TY - GEN
T1 - Irrigation Mapping Using Sentinel-1 and Sentinel-2 Data
AU - Zribi, Mehrez
AU - Elwan, Ehsan
AU - Page, Michel Le
AU - Jarlan, Lionel
AU - Brocca, Luca
AU - Modanesi, Sara
AU - Dari, Jacopo
AU - Segui, Pere Quintana
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - The main objective of this study is to develop an operational approach for mapping irrigated agricultural plots using Sentinel-1 (S1) and Sentinel-2 (S2) data. The application is carried out on two agricultural sites in Europe with two different climatic contexts. Different classifiers are identified to allow the separation between irrigated and rainfed areas. From the time series of S1 and S2 data and at two different scales, that of the agricultural plot and that of 5 km, we have proposed different statistical variables. The Support Vector Machine SVM classification method is used with different options to assess the potential of each variable. Results confirm the interest of using multi-sensor data and more than one scale for training. The best classification result is produced using mixed training data from both sites. In this case, an accuracy of 85% is achieved in the mapping of irrigated areas.
AB - The main objective of this study is to develop an operational approach for mapping irrigated agricultural plots using Sentinel-1 (S1) and Sentinel-2 (S2) data. The application is carried out on two agricultural sites in Europe with two different climatic contexts. Different classifiers are identified to allow the separation between irrigated and rainfed areas. From the time series of S1 and S2 data and at two different scales, that of the agricultural plot and that of 5 km, we have proposed different statistical variables. The Support Vector Machine SVM classification method is used with different options to assess the potential of each variable. Results confirm the interest of using multi-sensor data and more than one scale for training. The best classification result is produced using mixed training data from both sites. In this case, an accuracy of 85% is achieved in the mapping of irrigated areas.
KW - Sentinel-1
KW - Sentinel-2
KW - Support Vector Machine
KW - irrigation
UR - http://www.scopus.com/inward/record.url?scp=85134224947&partnerID=8YFLogxK
U2 - 10.1109/ATSIP55956.2022.9805877
DO - 10.1109/ATSIP55956.2022.9805877
M3 - Conference contribution
AN - SCOPUS:85134224947
T3 - International Conference on Advanced Technologies for Signal and Image Processing, ATSIP 2022
BT - International Conference on Advanced Technologies for Signal and Image Processing, ATSIP 2022
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 6th International Conference on Advanced Technologies for Signal and Image Processing, ATSIP 2022
Y2 - 24 May 2022 through 27 May 2022
ER -