Vegetation Fraction Images Derived from PROBA-V Data for Rapid Assessment of Annual Croplands in Brazil

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.

Discriminating Land Use and Land Cover Classes in Brazil Based on the Annual PROBA-V 100 m Time Series

“…In this step, obtaining the pure endmembers is critical and requires a visual inspection of the pixel references. The collection of pure endmembers of PROBA-V in Brazil was based on spectral reference values obtained by Arai et al [23] (see Table I).…”

“…The images are made available in surface reflectance so that they can be transformed in synthetic images, such as vegetation indices or fraction images derived from the linear spectral mixing model (LSMM). The PROBA-V 100 m data have been used for the LULC classification of the semi-arid Africa [21], crop area mapping of globally spread agricultural areas in Belgium, Russia, Ukraine, and Brazil [22], and crop mapping of Mato Grosso state, Brazil [23] among other applications.…”

Modelo Linear de Mistura Espectral: Conceitos Teóricos, Algoritmos e Aplicações em Estudos na Amazônia Legal

“…As imagens fração de vegetação, solo e fração sombra/água foram usadas para mapear o uso e cobertura da terra em escala nacional (SHIMABUKURO et al, 2020;ARAI et al, 2020) e em escala regional (CASSOL et al, 2020). A Figura 8 representa a ideia conceitual utilizada no mapeamento da agricultura no estado do Mato Grosso usando dados multitemporais do satélite PROBA-V aplicando a metodologia de máxima proporção das imagens fração obtidas na série temporal de um ano (ARAI et al, 2020).…”

“…As imagens fração de vegetação, solo e fração sombra/água foram usadas para mapear o uso e cobertura da terra em escala nacional (SHIMABUKURO et al, 2020;ARAI et al, 2020) e em escala regional (CASSOL et al, 2020). A Figura 8 representa a ideia conceitual utilizada no mapeamento da agricultura no estado do Mato Grosso usando dados multitemporais do satélite PROBA-V aplicando a metodologia de máxima proporção das imagens fração obtidas na série temporal de um ano (ARAI et al, 2020). Nesse exemplo, considerando a série temporal de, aproximadamente, 288 imagens (72 cenas x quatro bandas espectrais) provenientes do satélite utilizado no estudo, a aplicação do MLME permitiu a identificação das áreas destinadas à agricultura devido à sua caracterização por áreas homogêneas e por um padrão cíclico entre solo exposto (colheita e preparo do solo) e vegetação.…”

“…Algumas das aplicações do MLME utilizando as imagens fração derivadas pontualmente no tempo e em escalas local e regional foram explicitadas aqui nesse trabalho. Recentemente, alguns trabalhos têm demonstrado a aplicabilidade do MLME em discriminar diferentes alvos da superfície terrestre em escala nacional (SHIMABUKURO et al, 2020) e global (ADAMI et al, 2018; e, ainda mais recentemente, alguns trabalhos que evidenciam a aplicabilidade do MLME em análises que utilizam as imagens frações derivadas de imagens intranual (ARAI et al, 2020;CASSOL et al, 2020) ou interanual (DUTRA, 2019).…”