Crop type identification

Crop type identification and mapping has a number of important aspects. It can serve for production statistics together with yield prediction, mapping soil productivity, assessment of crop damage and monitoring of farming activities. These include identifying the crop types (winter and spring cereals, rapeseed, sugar beet, potato, maize, grass, etc.) and delineating their parcel extent. Crop type identification is often based on multitemporal, multispectral high resolution imagery, while parcel boundaries delineation is more often based on very-high resolution imagery.
Traditional methods of obtaining this information are statistical estimates or ground surveying. Remote sensing offers an efficient means of collecting the information, in order to map crop type and acreage. Remote sensing can also provide state information about the health of the vegetation. The spectral reflections vary with respect to changes in the phenology and crop health. This can be measured and monitored by multispectral sensors. Interpretations from remotely sensed data and geographic information system (GIS) combined with ancillary data provide information of ownership, management practices, etc. This approach can be used e.g. in agricultural subsidy controls.
Crop type identification and mapping is based on use of multitemporal imagery to enhance the classification by taking into account changes in reflectance as a function of plant phenology. This in turn requires calibrated sensors and frequent repeat imaging throughout the vegetation season.

Thematic services:

• crop maps
• map of crop canopy health
• statistical figures of crop acreages
  

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Crop growth development monitoring

Crop growth development monitoring by means of remote sensing and simulation models give main indicators for quantitative yield predictions. Remote sensing data (e.g. middle resolution) with daily acquisitions serve as a main source of vegetation indicators. Often NDVI (Normalized Differential Vegetation Index), DMP (Dry Matter Production) and other indicators are calculated in time series (daily or decadal composites) to monitor crop growth. Detailed spatial and time analysis results in yield predictions. Crop growth and soil water mechanistic simulation models can be employed in order to receive complementary information on crop growth development. These indicators provide information on potential and limited crop growth given a local meteorological and soil conditions calculated for different parameterized crop types. Assimilation of remote sensing parameters into simulation models enhances the analysed crop indicators. Prediction of drought can be based on both, purely remote sensing measurements but also on employing simulation models.
Final quantitative yield prediction can be also based on both sources of crop indicators. The scale of the output statistics depend upon the scale of input data (remote sensing images, meteorological data, soil maps, etc.) but also more notably on scale of collected reference measured yields. Yield prediction together with acreage estimates is used for production statistics.

Thematic services:

• maps of vegetation indicators
• analysis of vegetation development
• drought monitoring
• crop yield prediction (national / regional)
  

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Precision Farming

Precision farming (or agriculture) is a new technology that allows farmers to adjust for within-field variability in a number of characteristics like soil fertility and weed populations. The use of global positioning system (GPS) to identify position within the field in the real time is one of the technological key elements. With the information of the position and on-board sensors, farm equipment can guide applications of crop inputs like fertilizers and herbicides or monitor crop yields. Precision farming has the potential to reduce costs through more efficient and effective applications of crop inputs. It can also reduce environmental impacts by allowing farmers to apply inputs only where they are needed at the appropriate rate.
Remote sensing is one of the main sources of the information on the crop and soil spatial variation with the fields. Several biophysical variables can be derived from RS images to assess degree of spatial variation as well as producing categorical maps separating parts of the fields according to the crop status.

Thematic services:

• segmentation of vegetation cover (airborne and satellite images)
• segmentation of yield maps
• application maps preprocessing
• geostatistical analysis of spatial variation
• sampling optimisation
  

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