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@Article{OldoniPrDiWiSaGa:2020:PoSADa,
               author = "Oldoni, Lucas Volochen and Prudente, Victor Hugo Rohden and Diniz, 
                         Juliana Maria Ferreira de Souza and Wiederkehr, Nat{\'a}lia 
                         Cristina and Sanches, Ieda Del'Arco and Gama, F{\'a}bio Furlan",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto 
                         Nacional de Pesquisas Espaciais (INPE)} and {Instituto Nacional de 
                         Pesquisas Espaciais (INPE)} and {Instituto Nacional de Pesquisas 
                         Espaciais (INPE)} and {Instituto Nacional de Pesquisas Espaciais 
                         (INPE)} and {Instituto Nacional de Pesquisas Espaciais (INPE)}",
                title = "Polarimetric SAR data from sentinel-1a applied to early crop 
                         classification",
              journal = "International Archives of the Photogrammetry, Remote Sensing and 
                         Spatial Information Sciences - ISPRS Archives",
                 year = "2020",
               volume = "43",
               number = "B3",
                pages = "1039--1046",
                month = "Aug.",
                 note = "2020 24th ISPRS Congress - Technical Commission III; Nice, 
                         Virtual; France; 31 August 2020 through 2 September 2020",
             keywords = "Agriculture monitoring, Remote Sensing, Microwave, Soybean, Early 
                         classification, Machine learning.",
             abstract = "This paper aims to map crops in two Brazilian municipalities, 
                         Lu{\'{\i}}s Eduardo Magalh{\~a}es (LEM) and Campo Verde, using 
                         dualpolarimetric Sentinel-1A images. The specific objectives were: 
                         (1) to evaluate the accuracy gain in the crop classification using 
                         Sentinel-1A multitemporal data backscatter coefficients and ratio 
                         (\σ0VH, \σ0VV and, \σ0VH/\σ0VV, denominate 
                         BS group) in comparison to the addition of polarimetric attributes 
                         (\σ0VH, \σ0VV, \σ 0VH/\σ0VV, H, and 
                         \α, denominate BP group) and; (2) to assess the accuracy 
                         gain in the earliest crop classification, creating new scenarios 
                         with the addition of the new SAR data together with the previous 
                         images for each date and group (BS and BP) during the crop 
                         development. For BS and BP groups, 13 e 10 scenarios were analyzed 
                         in LEM and Campo Verde, respectively. For the classification 
                         process, we used the Random Forest (RF) algorithm. In the LEM 
                         site, the best results for BS and BP groups were equivalent 
                         (overall accuracy: ~82%), while for the Campo Verde site, the 
                         classification accuracy for the BP group (overall accuracy: ~80%) 
                         was 2% higher than the BS group. The addition of new images during 
                         the crop development period increased the earliest crop 
                         classification overall accuracy, stabilizing from mid-February in 
                         LEM and mid-December in Campo Verde, after 10 and 8 images, 
                         respectively. After these periods, the gain in classification 
                         accuracy was small with the addition of new images. In general, 
                         our results suggest the backscattering coefficients and 
                         polarimetric attributes extracted from the Sentinel-1A imagery 
                         exhibited a great performance to discriminate croplands.",
                  doi = "10.5194/isprs-archives-XLIII-B3-2020-1039-2020",
                  url = "http://dx.doi.org/10.5194/isprs-archives-XLIII-B3-2020-1039-2020",
                 issn = "0256-1840",
             language = "en",
           targetfile = "oldoni_polarimetric.pdf",
        urlaccessdate = "22 jan. 2021"
}


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