ALBERT

Description: 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.

Description: 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.

Description: The main focus of the TERENO Northeastern German Lowland Observatory (TERENO-Northeast) is the regional impact of Global Change. Since 2011, the observatory has recorded changes in the geo-, hydro-, bio- and atmosphere at six main study sites. The year 2018, particularly in northeast Germany, was record-breaking in regard to dryness and heat. The mean temperature in Mecklenburg-Vorpommern was 2 °C above the long-term average and precipitation was very low at 440 mm (normally around 600 mm). The extreme summer of 2018 was a special opportunity for TERENO-Northeast to measure the regional effects of climate change. One of the consequences was the large number of forest fires, with one major fire destroying around 400 hectares. Other extreme reactions of the ecosystems were shown in TERENO-Northeast. For example, for the first time since its rewetting, Polder Zarnekov fell dry, with unpredictable consequences for the greenhouse gas exchanges. The forest ecosystems of Müritz National Park, on the other hand, survived the extreme summer surprisingly well, partly because the months before the drought were relatively damp. The research activities of TERENO-Northeast form an important basis to develop realistic options for improved adaptation strategies to the ongoing global change with its particular region-specific effects and challenges.

Description: 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.

Description: The Northeast German Lowland Observatory (TERENO‐NE) was established to investigate the regional impact of climate and land use change. TERENO‐NE focuses on the Northeast German lowlands, for which a high vulnerability has been determined due to increasing temperatures and decreasing amounts of precipitation projected for the coming decades. To facilitate in‐depth evaluations of the effects of climate and land use changes and to separate the effects of natural and anthropogenic drivers in the region, six sites were chosen for comprehensive monitoring. In addition, at selected sites, geoarchives were used to substantially extend the instrumental records back in time. It is this combination of diverse disciplines working across different time scales that makes the observatory TERENO‐NE a unique observation platform. We provide information about the general characteristics of the observatory and its six monitoring sites and present examples of interdisciplinary research activities at some of these sites. We also illustrate how monitoring improves process understanding, how remote sensing techniques are fine‐tuned by the most comprehensive ground‐truthing site DEMMIN, how soil erosion dynamics have evolved, how greenhouse gas monitoring of rewetted peatlands can reveal unexpected mechanisms, and how proxy data provides a long‐term perspective of current ongoing changes.

Description: Agricultural monitoring of crops such as winter wheat and winter barley is of high importance for farmers in the context of current global challenges like climate change and population growth as well as the related food security and arable land scarcity. A dense time series of satellite remote sensing data enables a farmer to react quickly to disturbances by adapting his field management strategy. Whereas optical remote sensing data need clear conditions, radar remote sensing data are independent of clouds and thus ensure regular acquisitions. Therefore, this thesis investigates the potential of synthetic aperture radar (SAR) data of Sentinel-1 to monitor biophysical parameters and to detect phenological entry dates of winter wheat and winter barley in two study areas in Northeast Germany. Two meteorologically very different years are compared: 2017, which was a rather wet year, and 2018, which was an extremely hot and dry year. The three SAR backscatter parameters VV (vertical-vertical), VH (vertical-horizontal) and their ratio VH/VV as well as three polarimetric decomposition parameters (entropy, anisotropy and alpha) are extracted from 400 Sentinel-1 images covering the vegetation periods in 2017 and 2018. Extensive field measurements took place regularly at the same time period at predefined observation points. Field and laboratory measurements result in six biophysical parameters (wet and dry biomass, leaf area index (LAI), plant height as well as absolute and relative vegetation water content) that are analysed in the thesis. Phenological observations and meteorological data are obtained from the German Weather Service (DWD). To get a basic understanding of the development of the SAR signal in the course of the vegetation period, temporal profiles of all SAR parameters are analysed as a first step. Differences between test sites, years and crop types as well as between- and withinfield differences are evaluated. Single and multiple regression models are used to estimate biophysical parameters from SAR parameters. To detect phenological entry dates, local extrema and breakpoints of smoothed time series of all SAR parameters are compared to observed phenological entry dates. The analysis of temporal profiles shows characteristic curves indicating phenological development of wheat and barley. Depending on prevailing phenological stages, the variable contribution of soil and vegetation, structural changes of the plants as well as their water content are mainly influencing the SAR signal. Temporal profiles are strongly influenced by meteorological conditions, therefore remarkable differences between profiles of 2017 and 2018 are observable. Based on these findings, the data is split in three groups depending on phenological development for the regression analysis. Between tillering and end of booting, wheat achieves best single regression results for VV backscatter. Multiple regression using VV, VH, entropy and alpha only marginally improves regression results and show coefficients of determination (R2) of 0.76 for plant height, 0.7 for wet and dry biomass, 0.69 for vegetation water content and 0.67 for LAI. Wheat continuously achieves higher R2 values than barley. Regression results as well as temporal profiles reveal that between-field differences are more prominent than within-field differences. Unlike known from optical remote sensing data, it is not possible to detect field heterogeneity using SAR parameters only, which is mainly due to geometrical characteristics of the SAR data such as the speckle effect. Entry dates of phenological stages are successfully detected. Particularly the beginning of stem elongation and the beginning of heading as well as the hard dough stage (only in 2018) are detected with accuracies of around 5 days. The variability of identified entry dates is higher between years than between fields and test sites and mainly due to meterological differences. This thesis provides multiple new insights of Sentinel-1 SAR data in an agricultural context. It is one of the first studies applying polarimetric decomposition of dual-polarized SAR data for agricultural monitoring. Results of the thesis confirm and expand results of previous studies and are particularly useful as a basis for continuative research to fully exploit the opportunities of SAR data in an agricultural context. Additionally, some of the results can directly support farmers in their decisions about field management. Particularly the successful detection of the beginning of stem elongation as well as the detection of flag leave emergence is of high interest for farmers to define the optimal timing of the second and third nitrogen fertilization as well as the most effective application time of pesticides and an optimal irrigation start time. Furthermore, indicators for an optimal harvest time can be derived from SAR data. However, the development of practical and timely applications of SAR data that are directly usable for farmers requires further research such as the extension of the methods to further years, crop types and study areas.

