@article{LaswayKinaboMremietal.2021,
  author    = {Lasway, Julius V. and Kinabo, Neema R. and Mremi, Rudolf F. and Martin, Emanuel H. and Nyakunga, Oliver C. and Sanya, John J. and Rwegasira, Gration M. and Lesio, Nicephor and Gideon, Hulda and Pauly, Alain and Eardley, Connal and Peters, Marcell K. and Peterson, Andrew T. and Steffan-Dewenter, Ingolf and Njovu, Henry K.},
  title     = {A synopsis of the Bee occurrence data of northern Tanzania},
  series = {Biodiversity Data Journal},
  volume    = {9},
  journal   = {Biodiversity Data Journal},
  doi       = {10.3897/BDJ.9.e68190},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-265018},
  year      = {2021},
  abstract  = {Background Bees (Hymenoptera: Apoidea: Anthophila) are the most important group of pollinators with about 20,507 known species worldwide. Despite the critical role of bees in providing pollination services, studies aiming at understanding which species are present across disturbance gradients are scarce. Limited taxononomic information for the existing and unidentified bee species in Tanzania make their conservation haphazard. Here, we present a dataset of bee species records obtained from a survey in nothern Tanzania i.e. Kilimanjaro, Arusha and Manyara regions. Our findings serve as baseline data necessary for understanding the diversity and distribution of bees in the northern parts of the country, which is a critical step in devising robust conservation and monitoring strategies for their populations. New information In this paper, we present information on 45 bee species belonging to 20 genera and four families sampled using a combination of sweep-netting and pan trap methods. Most species (27, ~ 60\%) belong to the family Halictidae followed by 16 species (35.5\%) from the family Apidae. Megachilidae and Andrenidae were the least represented, each with only one species (2.2\%). Additional species of Apidae and Megachilidae sampled during this survey are not yet published on Global Biodiversity Information Facility (GBIF), once they will be available on GBIF, they will be published in a subsequent paper. From a total of 953 occurrences, highest numbers were recorded in Kilimanjaro Region (n = 511), followed by Arusha (n = 410) and Manyara (n = 32), but this pattern reflects the sampling efforts of the research project rather than real bias in the distributions of bee species in northern Tanzania.},
  language  = {en}
}
@phdthesis{Awoye2015,
  author    = {Awoye, Oy{\´e}monbad{\´e} Herv{\´e} Rodrigue},
  title     = {Implications of future climate change on agricultural production in tropical West Africa: evidence from the Republic of Benin},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-122887},
  school      = {Universit{\"a}t W{\"u}rzburg},
  year      = {2015},
  abstract  = {Environmental interlinked problems such as human-induced land cover change, water scarcity, loss in soil fertility, and anthropogenic climate change are expected to affect the viability of agriculture and increase food insecurity in many developing countries. Climate change is certainly the most serious of these challenges for the twenty-first century. The poorest regions of the world - tropical West Africa included - are the most vulnerable due to their high dependence on climate and weather sensitive activities such as agriculture, and the widespread poverty that limits the institutional and economic capacities to adapt to the new stresses brought about by climate change. Climate change is already acting negatively on the poor smallholders of tropical West Africa whose livelihoods dependent mainly on rain-fed agriculture that remains the cornerstone of the economy in the region. Adaptation of the agricultural systems to climate change effects is, therefore, crucial to secure the livelihoods of these rural communities. Since information is a key for decision-making, it is important to provide well-founded information on the magnitude of the impacts in order to design appropriate and sustainable adaptation strategies. Considering the case of agricultural production in the Republic of Benin, this study aims at using large-scale climatic predictors to assess the potential impacts of past and future climate change on agricultural productivity at a country scale in West Africa. Climate signals from large-scale circulation were used because state-of-the art regional climate models (RCM) still do not perfectly resolve synoptic and mesoscale convective processes. It was hypothesised that in rain-fed systems with low investments in agricultural inputs, yield variations are widely governed by climatic factors. Starting with pineapple, a perennial fruit crops, the study further considered some annual crops such as cotton in the group of fibre crops, maize, sorghum and rice in the group of cereals, cowpeas and groundnuts belonging to the legume crops, and cassava and yams which are root and tuber crops. Thus the selected crops represented the three known groups of photosynthetic pathways (i.e. CAM, C3, and C4 plants). In the study, use was made of the historical agricultural yield statistics for the Republic of Benin, observed precipitation and mean near-surface air temperature data from the Climatic Research Unit (CRU TS 3.1) and the corresponding variables simulated by the regional climate model (RCM) REMO. REMO RCM was driven at its boundaries by the global climate model ECHAM 5. Simulations with different greenhouse gas concentrations (SRES-A1B and B1 emission scenarios) and transient land cover change scenarios for present-day and future conditions were considered. The CRU data were submitted to empirical orthogonal functions analysis over the north hemispheric part of Africa to obtain large-scale observed climate predictors and associated consistent variability modes. REMO RCM data for the same region were projected on the derived climate patterns to get simulated climate predictors. By means of cross-validated Model Output Statistics (MOS) approach combined with Bayesian model averaging (BMA) techniques, the observed climate predictors and the crop predictand were further on used to derive robust statistical relationships. The robust statistical crop models perform well with high goodness-of-fit coefficients (e.g. for all combined crop models: 