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Assessing the optimal preprocessing steps of MODIS time series to map cropping systems in Mato Grosso, Brazil.

, , , , , and . Int. J. Appl. Earth Obs. Geoinformation, (2020)

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Remote Sensing and Cropping Practices: A Review., , , , , , , , , and . Remote. Sens., 10 (1): 99 (2018)Deforestation Patterns in the Southern Brazilian Amazon Watersheds., , , , , , , and . IGARSS, page 5808-5811. IEEE, (2022)Assessing the optimal preprocessing steps of MODIS time series to map cropping systems in Mato Grosso, Brazil., , , , , and . Int. J. Appl. Earth Obs. Geoinformation, (2020)Addendum: Arvor, D. et al. Monitoring Rainfall Patterns in the Southern Amazon with PERSIANN-CDR Data: Long-Term Characteristics and Trends. Remote Sens. 2017, 9, 889., , , and . Remote Sensing, 10 (1): 128 (2018)Performance of TRMM TMPA 3B42 V7 in Replicating Daily Rainfall and Regional Rainfall Regimes in the Amazon Basin (1998-2013)., , , , , , and . Remote. Sens., 10 (12): 1879 (2018)Monitoring land use changes around the indigenous lands of the Xingu basin in Mato Grosso, Brazil., , , , , , , and . IGARSS, page 3190-3193. IEEE, (2010)Monitoring the vulnerability of soybean to heat waves and their impacts in Mato Grosso state, Brazil., , , , and . IGARSS, page 859-862. IEEE, (2014)Assessing the Causes of Tropical Forest Degradation Using Landsat Time Series: A Case Study in the Brazilian Amazon., , , , , , , , , and 3 other author(s). IGARSS, page 1015-1018. IEEE, (2021)Comparison of Multitemporal MODIS-EVI Smoothing Algorithms and its Contribution to Crop Monitoring., , , , and . IGARSS (2), page 958-961. IEEE, (2008)Mapping Grassland Frequency Using Decadal MODIS 250 m Time-Series: Towards a National Inventory of Semi-Natural Grasslands., , , , , , and . Remote. Sens., 11 (24): 3041 (2019)