Description: Das landwirtschaftliche Monitoring von Feldfrüchten wie Winterweizen und Wintergerste ist von großer Bedeutung für Landwirte im Rahmen aktueller globaler Herausforderungen wie Klimawandel und Bevölkerungswachstum sowie der damit zusammenhängenden Nahrungssicherheit und der steigenden Knappheit fruchtbarer Flächen. Eine dichte Zeitreihe aus Fernerkundungsdaten ermöglicht dem Landwirt, schnell auf Störungen zu reagieren, indem er seine Bewirtschaftungsstrategien anpasst. Während optische Satellitendaten auf wolkenfreie Bedingungen angewiesen sind, sind Radardaten wolkenunabhängig und sichern somit regelmäßige Aufnahmen. Diese Arbeit untersucht das Potential von Synthetic Aperture Radar (SAR) Daten der Sentinel-1 Mission, biophysikalische Parameter zu beobachten und phänologische Eintrittsdaten von Winterweizen und Wintergerste in zwei Untersuchungsgebieten in Nordostdeutschland zu erfassen. Zwei meteorologisch sehr unterschiedliche Jahre werden dabei miteinander verglichen: 2017, ein eher feuchtes Jahr, und 2018, ein extrem heißes und trockenes Jahr. Die drei SAR-Rückstreuungsparameter VV (vertikal-vertikal), VH (horizontal-horizontal) und deren Ratio VH/VV sowie drei Parameter abgeleitet aus polarimetrischer Dekomposition (Entropie, Anisotropie und Alpha) werden aus 400 Sentinel-1 Bildern extrahiert, die in den Vegetationsperioden 2017 und 2018 aufgenommen wurden. Umfangreiche Feldmessungen fanden in denselben Zeiträumen an vorher definierten Messpunkten statt. Feld- und Labormessungen resultieren in sechs biophysikalische Parameter (feuchte und trockene Biomasse, Blattflächenindex (LAI), Pflanzenhöhe sowie absoluter und relativer Pflanzenwassergehalt), die in dieser Arbeit betrachtet werden. Phänologische Beobachtungen und meteorologische Daten werden vom Deutschen Wetterdienst (DWD) bezogen. Um ein Grundlagenverständnis über die Entwicklung des SAR-Signals im Verlauf der Vegetationsperiode zu erhalten, wurde zunächst die zeitliche Entwicklung aller SAR-Parameter analysiert. Unterschiede zwischen Untersuchungsgebieten, Jahren und Fruchtarten sowie Unterschiede zwischen einzelnen Feldern und innerhalb eines Feldes werden evaluiert. Einfache und multiple Regressionsanalysen werden genutzt um biophysikalische Parameter aus den SAR-Parametern abzuleiten. Um Eintrittsdaten phänologischer Phasen zu detektieren, werden lokale Extrema und Strukturbrüche geglätteter Zeitreihen mit beobachteten phänologischen Eintrittsdaten verglichen. Die Analyse der zeitlichen Entwicklung der SAR-Parameter zeigt charakteristische Kurven, die die phänologische Entwicklung von Weizen und Gerste nachzeichnen. Abhängig von der vorherrschenden phänologischen Phase beeinflussen der unterschiedliche Anteil von Boden und Vegetation, strukturelle Änderungen der Pflanzen sowie deren Wassergehalt maßgeblich das SAR-Signal. Die Zeitreihen sind stark abhängig von meteorologischen Bedingungen, daher sind bemerkenswerte