Abstract
This paper presents a new method for rapid assessment of the extent of annual croplands in Brazil. The proposed method applies a linear spectral mixing model (LSMM) to PROBA-V time series images to derive vegetation, soil, and shade fraction images for regional analysis. We used S10-TOC (10 days synthesis, 1 km spatial resolution, and top-of-canopy) products for Brazil and S5-TOC (five days synthesis, 100 m spatial resolution, and top-of-canopy) products for Mato Grosso State (Brazilian Legal Amazon). Using the time series of the vegetation fraction images of the whole year (2015 in this case), only one mosaic composed with maximum values of vegetation fraction was generated, allowing detecting and mapping semi-automatically the areas occupied by annual crops during the year. The results (100 m spatial resolution map) for the Mato Grosso State were compared with existing global datasets (Finer Resolution Observation and Monitoring—Global Land Cover (FROM-GLC) and Global Food Security—Support Analyses Data (GFSAD30)). Visually those maps present a good agreement, but the area estimated are not comparable since the agricultural class definition are different for those maps. In addition, we found 11.8 million ha of agricultural areas in the entire Brazilian territory. The area estimation for the Mato Grosso State was 3.4 million ha for 1 km dataset and 5.3 million ha for 100 m dataset. This difference is due to the spatial resolution of the PROBA-V datasets used. A coefficient of determination of 0.82 was found between PROBA-V 100 m and Landsat-8 OLI area estimations for the Mato Grosso State. Therefore, the proposed method is suitable for detecting and mapping annual croplands distribution operationally using PROBA-V datasets for regional analysis.
Highlights
Information on the extent, distribution, and dynamics of croplands have been identified as key issues for food security scenarios and economic and policy implications [1,2,3]
Research has been conducted on mapping croplands [11] and other land use and land cover (LULC) classes in Brazil at a national scale, as the MapBiomas project [12], or even at a local scale [7,13,14], or at global scale as the Finer Resolution Observation and Monitoring of Global Land Cover (FROM–GLC) [15] and the Global Food Security Support Analysis Data (GFSAD30) [16] provided at certain years
Taking advantage of the temporal resolution of PROBA-V satellite, the objective of this paper is to present a new method based on PROBA-V fraction images derived from linear spectral mixing model (LSMM) to assess the spatial distribution of annual croplands in Brazil and in Mato Grosso State rapidly
Summary
Information on the extent, distribution, and dynamics of croplands have been identified as key issues for food security scenarios and economic and policy implications [1,2,3]. Research has been conducted on mapping croplands [11] and other land use and land cover (LULC) classes in Brazil at a national scale, as the MapBiomas project [12], or even at a local scale [7,13,14], or at global scale as the Finer Resolution Observation and Monitoring of Global Land Cover (FROM–GLC) [15] and the Global Food Security Support Analysis Data (GFSAD30) [16] provided at certain years Most of these studies have used a combination of either medium spatial resolution and relatively long re-visiting capability satellite data (e.g., Landsat satellite) or moderate spatial resolution and high temporal resolution imageries [e.g., moderate resolution imaging spectroradiometer (MODIS) sensor onboard Terra and Aqua platforms]
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