Volume XLI-B1
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B1, 101-107, 2016
https://doi.org/10.5194/isprs-archives-XLI-B1-101-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B1, 101-107, 2016
https://doi.org/10.5194/isprs-archives-XLI-B1-101-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.

  02 Jun 2016

02 Jun 2016

NEW MICROWAVE-BASED MISSIONS APPLICATIONS FOR RAINFED CROPS CHARACTERIZATION

N. Sánchez1,2, J. M. Lopez-Sanchez3, B. Arias-Pérez2, R. Valcarce-Diñeiro2, J. Martínez-Fernández1, J. M. Calvo-Heras1, A. Camps4, A. González-Zamora1, and F. Vicente-Guijalba3 N. Sánchez et al.
  • 1CIALE, Universidad de Salamanca, Duero 12, 37185 Villamayor, Salamanca, Spain
  • 2Departamento de Ingeniería Cartográfica y del Terreno, Hornos Caleros 50, 05003 Ávila, Spain
  • 3IUII, Universidad de Alicante, P.O. Box 99, E-03080 Alicante, Spain
  • 4Universitat Politècnica de Catalunya–BarcelonaTech, Department of Signal Theory and Communications (TSC), Jordi Girona 1-3, 08034 Barcelona, Spain

Keywords: Radar, crops, Landsat 8, Radarsat-2, GNSS-R

Abstract. A multi-temporal/multi-sensor field experiment was conducted within the Soil Moisture Measurement Stations Network of the University of Salamanca (REMEDHUS) in Spain, in order to retrieve useful information from satellite Synthetic Aperture Radar (SAR) and upcoming Global Navigation Satellite Systems Reflectometry (GNSS-R) missions. The objective of the experiment was first to identify which radar observables are most sensitive to the development of crops, and then to define which crop parameters the most affect the radar signal. A wide set of radar variables (backscattering coefficients and polarimetric indicators) acquired by Radarsat-2 were analyzed and then exploited to determine variables characterizing the crops. Field measurements were fortnightly taken at seven cereals plots between February and July, 2015. This work also tried to optimize the crop characterization through Landsat-8 estimations, testing and validating parameters such as the leaf area index, the fraction of vegetation cover and the vegetation water content, among others. Some of these parameters showed significant and relevant correlation with the Landsat-derived Normalized Difference Vegetation Index (R>0.60). Regarding the radar observables, the parameters the best characterized were biomass and height, which may be explored for inversion using SAR data as an input. Moreover, the differences in the correlations found for the different crops under study types suggested a way to a feasible classification of crops.