Unterschiede zwischen den Jahren 2017 und 2018 zu beobachten. Basierend auf diesen Ergebnissen wurden die Daten für die Regressionsanalyse in drei Gruppen abhängig von ihrer phänologischen Entwicklung aufgeteilt. Zwischen Bestockung und Ende des Ährenschwellens erreicht Weizen dabei die besten Ergebnisse für einfache Regression mit VV-Rückstreuung. Die multiple Regression mit den Parametern VV, VH, Entropie und Alpha verbessert die Regressionsergebnisse nur geringfügig und erreicht Determinationskoeffizienten (R2) von 0.76 für Pflanzenhöhe, 0.7 für feuchte und trockene Biomasse, 0.69 für absoluten Pflanzenwassergehalt und 0.67 für den LAI. Weizen erreicht durchgängig höhere R2-Werte als Gerste. Die Ergebnisse der Regressionsanalyse sowie die zeitlichen Verläufe zeigen, dass Unterschiede zwischen Feldern deutlicher hervortreten als Unterschiede innerhalb eines Feldes. Anders als von optischen Fernerkundungsdaten bekannt, ist es hauptsächlich aufgrund der geometrischen Eigenschaften der SAR-Daten wie dem Speckle-Effekt nicht möglich, die Heterogenität eines Feldes ausschließlich mithilfe von SAR-Parametern abzuleiten. Eintrittsdaten phänologischer Phasen konnten erfolgreich detektiert werden. Vor allem der Beginn des Schossens, der Beginn des Ährenschiebens sowie die Gelbreife (nur in 2018) konnten mit Genauigkeiten von ca. 5 Tagen erkannt werden. Die Variabilität der identifizierten Eintrittsdaten zwischen den beiden Jahre ist dabei größer als die Variabilität zwischen einzelnen Feldern und den beiden Untersuchungsgebieten, was hauptsächlich auf meteorologische Unterschiede zurückzuführen ist. Diese Arbeit stellt mehrere neue Erkenntnisse über Sentinel-1 SAR-Daten im landwirtschaftlichen Kontext bereit. Es ist eine der ersten Arbeiten, die polarimetrische Dekomposition von dual-polarisierten SAR-Daten für ein landwirtschaftliches Monitoring anwendet. Die Ergebnisse dieser Arbeit bestätigen und ergänzen Ergebnisse bisheriger Studien und sind vor allem als Basis für weiterführende Forschungsaktivitäten wertvoll, um die Möglichkeiten von SAR-Daten im landwirtschaftlichen Kontext voll auszuschöpfen. Zusätzlich können einige der Ergebnisse Landwirte direkt bei der Entscheidungsfindung hinsichtlich ihrer Feldbewirtschaftung unterstützen. Vor allem das erfolgreiche Erkennen des Schossbeginns sowie des Hervortretens des Fahnenblattes kurz vor dem Ährenschieben ist für Landwirte von hohem Interesse, um den optimalen Zeitpunkt für die zweite und dritte Stickstoffgabe, die effektivste Zeit des Pestizideinsatzes sowie den optimalen Startzeitpunkt der Bewässerung zu bestimmen. Weiterhin können Indikatoren zum Bestimmen des optimalen Erntezeitpunktes aus SAR-Daten abgeleitet werden. Dennoch benötigt die Entwicklung von praxis- und zeitnahen Anwendungen, die direkt für den Landwirt nutzbar sind, weiterführende Forschung wie zum Beispiel die Erweiterung der Methoden auf weitere Jahre, Fruchtarten und Untersuchungsgebiete.