The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XL-7/W3
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-7/W3, 1299–1303, 2015
https://doi.org/10.5194/isprsarchives-XL-7-W3-1299-2015
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-7/W3, 1299–1303, 2015
https://doi.org/10.5194/isprsarchives-XL-7-W3-1299-2015

  30 Apr 2015

30 Apr 2015

MULTISENSOR EXPERIMENTS OVER VINEYARD: NEW CHALLENGES FOR THE GNSS-R TECHNIQUE

N. Sánchez1, A. Alonso-Arroyo2, J. Martínez-Fernández1, A. Camps2, A. González-Zamora1, M. Pablos2, C. M. Herrero-Jiménez1, and A. Gumuzzio1 N. Sánchez et al.
  • 1Universidad de Salamanca/CIALE, Duero 12, 37185 Villamayor, Spain
  • 2Universitat Politècnica de Catalunya – BarcelonaTech and IEEC/UPC, Jordi Girona 1-3, 08034 Barcelona, Spain

Keywords: GNSS-R, Landsat, airborne, soil moisture, multispectral, thermal, vineyard

Abstract. An airborne campaign was performed during August, 2014 in an agricultural area in the Duero basin (Spain) in order to appraise the synergy between very different sources of Earth Observation imagery, and very different instruments for soil moisture retrieval. During the flight, an intensive field campaign comprising soil, plant and spectral measurements was carried out. An innovative sensor based on the Global Navigation Satellite Systems Reflectometry (GNSS-R) was on board the manned vehicle, the Light Airborne Reflectometer for GNSS-R Observations (LARGO) engineered by the Universitat Politècnica de Catalunya. While the synergy between thermal, optical and passive microwave spectra observations is well known for vegetation parameters and soil moisture retrievals, the experiment aimed to evaluate the synergy of GNSS-R reflectivity with a time-collocated Landsat 8 imagery for soil moisture retrieval under semiarid climatic conditions. LARGO estimates, field measurements, and optical, NIR, SWIR and thermal bands from Landsat 8 were compared. Results showed that the joint use of GNSS-R reflectivity with vegetation and water indices together with thermal maps from Landsat 8 thoroughly improved the soil moisture estimation.