Abstract
This study aimed to propose methods to identify croplands cultivated with winter cereals in the northern region of Rio Grande do Sul State, Brazil. Thus, temporal profiles of Normalized Difference Vegetation Index (NDVI) from MODIS sensor, from April to December of the 2000 to 2008, were analyzed. Firstly, crop masks were elaborated by subtracting the minimum NDVI image (April to May) from the maximum NDVI image (June to October). Then, an unsupervised classification of NDVI images was carried out (Isodata), considering the crop mask areas. According to the results, crop masks allowed the identification of pixels with greatest green biomass variation. This variation might be associated or not with winter cereals areas established to grain production. The unsupervised classification generated classes in which NDVI temporal profiles were associated with water bodies, pastures, winter cereals for grain production and for soil cover. Temporal NDVI profiles of the class winter cereals for grain production were in agree with crop patterns in the region (developmental stage, management standard and sowing dates). Therefore, unsupervised classification based on crop masks allows distinguishing and monitoring winter cereal crops, which were similar in terms of morphology and phenology.
Highlights
Information collected from surface and orbital remote sensors have emerged as an important and promising data source for agriculture
In the case of MODIS sensor (Moderate Resolution Imaging Spectroradiometer), that is the key instrument aboard the Terra and Aqua satellites, vegetation indexes are available in the form of ready product named MOD13Q1, which included Normalized Difference Vegetation Index (NDVI) and EVI (Enhanced Vegetation Index) images (MODIS, 2012)
The aim of this work was to propose methods for discrimination, in NDVI/MODIS images, of agricultural areas cultivated with winter cereals for grain production, in relation to other established areas during the autumn-winter-spring period in the northern region of Rio Grande do Sul state
Summary
Information collected from surface and orbital remote sensors have emerged as an important and promising data source for agriculture. In order to make this information available to be used to monitor agricultural crops and estimation of cultivated areas and yield, it is necessary to establish the relationship between radiometric parameters contained in remote sensing products, and biophysical parameters of vegetation. In various studies these relationships have been established through vegetation index (EPIPHANIO et al, 1996). The NDVI/MODIS images are available free of charge to the end user, with atmospheric and geometric corrections previously performed (RIZZI, 2004), with spatial resolution of 250 meters and time resolution of 16 days, both relevant for monitoring crops at regional scale
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