Soybean crop area estimation by Modis/Evi data

Soybean Crop Area Estimation and Mapping in Mato Grosso State, Brazil

Evaluation of the MODIS Crop Detection Algorithm (MCDA) procedure for estimating historical planted soybean crop areas was done on fields in Mato Grosso State, Brazil. MCDA is based on temporal profiles of EVI (Enhanced Vegetation Index) derived from satellite data of the MODIS (Moderate Resolution Imaging Spectroradiometer) imager, and was previously developed for soybean area estimation in Ri...

Crop area estimation by data-driven decomposition of mixed pixels in Landsat-TM images

Pixel-based classiication is a standard approach towards crop area estimation, but it is unsuited to handle mixed pixels. Therefore, we developed the data-driven decomposition method, which combines the linear mixture model with knowledge of the application domain. A quantitative comparison proved that both concepts lead to more accurate estimates when processing simulated images. Experiments o...

Research on Estimation Crop Planting Area by Integrating the Optical and Microwave Remote Sensing Data

Considering the problem in monitoring agricultural condition in the semi-arid areas of Northwest of China, we propose a new method for estimation of crop planting area, using the single phase optical and microwave remote sensing data collaboratively, which have demonstrated their respective advantages in the extraction of surface features. In the model, the ASAR backscatter coefficient is norma...

Application of Data-Driven Decomposition to Landsat-TM Images for Crop Area Estimation

An accurate crop area estimation method based on satellite remote sensing imagery is needed to manage the agricultural subsidy system of the European Union. The area estima-tor can use either classiication, which allocates a pixel to a single class, or decomposition, which divides a pixel between several classes, to determine the ground cover type(s) a pixel is composed of. While in early days ...

Winter wheat area estimation from MODIS-EVI time series data using the Crop Proportion Phenology Index