The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Download
Publications Copernicus
Download
Citation
Articles | Volume XLII-3/W12-2020
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3/W12-2020, 291–296, 2020
https://doi.org/10.5194/isprs-archives-XLII-3-W12-2020-291-2020
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3/W12-2020, 291–296, 2020
https://doi.org/10.5194/isprs-archives-XLII-3-W12-2020-291-2020

  06 Nov 2020

06 Nov 2020

GOOGLE EARTH ENGINE: APPLICATION OF ALGORITHMS FOR REMOTE SENSING OF CROPS IN TUSCANY (ITALY)

J. P. Clemente1,2, G. Fontanelli3, G. G. Ovando1, Y. L. B. Roa2,4, A. Lapini3, and E. Santi3 J. P. Clemente et al.
  • 1Universidad Nacional de Córdoba. Facultad de Ciencias Agropecuarias, Córdoba, Argentina
  • 2Instituto de Altos Estudios Espaciales Mario Gulich (CONAE-UNC), Córdoba, Argentina
  • 3Institute of Applied Physics-National Research Council, Firenze, Italy
  • 4CONICET and Instituto CEDIAC, Universidad Nacional de Cuyo, Mendoza, Argentina

Keywords: Google Earth Engine, Support Vector Machine, Crop classification, Random Forest, Sentinel-1, Sentinel-2

Abstract. Remote sensing has become an important mean to assess crop areas, specially for the identification of crop types. Google Earth Engine (GEE) is a free platform that provides a large number of satellite images from different constellations. Moreover, GEE provides pixel-based classifiers, which are used for mapping agricultural areas. The objective of this work is to evaluate the performance of different classification algorithms such as Minimum Distance (MD), Random Forest (RF), Support Vector Machine (SVM), Classification and Regression Trees (CART) and Na¨ıve Bayes (NB) on an agricultural area in Tuscany (Italy). Four different scenarios were implemented in GEE combining different information such as optical and Synthetic Aperture Radar (SAR) data, indices and time series. Among the five classifiers used the best performers were RF and SVM. Integrating Sentinel-1 (S1) and Sentinel-2 (S2) slightly improves the classification in comparison to the only S2 image classifications. The use of time series substantially improves supervised classifications. The analysis carried out so far lays the foundation for the integration of time series of SAR and optical data.