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@Article{EberhardtSRSFAMITFL:2016:ClCoAs,
               author = "Eberhardt, Isaque Daniel Rocha and Schultz, Bruno and Rizzi, 
                         Rodrigo and Sanches, Ieda Del'Arco and Formaggio, Antonio Roberto 
                         and Atzberger, Clement and Mello, Marcio Pupin and Immitzer, 
                         Markus and Trabaquini, Kleber and Foschiera, William and Luiz, 
                         Alfredo Jos{\'e} Barreto",
          affiliation = "{Universidade de Bras{\'{\i}}lia (UNB)} and {Instituto Nacional 
                         de Pesquisas Espaciais (INPE)} and {Universidade Federal de 
                         Pelotas (UFPel)} and {Instituto Nacional de Pesquisas Espaciais 
                         (INPE)} and {Instituto Nacional de Pesquisas Espaciais (INPE)} and 
                         {University of Natural Resources and Life Sciences} and {The 
                         Boeing Company} and {University of Natural Resources and Life 
                         Sciences} and {Agricultural Research and Rural Extension Company 
                         of Santa Catarina (Epagri)} and {Instituto Nacional de Pesquisas 
                         Espaciais (INPE)} and {Embrapa Meio Ambiente}",
                title = "Cloud cover assessment for operational crop monitoring systems in 
                         tropical areas",
              journal = "Remote Sensing",
                 year = "2016",
               volume = "8",
               number = "3",
                pages = "219",
             keywords = "Agriculture monitoring, Clear sky coverage, Crop classification, 
                         MODIS.",
             abstract = "The potential of optical remote sensing data to identify, map and 
                         monitor croplands is well recognized. However, clouds strongly 
                         limit the usefulness of optical imagery for these applications. 
                         This paper aims at assessing cloud cover conditions over four 
                         states in the tropical and sub-tropical Center-South region of 
                         Brazil to guide the development of an appropriate agricultural 
                         monitoring system based on Landsat-like imagery. Cloudiness was 
                         assessed during overlapping four months periods to match the 
                         typical length of crop cycles in the study area. The percentage of 
                         clear sky occurrence was computed from the 1 km resolution MODIS 
                         Cloud Mask product (MOD35) considering 14 years of data between 
                         July 2000 and June 2014. Results showed high seasonality of cloud 
                         occurrence within the crop year with strong variations across the 
                         study area. The maximum seasonality was observed for the two 
                         states in the northern part of the study area (i.e., the ones 
                         closer to the Equator line), which also presented the lowest 
                         averaged values (15%) of clear sky occurrence during the main 
                         (summer) cropping period (November to February). In these 
                         locations, optical data faces severe constraints for mapping 
                         summer crops. On the other hand, relatively favorable conditions 
                         were found in the southern part of the study region. In the South, 
                         clear sky values of around 45% were found and no significant clear 
                         sky seasonality was observed. Results underpin the challenges to 
                         implement an operational crop monitoring system based solely on 
                         optical remote sensing imagery in tropical and sub-tropical 
                         regions, in particular if short-cycle crops have to be monitored 
                         during the cloudy summer months. To cope with cloudiness issues, 
                         we recommend the use of new systems with higher repetition rates 
                         such as Sentinel-2. For local studies, Unmanned Aircraft Vehicles 
                         (UAVs) might be used to augment the observing capability. 
                         Multi-sensor approaches combining optical and microwave data can 
                         be another option. In cases where wall-to-wall maps are not 
                         mandatory, statistical sampling approaches might also be a 
                         suitable alternative for obtaining useful crop area information.",
                  doi = "10.3390/rs8030219",
                  url = "http://dx.doi.org/10.3390/rs8030219",
                 issn = "2072-4292",
             language = "en",
           targetfile = "eberhardt_cloud.pdf",
        urlaccessdate = "27 nov. 2020"
}


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