Originally published as:Förster, S., Kaden, K., Foerster, M., Itzerott, S. (2012) Spatially explicit multi-year crop information is required for many environmental applica-17 tions. The study presented here proposes a hierarchical classification approach for per-plot 18 crop type identification that is based on spectral-temporal profiles and accounts for deviations 19 from the average growth stage timings by incorporating agro-meteorological information in 20 the classification process. It is based on the fact that each crop type has a distinct seasonal 21 spectral behaviour and that the weather may accelerate or delay crop development. The classi-22 fication approach was applied to map twelve crop types in a 14 000 km² catchment area in 23Northeast Germany for several consecutive years. An accuracy assessment was performed 24 and compared to those of a maximum likelihood classification. The 7.1 % lower overall clas-25 2 sification accuracy of the spectral-temporal profiles approach may be justified by its inde-26 pendence of ground truth data. The results suggest that the number and timing of image ac-27 quisition is crucial to distinguish crop types. The increasing availability of optical imagery 28 offering a high temporal coverage and a spatial resolution suitable for per-plot crop type map-29 ping will facilitate the continuous refining of the spectral-temporal profiles for common crop 30 types and different agro-regions and is expected to improve the classification accuracy of crop 31 type maps using these profiles. 32 33

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In light of the increasing demand for food production, climate change challenges for agriculture, and economic pressure, precision farming is an ever-growing market. The development and distribution of remote sensing applications is also growing. The availability of extensive spatial and temporal data-enhanced by satellite remote sensing and open-source policies-provides an attractive opportunity to collect, analyze and use agricultural data at the farm scale and beyond. The division of individual fields into zones of differing yield potential (management zones (MZ)) is the basis of most offline and mapoverlay precision farming applications. In the process of delineation, manual labor is often required for the acquisition of suitable images and additional information on crop type. The authors therefore developed an automatic segmentation algorithm using multi-spectral satellite data, which is able to map stable crop growing patterns, reflecting areas of relative yield expectations within a field. The algorithm, using RapidEye data, is a quick and probably low-cost opportunity to divide agricultural fields into MZ, especially when yield data is insufficient or non-existent. With the increasing availability of satellite images, this method can address numerous users in agriculture and lower the threshold of implementing precision farming practices by providing a preliminary spatial field assessment.

Abstract:The monitoring of ecosystems alterations has become a crucial task in order to develop valuable habitats for rare and threatened species. The information extracted from hyperspectral remote sensing data enables the generation of highly spatially resolved analyses of such species' habitats. In our study we combine information from a species ordination with hyperspectral reflectance signatures to predict occurrence probabilities for Natura 2000 habitat types and their conservation status. We examine how accurate habitat types and habitat threat, expressed by pressure indicators, can be described in an ordination space using spatial correlation functions from the geostatistic approach. We modeled habitat quality assessment parameters using floristic gradients derived by non-metric multidimensional scaling on the basis of 58 field plots. In the resulting ordination space, the variance structure of habitat types and pressure indicators could be explained by 69% up to 95% with fitted variogram models with a correlation to terrestrial mapping of >0.8. OPEN ACCESSRemote Sens. 2015, 7 2872Models could be used to predict habitat type probability, habitat transition, and pressure indicators continuously over the whole ordination space. Finally, partial least squares regression (PLSR) was used to relate spectral information from AISA DUAL imagery to floristic pattern and related habitat quality. In general, spectral transferability is supported by strong correlation to ordination axes scores (R 2 = 0.79-0.85), whereas second axis of dry heaths (R 2 = 0.13) and first axis for pioneer grasslands (R 2 = 0.49) are more difficult to describe.

a b s t r a c tThis research proposes a new model for the generation of basic soil information maps for precision agriculture based on multitemporal remote sensing data analysis and GIS spatial data modelling. It demonstrates (i) the potential of multitemporal soil pattern analysis (ii) to generate functional soil maps at field scale based on soil reflectance patterns and related soil properties and (iii) how to improve these soil maps based on the identification of static homogenous soil patterns by excluding temporal influences from the developed prediction model. Principal components and per-pixel analyses are used for the separation of static soil pattern from temporal reflectance pattern, influenced by (vital and senescent) vegetation and land management practices. The potential of the proposed algorithm is investigated using multitemporal multispectral RapidEye satellite imagery at a demonstration field ''Borrentin'' field in Northeast Germany.