Variable N management is one of the most promising practices of precision agriculture to optimize nitrogen-use efficiency (NUE) and decrease environmental impact of agriculture. The objective of this study was to test the performance of fertilization in winter wheat (Triticum aestivum L.) and triticale (Triticosecale Wittm.) determined by reflection measurements of on-the-go sensors under heterogeneous field conditions. In 2004 geo-referenced yield and N fertilization data were collected in four heterogeneous fields in southern Germany. Nitrogen demand of plants was determined throughout the growing season and the corresponding amount of N fertilizer was broadcast with the N-Sensor (Yara, Germany) in real-time. The sensor uses the red edge position (720-740 nm) as an indicator of crop N status and relates this to crop N demand. The sensor algorithm is designed to stimulate plant growth in areas with low biomass and reduce risk of lodging in areas with high biomass. Fertilization was evaluated by calculating site-specific N balance maps to delineate zones with N surplus in the soil. The results revealed some general limitations of this sensor approach in areas with yield-limiting factors other than N. Nitrogen surplus above 50 kg ha 21 was calculated for subfield areas dominated by shallow soils. The results of this study indicated that sensor-based measurements can be used efficiently for variable N application in cereal crops when N is the main growth-limiting factor. However, the causes for variability must be adequately understood before sensor-based variable rate fertilization can safely be used to optimize N side-dressing in cereals.

The Tasseled Cap Features, derived by the Tasseled Cap Transformation of the satellite spectral information, provide a way to consistently associate spectral information to biophysical characteristics of land surface features. Since currently there are no Tasseled Cap Coefficients available for RapidEye data, the goal of this study was to obtain Tasseled Cap Coefficients for the RapidEye sensors. As a result the Tasseled Cap Features Brightness, Greenness and Yellowness were derived. Brightness is a weighted sum of all bands and is aligned to the principal direction of soil brightness. Greenness contrasts the visual bands (including the Red Edge band) with the near infrared band, representing the spectral variation of vital vegetation. Yellowness contrast the Blue and Green bands with the Red, Red Edge and, to a lesser extent, NIR bands, and corresponds to the reflectance characteristics of dry, senescent crops. A transferability test of the Tasseled Cap Coefficients showed a successful application of the coefficients to other regions of the world, indicating a wider application potential.

Grasslands cover approximately 40% of the Earth's surface. Low-cost tools for inventory, management, and monitoring are needed because of their great expanse, diversity, and the importance for environmental processes. Remote sensing is a useful technique for providing accurate and reliable information for land use planning and large-scale grassland management. In the context of "GIO land" (Copernicus Initial Operations land program), which is currently contracted by the European Environment Agency, a high-resolution grassland layer of 39 European countries is being created with an overall classification accuracy of better than 80%. Since grassland canopy density, chlorophyll status, and ground cover (GC) are highly dynamic throughout the growing season, no unique spectral signature can be used to map grasslands. Therefore, it is necessary to use image time series to characterize the phenological dynamics of grasslands throughout the year in order to discriminate between grasslands and other vegetation with similar spectral responses. This paper describes an operational approach based on a multisensor concept that employs optical multitemporal and multiscale satellite imagery to generate the high-resolution panEuropean grassland layer. The approach is based on the supervised decision tree classifier C5.0 in combination with previous image segmentation and seasonal statistics for various vegetation indices (VIs). Results from the grassland classification for Hungary are presented. The accuracy assessment for this classification was carried out using 328 independent sample points derived from a ground-based European field survey program (LUCAS) and current CORINE Land Cover data. The grassland classification approach is explained in detail on the example of Hungary where an overall accuracy of 92.2% has been reached.

Crop classification greatly benefits from the analysis of multi-temporal Earth Observation (EO) data within a growing season utilizing the distinct phenological behavior of each crop. RapidEye's high repetition rate increases the chances of providing sufficient high resolution image time series offering new ways of classifying crops. This study introduces a supervised decision tree (DT) classification approach using image objects in combination with seasonal statistics of various vegetation indices (VI) for crop identification. The aim of this study is, first, to show the potential of VI seasonal statistics for crop identification, and secondly, to evaluate the relative contribution of each variable to the overall classification accuracy. The results presented in this paper correspond to an area of 625 km² in Saxony-Anhalt, Germany. The cultivated landscape is characterized by large agricultural fields, with winter wheat, canola, corn and winter barley as the main crops. Crop identification accuracies were assessed on the basis of reference fields and the importance of each employed variable is assessed by rule set analysis. The classification accuracy for the test area demonstrates that the proposed approach of multi-temporal image analysis provides spatially detailed and thematically accurate information on the crop type distribution.

ABSTRACT:Remote sensing is a suitable tool for estimating the spatial variability of crop canopy characteristics, such as canopy chlorophyll content (CCC) and green ground cover (GGC%), which are often used for crop productivity analysis and site-specific crop management. Empirical relationships exist between different vegetation indices (VI) and CCC and GGC% that allow spatial estimation of canopy characteristics from remote sensing imagery. However, the use of VIs is not suitable for an operational production of CCC and GGC% maps due to the limited transferability of derived empirical relationships to other regions. Thus, the operational value of crop status maps derived from remotely sensed data would be much higher if there was no need for reparametrization of the approach for different situations. This paper reports on the suitability of high-resolution RapidEye data for estimating crop development status of winter wheat over the growing season, and demonstrates two different approaches for mapping CCC and GGC%, which do not rely on empirical relationships. The final CCC map represents relative differences in CCC, which can be quickly calibrated to field specific conditions using SPAD chlorophyll meter readings at a few points. The prediction model is capable of predicting SPAD readings with an average accuracy of 77%. The GGC% map provides absolute values at any point in the field. A high R² value of 80% was obtained for the relationship between estimated and observed GGC%. The mean absolute error for each of the two acquisition dates was 5.3% and 8.7%, respectively.