@InProceedings{DoblasCarShiSanAra:2020:AsRaIn,
               author = "Doblas, Juan Prieto and Carneiro, Arian Ferreira and Shimabukuro, 
                         Yosio Edemir and Sant'Anna, Sidnei Jo{\~a}o Siqueira and 
                         Arag{\~a}o, Luiz Eduardo Oliveira e Cruz de",
          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)}",
                title = "Assessment of rainfall influence on sentinel-1 time series on 
                         amazonian tropical forests aiming deforestation detection 
                         improvement",
            booktitle = "Proceedings...",
                 year = "2020",
                pages = "397--402",
         organization = "IEEE Latin American GRSS; ISPRS Remote Sensing Conference",
            publisher = "IEEE",
             keywords = "Sentinel-1, Time series, Change Detection, Rainfall Influence, 
                         Forests, Amazon.",
             abstract = "This work aims to determinate the relationship between C-band SAR 
                         backscattering measurements over Amazonian tropical forests and 
                         hourly precipitation rates, and to study the feasibility of a 
                         SAR-anomaly masking method based on orbital rain measurements. To 
                         do so, a comprehensive dataset of ESAs Sentinel-1 backscattering 
                         data and the concomitant GPM-IMERG precipitation data was 
                         collected and analysed. Backscattering anomalies were 
                         characterized in a statistically meaningful way. GAM models were 
                         then adjusted to the backscatter-rain data pairs. The computed 
                         models show a positive correlation between non-anomalous 
                         backscattering values and accumulated rain, of approximately 0,2 
                         dB/mm·h-1 and 0,4 dB/mm·h-1 for VV and VH polarizations. Negative 
                         anomalies, which can easily mislead deforestation algorithms, have 
                         a strong negative correlation with rain rate observed at the time 
                         of the SAR acquisition. This is especially true for VV 
                         measurements. The subsequent anomaly masking procedure, based on 
                         computed accumulated and hourly rain thresholding, yielded 
                         unsatisfactory results. These poor results are probably due to the 
                         coarse resolution of the 0.1° GPM-IMERG data, which is 
                         insufficient to track anomaly-generating atmospheric events such 
                         as storm rain cells. Rainrelated changes in SAR backscattering can 
                         compromise deforestation detection algorithms, and further 
                         research and sensor developing is needed to increase spatial 
                         resolution of precipitation measures, to reach an optimal 
                         backscattering anomaly screening.",
  conference-location = "Santiago, Chile",
      conference-year = "21-26 Mar.",
                  doi = "10.1109/LAGIRS48042.2020.9165637",
                  url = "http://dx.doi.org/10.1109/LAGIRS48042.2020.9165637",
                 isbn = "978-172814350-7",
             language = "en",
           targetfile = "doblas_assessment.pdf",
        urlaccessdate = "24 abr. 2024"
}