The knowledge about heterogeneity on agricultural fields is essential for a sustainable and effective field management. This study investigates the performance of Synthetic Aperture Radar (SAR) data of the Sentinel-1 satellites to detect variability between and within agricultural fields in two test sites in Germany. For this purpose, the temporal profiles of the SAR backscatter in VH and VV polarization as well as their ratio VH/VV of multiple wheat and barley fields are illustrated and interpreted considering differences between acquisition settings, years, crop types and fields. Within-field variability is examined by comparing the SAR backscatter with several crop parameters measured at multiple points in 2017 and 2018. Structural changes, particularly before and after heading, as well as moisture and crop cover differences are expressed in the backscatter development. Furthermore, the crop parameters wet and dry biomass, absolute and relative vegetation water content, leaf area index (LAI) and plant height are related to SAR backscatter parameters using linear and exponential as well as multiple regression. The regression performance is evaluated using the coefficient of determination (R ${}^2$ ) and the root mean square error (RMSE) and is strongly dependent on the phenological growth stage. Wheat shows R ${}^2$ values around 0.7 for VV backscatter and multiple regression and most crop parameters before heading. Single fields even reach R ${}^2$ values above 0.9 for VV backscatter and for multiple regression related to plant height with RMSE values around 10 cm. The formulation of clear rules remains challenging, as there are multiple influencing factors and uncertainties and a lack of conformity.

Local parameters for climate modelling are highly dependent on crop types and their phenological growth stage. The land cover change of agricultural areas during the growing season provides important information to distinguish crop types. The presented progressive classification algorithm identifies crop types based on their phenological development and their corresponding reflectance characteristics in multitemporal satellite images of the four sensors Landsat-7 and -8, Sentinel-2A and RapidEye. It distinguishes crop types not only retrospectively, but progressively during the growing season starting in early spring. Binary fuzzy c-means clustering differentiates seven crop types in eight decisions at particular time periods. These decisions are based on expert knowledge about plant characteristics in different phenological stages. The unsupervised classification approach and previously defined decisions enable the algorithm to work independently of training data. The fuzzy approach provides certainties of crop-type existence and generates first results in early spring. The accuracy and reliability of the classification results improve with increasing time. The method is developed at the German Durable Environmental Multidisciplinary Monitoring Information Network (DEMMIN). The study area is an official test site of the Joint Experiment of Crop Assessment and Monitoring (JECAM) and is located in an intensely agricultural used area in Northern Germany. Classification results were produced for the growing seasons 2015 and 2016. The overall accuracy in 2015 amounted to 89.49%. A challenge remains the distinct separation of wheat and rye, whereas barley, rapeseed, potato, corn and sugar beet are classified with high accuracies. The overall accuracy in 2016 was lower (77.19%) due to unfavourable weather conditions. Eine progressive Klassifikation von Fruchtarten unter Verwendung von multitemporalen Fernerkundungsdaten und phänologischen Informationen. Lokale Parameter für die Klimamodellierung sind stark von Fruchtarten und deren aktuellen phänologischen Entwicklungsstadien abhängig. Die Bodenbedeckung landwirtschaftlicher Flächen verändert sich im Laufe der Vegetationsperiode erheblich und liefert somit wichtige Informationen zur Unterscheidung verschiedener Fruchtarten. Der vorgestellte fortschreitende Klassifikationsalgorithmus identifiziert Fruchtarten anhand ihrer phänologischen Entwicklung und ihrer entsprechenden charakteristischen Reflexionseigenschaften von multitemporalen optischen Satellitenbildern der Sensoren Landsat-7 und -8, Sentinel-2 und RapidEye. Die Fruchtarten werden nicht am Ende der Vegetationsperiode klassifiziert, sondern fortschreitend mit Beginn der Vegetationsperiode im Frühling. Mithilfe eines binären Fuzzy C-Means Clustering können sieben Fruchtarten in acht Entscheidungsschritten zu bestimmten Zeiträumen unterschieden werden. Diese Entscheidungen basieren auf Expertenwissen über Pflanzencharakteristiken in den verschiedenen phänologischen Phasen. Die unüberwachte Klassifikationsmethode und die zuvor definierten Entscheidungen ermöglichen es dem Algorithmus, unabhängig von Trainingsdaten zu arbeiten. Die Verwendung von ”fuzzy” Entscheidungen erlaubt es, die Verlässlichkeitder Fruchtartenzuordnung anzugeben und liefert erste Ergebnisse bereits im Frühjahr. Die Genauigkeit und Zuverlässigkeit der Klassifikationsergebnisse verbessern sich im Laufe der Zeit. Die Methode wurde am Kalibrierungs- und Validierungsstandort DEMMIN (Durable Environmental Multidisciplinary Monitoring Information Network) entwickelt. Das Untersuchungsgebiet liegt in einem intensiv landwirtschaftlich genutzten Gebiet in Norddeutschland und ist außerdem offizieller Teststandort des Joint Experiment of Crop Assessment and Monitoring (JECAM). Klassifikationsergebnisse wurden für die Vegetationsperioden der Jahre 2015 und 2016 erstellt. Die Gesamtgenauigkeit im Jahr 2015 betrug 89,49%. Eine Herausforderung bleibt die eindeutige Trennung von Weizen und Roggen, wohingegen Gerste, Raps, Kartoffeln, Mais und Zuckerrüben mit hohen Genauigkeiten identifiziert werden konnten. Aufgrund ungünstiger Witterungsbedingungen war die Gesamtgenauigkeit für das Jahr 2016 mit 77,19% etwas geringer.

