Maximum Fraction Images Derived from Year-Based Project for On-Board Autonomy-Vegetation (PROBA-V) Data for the Rapid Assessment of Land Use and Land Cover Areas in Mato Grosso State, Brazil  
[PERSON]  
1 National Institute for Space Research (INPE), Av. dos Astronauitas, 1758, CEP: 12.227-010 Sao Jose dos Campos, SP, Brazil; [EMAIL_ADDRESS] (E.A. ); [EMAIL_ADDRESS] (A.C.D. ); [EMAIL_ADDRESS] (T.B.H. ); [EMAIL_ADDRESS] (Y.E.S.) 2 Embrapa Cerrados, BR-020, km 18, CEP: 73301-970 Planaltina, DF, Brazil; [EMAIL_ADDRESS] 2  
S. G. A. G.  
Rondonia, and Amazonas, as well as the newest agricultural frontier in Brazil, the MATOPIBA region (states of Maranhao, Tocantins, Piaui, and Bahia) [5]. The negative outcomes of this large expansion in food production are habitat fragmentation, biodiversity reduction, and high pressure on protected areas (conservation units and indigenous lands) [6; 7]. Therefore, a quick assessment of LULC changes in all the above-mentioned regions is important for forecasting yield, planning logistics (transportation and storage), preventing fire, and combating and controlling illegal deforestation [6; 8; 9]. LULC monitoring is also crucial to estimate CO\({}_{2}\) emissions from deforestation and degradation losses [10; 11], as well as to prevent soil erosion, the introduction of invasive species, and resource losses such as timber and water supply for local inhabitants [12; 13]. Various moderate spatial resolution (\(\sim\)30 m) remote sensing data have been widely applied to map and monitor LULC changes at the global scale. These are the cases of China's GlobeLand30 [14] and the University of Maryland's Global Land Analysis and Discovery (GLAD) [15]. Recently, a pool of Brazilian universities and nongovernment and private institutions launched the MapBiomas project [16] to produce year-based LULC maps of Brazil using cloud-computing resources and historical series of Landsat data available in the Google Earth Engine platform. These initiatives use remote sensing data with moderate spatial and temporal resolutions, that is, Landsat-like satellites, typically with 16-day repeat passes and 30-m spatial resolution. Especially in tropical regions, there is well-known persistent cloud coverage. For example, in Brazil, only 20% of its territory presents more than 12 cloud-free Landsat observations in a year (within a total of 23 scenes) [17]. The limited number of Landsat scenes is crucial not only in the Amazonia biome but also in the Cerrado biome because of its high climatic seasonality and the presence of a six-month wet season (from October to March) [18], in which most of the grain production is concentrated. Thus, at a 16-day temporal resolution, Landsat images may not be sufficient to fully explore the variations in vegetation phenology and the temporal patterns of agricultural lands due to cloud cover constraints [19]. In this sense, satellite sensors with relatively coarse spatial resolution (\(\sim\)100-300 m) and high temporal resolution may be a singular alternative to monitor LULC changes in regions such as Mato Grosso state in Brazil. These sensors include the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor onboard the Terra and Aqua platforms, with spatial resolutions of 250, 500, and 1000 m and 1-2 days of repeat pass [20]; the Medium Resolution Imaging Spectrometer (MERIS) sensor onboard the Envisat platform with a spatial resolution of 300 m and three-day repeat pass [21]; and the Project for On-Board Autonomy-Vegetation (PROBA-V), with 100-m or 1-km spatial resolutions and daily observations [22]. Recently, the combination of sensors, such as Landsat and Sentinel-2 [23; 24], has been a good alternative to increase the availability of temporal images, allowing an improved characterization of land surfaces. However, so far, application of this sensor combination has been limited in the detection of burned areas [24] or in the monitoring of agricultural crop cycles [23].