., Campbell J. L., Oechel W. C., Kwon H. J., Meyers T. P., Small E. E., Kurc S. A., Gamon J. A. 2005. Site-level evaluation of satellite-based global terrestrial gross primary production and net primary production monitoring, in Global Change Biology, n.11: 666–684

    Article  Google Scholar 

  • Verstraete M. M., Pinty B. 1996. Designing optimal spectral indexes for remote sensing applications, in IEEE Transactions on Geoscience and Remote Sensing, n.34 (5): 1254–1265

    Article  Google Scholar 

  • Wan Z. 1999. MODIS Land-Surface Temperature Algorithm Theoretical Basis Document (LST ATBD). Institute for Computational Earth System Science University of California, Santa Barbara, Version 3.3, April 1999

    Google Scholar 

  • Xue Y., Cracknell A. P. 1995. Advanced thermal inertia modelling, in International Journal of Remote Sensing, n.16 (3): 431–446

    Article  Google Scholar 

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Correspondence to Stefano Natali , Massimo Iannetta or Alberto Marini .

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Natali, S. et al. (2009). Estimating Soil Moisture Using Optical and Radar Satellite Remote Sensing Data. In: Marini, A., Talbi, M. (eds) Desertification and Risk Analysis Using High and Medium Resolution Satellite Data. NATO Science for Peace and Security Series C: Environmental Security. Springer, Dordrecht. https://doi.org/10.1007/978-1-4020-8937-4_9

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