The time series of synthetic aperture radar (SAR) data are commonly and successfully used to monitor the biophysical parameters of agricultural fields. Because, until now, mainly backscatter coefficients have been analysed, this study examines the potentials of entropy, anisotropy, and alpha angle derived from a dual-polarimetric decomposition of Sentinel-1 data to monitor crop development. The temporal profiles of these parameters are analysed for wheat and barley in the vegetation periods 2017 and 2018 for 13 fields in two test sites in Northeast Germany. The relation between polarimetric parameters and biophysical parameters observed in the field is investigated using linear and exponential regression models that are evaluated using the coefficient of determination (R²) and the root mean square error (RMSE). The performance of single regression models is furthermore compared to those of multiple regression models, including backscatter coefficients in VV and VH polarisation as well as polarimetric decomposition parameters entropy and alpha. Characteristic temporal profiles of entropy, anisotropy, and alpha reflecting the main phenological changes in plants as well as the meteorological differences between the two years are observed for both crop types. The regression models perform best for data from the phenological growth stages tillering to booting. The highest R² values of the single regression models are reached for the plant height of wheat related to entropy and anisotropy with R² values of 0.64 and 0.61, respectively. The multiple regression models of VH, VV, entropy, and alpha outperform single regression models in most cases. R² values of multiple regression models of plant height (0.76), wet biomass (0.7), dry biomass (0.7), and vegetation water content (0.69) improve those of single regression models slightly by up to 0.05. Additionally, the RMSE values of the multiple regression models are around 10% lower compared to those of single regression models. The results indicate the capability of dual-polarimetric decomposition parameters in serving as meaningful input parameters for multiple regression models to improve the prediction of biophysical parameters. Additionally, their temporal profiles indicate phenological development dependent on meteorological conditions. Knowledge about biophysical parameter development and phenology is important for farmers to monitor crop growth variability during the vegetation period to adapt and to optimize field management.

Information provided by satellite data is becoming increasingly important in the field of agriculture. Estimating biomass, nitrogen content or crop yield can improve farm management and optimize precision agriculture applications. A vast amount of data is made available both as map material and from space. However, it is up to the user to select the appropriate data for a particular problem. Without the appropriate knowledge, this may even entail an economic risk. This study therefore investigates the direct relationship between satellite data from six different optical sensors as well as different soil and relief parameters and yield data from cereal and canola recorded by the thresher in the field. A time series of 13 years is considered, with 947 yield data sets consisting of dense point data sets and 755 satellite images. To answer the question of how well the relationship between remote sensing data and yield is, the correlation coefficient r per field is calculated and interpreted in terms of crop type, phenology, and sensor characteristics. The correlation value r is particularly high when a field and its crop are spatially heterogeneous and when the correct phenological time of the crop is reached at the time of satellite imaging. Satellite images with higher resolution, such as RapidEye and Sentinel-2 performed better in comparison with lower resolution sensors of the Landsat series. The additional Red Edge spectral band also has advantage, especially for cereal yield estimation. The study concludes that there are high correlation values between yield data and satellite data, but several conditions must be met which are presented and discussed here.

Monitoring the phenological development of agricultural plants is of high importance for farmers to adapt their management strategies and estimate yields. The aim of this study is to analyze the sensitivity of remote sensing features to phenological development of winter wheat and winter barley and to test their transferability in two test sites in Northeast Germany and in two years. Local minima, local maxima and breakpoints of smoothed time series of synthetic aperture radar (SAR) data of the Sentinel-1 VH (vertical-horizontal) and VV (vertical-vertical) intensities and their ratio VH/VV; of the polarimetric features entropy, anisotropy and alpha derived from polarimetric decomposition; as well as of the vegetation index NDVI (Normalized Difference Vegetation Index) calculated using optical data of Sentinel-2 are compared with entry dates of phenological stages. The beginning of stem elongation produces a breakpoint in the time series of most parameters for wheat and barley. Furthermore, the beginning of heading could be detected by all parameters, whereas particularly a local minimum of VH and VV backscatter is observed less then 5 days before the entry date. The medium milk stage can not be detected reliably, whereas the hard dough stage of barley takes place approximately 6–8 days around a local maximum of VH backscatter in 2018. Harvest is detected for barley using the fourth breakpoint of most parameters. The study shows that backscatter and polarimetric parameters as well as the NDVI are sensitive to specific phenological developments. The transferability of the approach is demonstrated, whereas differences between test sites and years are mainly caused by meteorological differences.