TY - JOUR A1 - Förster, Saskia A1 - Kaden, Klaus A1 - Förster, Michael A1 - Itzerott, Sibylle T1 - Crop type mapping using spectral-temporal profiles and phenological information JF - Computers and electronics in agriculture N2 - Spatially explicit multi-year crop information is required for many environmental applications. The study presented here proposes a hierarchical classification approach for per-plot crop type identification that is based on spectral-temporal profiles and accounts for deviations from the average growth stage timings by incorporating agro-meteorological information in the classification process. It is based on the fact that each crop type has a distinct seasonal spectral behavior and that the weather may accelerate or delay crop development. The classification approach was applied to map 12 crop types in a 14,000 km(2) catchment area in Northeast Germany for several consecutive years. An accuracy assessment was performed and compared to those of a maximum likelihood classification. The 7.1% lower overall classification accuracy of the spectral-temporal profiles approach may be justified by its independence of ground truth data. The results suggest that the number and timing of image acquisition is crucial to distinguish crop types. The increasing availability of optical imagery offering a high temporal coverage and a spatial resolution suitable for per-plot crop type mapping will facilitate the continuous refining of the spectral-temporal profiles for common crop types and different agro-regions and is expected to improve the classification accuracy of crop type maps using these profiles. KW - Crop type mapping KW - NDVI temporal profiles KW - Multi-temporal KW - Phenological correction KW - Agro-meteorological data Y1 - 2012 U6 - https://doi.org/10.1016/j.compag.2012.07.015 SN - 0168-1699 VL - 89 IS - 32 SP - 30 EP - 40 PB - Elsevier CY - Oxford ER - TY - JOUR A1 - Joshi, Siddharth A1 - Pingel, Patrick A1 - Grigorian, Souren A1 - Panzner, Tobias A1 - Pietsch, Ullrich A1 - Neher, Dieter A1 - Forster, Michael A1 - Scherf, Ullrich T1 - Bimodal temperature behavior of structure and mobility in high molecular weight p3ht thin films N2 - We report a temperature dependent crystalline structure of spin-coated thin films of high molecular weight regioregular poly(3-hexylthiophene) (P3HT) (M-n similar to 30000 g/mol) and its correlation with charge carrier mobility. These investigations show a reversible change of the crystalline structure, where the interlayer lattice spacing (100)along the alkyl side chains continuously increases up to a temperature of about 220 degrees C; in contrast, the in-plane pi-pi distance reduces with increasing temperature. These changes in structure are reversible and can be repeated several times. The temperature-induced structural properties differ for thick and thin films, pointing to a surface/interface role in stabilization of the layer morphology. In contrast to the structural changes, the carrier mobility is rather constant in the temperature range from room temperature up to 100-120 degrees C, followed by a continuous decrease. For thick layers this drop is significant and the transistor performance almost vanishes at high temperature, however, it completely recovers upon cooling back to roorn temperature. The drop of the charge carrier mobility at higher temperatures is in contrast with expectations front the structural studies, considering the increase of crystalline fraction of the polycrystalline layer. our electrical measurements Underscore that the reduction of the macroscopic mobility is mostly caused by it pronounced decrease of the intergrain transport. The thermally induced crystallization along(100) direction and the creation of numerous small crystallites at the film-substrate interface reduce the number of long polymer chain, bridging crystalline domains, which ultimately limits the macroscopic charge transport. Y1 - 2009 UR - http://pubs.acs.org/journal/mamobx U6 - https://doi.org/10.1021/Ma900021w SN - 0024-9297 ER - TY - JOUR A1 - Heistermann, Maik A1 - Francke, Till A1 - Scheiffele, Lena A1 - Petrova, Katya Dimitrova A1 - Budach, Christian A1 - Schrön, Martin A1 - Trost, Benjamin A1 - Rasche, Daniel A1 - Güntner, Andreas A1 - Doepper, Veronika A1 - Förster, Michael A1 - Köhli, Markus A1 - Angermann, Lisa A1 - Antonoglou, Nikolaos A1 - Zude, Manuela A1 - Oswald, Sascha T1 - Three years of soil moisture observations by a dense cosmic-ray neutron sensing cluster at an agricultural research site in north-east Germany JF - Earth system science data : ESSD N2 - Cosmic-ray neutron sensing (CRNS) allows for the estimation of root-zone soil water content (SWC) at the scale of several hectares. In this paper, we present the data recorded by a dense CRNS network operated from 2019 to 2022 at an agricultural research site in Marquardt, Germany - the first multi-year CRNS cluster. Consisting, at its core, of eight permanently installed CRNS sensors, the cluster was supplemented by a wealth of complementary measurements: data from seven additional temporary CRNS sensors, partly co-located with the permanent ones; 27 SWC profiles (mostly permanent); two groundwater observation wells; meteorological records; and Global Navigation Satellite System reflectometry (GNSS-R). Complementary to these continuous measurements, numerous campaign-based activities provided data by mobile CRNS roving, hyperspectral im-agery via UASs, intensive manual sampling of soil properties (SWC, bulk density, organic matter, texture, soil hydraulic properties), and observations of biomass