Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B1, 101-107, 2016
http://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLI-B1/101/2016/
doi:10.5194/isprs-archives-XLI-B1-101-2016
 
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 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.
Conference paper (PDF, 710 KB)


Citation: Sánchez, N., Lopez-Sanchez, J. M., Arias-Pérez, B., Valcarce-Diñeiro, R., Martínez-Fernández, J., Calvo-Heras, J. M., Camps, A., González-Zamora, A., and Vicente-Guijalba, F.: NEW MICROWAVE-BASED MISSIONS APPLICATIONS FOR RAINFED CROPS CHARACTERIZATION, Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B1, 101-107, doi:10.5194/isprs-archives-XLI-B1-101-2016, 2016.

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