and snow (cover, depth, and density). The unique temporal coverage of 3 years entails a broad spectrum of hydro-meteorological conditions, including exceptional drought periods and extreme rainfall but also episodes of snow coverage, as well as a dedicated irrigation experiment. Apart from serving to advance CRNS-related retrieval methods, this data set is expected to be useful for vari-ous disciplines, for example, soil and groundwater hydrology, agriculture, or remote sensing. Hence, we show exemplary features of the data set in order to highlight the potential for such subsequent studies. The data are available at doi.org/10.23728/b2share.551095325d74431881185fba1eb09c95 (Heistermann et al., 2022b). Y1 - 2023 U6 - https://doi.org/10.5194/essd-15-3243-2023 SN - 1866-3508 SN - 1866-3516 VL - 15 IS - 7 SP - 3243 EP - 3262 PB - Copernics Publications CY - Katlenburg-Lindau ER - TY - JOUR A1 - Yang, Xiao Hui A1 - Jaiser, Frank A1 - Neher, Dieter A1 - Lawson, PaDreyia V. A1 - Brédas, Jean-Luc A1 - Zojer, Egbert A1 - Güntner, Roland A1 - Scanduicci de Freitas, Patricia A1 - Forster, Michael A1 - Scherf, Ullrich T1 - Suppression of the keto-emission in polyfluorene light-emitting diodes : Experiments and models N2 - The spectral characteristics of polyfluorene (PF)-based light-emitting diodes (LEDs) containing a defined low concentration of either keto-defects or of the polymer poly(9.9-octylfuorene-co-benzothiadiazole) (F8BT) are preseneted. Both types of blend layers were tested in different device configurations with respect to the relative and absolute intensities of green blue emission components. It is shown that blending hole-transporting molecules into the emission layer at low concentration or incorporation of a suitable hole-transport layer reduces the green emission contribution in the electroluminescence (EL) spectrum of the PF:F8BT blend, which is similar to what is observed for the keto- containing PF layer. We conclude that the keto-defects in PF homopolymer layers mainly constitute weakly emissive electron traps, in agreement with the results of quantum-mechanical calculations Y1 - 2004 SN - 1616-301X ER - TY - JOUR A1 - Döpper, Veronika A1 - Jagdhuber, Thomas A1 - Holtgrave, Ann-Kathrin A1 - Heistermann, Maik A1 - Francke, Till A1 - Kleinschmit, Birgit A1 - Förster, Michael T1 - Following the cosmic-ray-neutron-sensing-based soil moisture under grassland and forest BT - exploring the potential of optical and SAR remote sensing JF - Science of remote Sensing N2 - Deriving soil moisture content (SMC) at the regional scale with different spatial and temporal land cover changes is still a challenge for active and passive remote sensing systems, often coped with machine learning methods. So far, the reference measurements of the data-driven approaches are usually based on point data, which entails a scale gap to the resolution of the remote sensing data. Cosmic Ray Neutron Sensing (CRNS) indirectly provides SMC estimates of a soil volume covering more than 1 ha and vertical depth up to 80 cm and is thus able to narrow this scale gap. So far, the CRNS-based SMC has only been used as validation source of remote sensing based SMC products. Its beneficial large sensing volume, especially in depth, has not been exploited yet. However, the sensing volume of the CRNS, which is changing with hydrological conditions, bears challenges for the comparison with remote sensing observations. This study, for the fist time, aims to understand the direct linkage of optical (Sentinel 2) and SAR (Sentinel 1) data with CRNS-based SMC. Thereby, the CRNS-based SMC is obtained by an experimental CRNS cluster that covers the high temporal and spatial SMC variability of an entire pre-alpine subcatchment. Using different Random Forest regressions, we analyze the potentials and limitations of both remote sensing sensors to follow the CRNS-based SMC signal. Our results show that it is possible to link the CRNS-based SMC signal with SAR and optical remote sensing observations via Random Forest modelling. We found that Sentinel 2 data is able to separate wet from dry periods with a R2 of 0.68. It is less affected by the changing soil volume that contributes to the CRNS-based SMC signal and it is able to assign a land cover specific SMC distribution. However, Sentinel 2 regression models are not accurate (R2 < 0.21) in mapping the CRNSbased SMC for the frequently mowed grassland areas of the study site. It requires soil type and topographical information to accurately follow the CRNS-based SMC signal with Random Forest regression. Sentinel 1 data instead is affected by the changing soil volume that contributes to the CRNS-based SMC signal. It has reasonable model performance (R2 = 0.34) when the CRNS data correspond to surface SMC. Also for Sentinel 1 the retrieval is impacted by the mowing activities at the test site. When separating the CRNS data set into dry and wet periods, soil properties and topography are the main drivers of SMC estimation. Sentinel 1 or Sentinel 2 data add the existing temporal variability to the regression models. The analysis underlines the need of combining optical and SAR observations (Sentinel 1, Sentinel 2) as well as soil property and topographical information to understand and follow the CRNS-based SMC signal for different hydrological conditions and land cover types. KW - Sentinel 1 KW - Sentinel 2 KW - soil texture KW - topography KW - sensing volume KW - Random Forest regression KW - CRNS Y1 - 2022 U6 - https://doi.org/10.1016/j.srs.2022.100056 SN - 2666-0172 VL - 5 PB - Elsevier CY - Amsterdam ER -