0.49 ≤ R2 ≤ 0.99; 0.28 ≤ Brier-Skill-Score ≤ 0.90). Provided that REMO RCM captures the main features of the real African climate system and thus is able to reproduce its inter-annual variability, the time-independent statistical transfer functions were then used to translate future climate change signal from the simulated climate predictors into attainable crop yields/crop yield changes. The results confirm that precipitation and air temperature governed agricultural production in Benin in general, and particularly, pineapple yield variations are mainly influenced by temperature. Furthermore, the projected yield changes under future anthropogenic climate change during the first-half of the 21st century amount up to -12.5\% for both maize and groundnuts, and -11\%, -29\%, -33\% for pineapple, cassava, and cowpeas respectively. Meanwhile yield gain of up to +10\% for sorghum and yams, +24\% for cotton, and +39\% for rice are expected. Over the time period 2001 - 2050, on average the future yield changes range between -3\% and -13\% under REMO SRES-B1 (GHG)+LCC, -2\% and -11\% under REMO SRES-A1B (GHG only),and -3\% and -14\% under REMO SRES-A1B (GHG)+LCC for pineapple, maize, sorghum, groundnuts, cowpeas and cassava. In the meantime for yams, cotton and rice, the average yield gains lie in interval of about +2\% to +7\% under REMO SRES-B1 (GHG)+LCC, +0.1\% and +12\% under REMO SRES-A1B (GHG only), and +3\% and +10\% under REMO SRES-A1B (GHG)+LCC. For sorghum, although the long-term average future yield depicts a reduction there are tendencies towards increasing yields in the future. The results also reveal that the increases in mean air temperature more than the changes in precipitation patterns are responsible for the projected yield changes. As well the results suggest that the reductions in pineapple yields cannot be attributed to the land cover/land use changes across sub-Saharan Africa. The production of groundnuts and in particular yams and cotton will profit from the on-going land use/land cover changes while the other crops will face detrimental effects. Henceforth, policymakers should take effective measures to limit the on-going land degradation processes and all other anthropogenic actions responsible for temperature increase. Biotechnological improvement of the cultivated crop varieties towards development of set of seed varieties adapted to hotter and dry conditions should be included in the breeding pipeline programs. Amongst other solutions, application of appropriate climate-smart agricultural practices and conservation agriculture are also required to offset the negative impacts of climate change in agriculture.},
  subject      = {Benin},
  language  = {en}
}
@article{ForkuorConradThieletal.2014,
  author    = {Forkuor, Gerald and Conrad, Christopher and Thiel, Michael and Ullmann, Tobias and Zoungrana, Evence},
  title     = {Integration of Optical and Synthetic Aperture Radar Imagery for Improving Crop Mapping in Northwestern Benin, West Africa},
  doi       = {10.3390/rs6076472},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-113070},
  year      = {2014},
  abstract  = {Crop mapping in West Africa is challenging, due to the unavailability of adequate satellite images (as a result of excessive cloud cover), small agricultural fields and a heterogeneous landscape. To address this challenge, we integrated high spatial resolution multi-temporal optical (RapidEye) and dual polarized (VV/VH) SAR (TerraSAR-X) data to map crops and crop groups in northwestern Benin using the random forest classification algorithm. The overall goal was to ascertain the contribution of the SAR data to crop mapping in the region. A per-pixel classification result was overlaid with vector field boundaries derived from image segmentation, and a crop type was determined for each field based on the modal class within the field. A per-field accuracy assessment was conducted by comparing the final classification result with reference data derived from a field campaign. Results indicate that the integration of RapidEye and TerraSAR-X data improved classification accuracy by 10\%-15\% over the use of RapidEye only. The VV polarization was found to better discriminate crop types than the VH polarization. The research has shown that if optical and SAR data are available for the whole cropping season, classification accuracies of up to 75\% are achievable.},
  language  = {en}
}
@article{AsamGessnerAlmengorGonzalezetal.2022,
  author    = {Asam, Sarah and Gessner, Ursula and Almengor Gonz{\´a}lez, Roger and Wenzl, Martina and Kriese, Jennifer and Kuenzer, Claudia},
  title     = {Mapping crop types of Germany by combining temporal statistical metrics of Sentinel-1 and Sentinel-2 time series with LPIS data},
  series = {Remote Sensing},
  volume    = {14},
  journal   = {Remote Sensing},
  number    = {13},
  issn      = {2072-4292},
  doi       = {10.3390/rs14132981},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-278969},
  year      = {2022},
  abstract  = {Nationwide and consistent information on agricultural land use forms an important basis for sustainable land management maintaining food security, (agro)biodiversity, and soil fertility, especially as German agriculture has shown high vulnerability to climate change. Sentinel-1 and Sentinel-2 satellite data of the Copernicus program offer time series with temporal, spatial, radiometric, and spectral characteristics that have great potential for mapping and monitoring agricultural crops. This paper presents an approach which synergistically uses these multispectral and Synthetic Aperture Radar (SAR) time series for the classification of 17 crop classes at 10 m spatial resolution for Germany in the year 2018. Input data for the Random Forest (RF) classification are monthly statistics of Sentinel-1 and Sentinel-2 time series. This approach reduces the amount of input data and pre-processing steps while retaining phenological information, which is crucial for crop type discrimination. For training and validation, Land Parcel Identification System (LPIS) data were available covering 15 of the 16 German Federal States. An overall map accuracy of 75.5\% was achieved, with class-specific F1-scores above 80\% for winter wheat, maize, sugar beet, and rapeseed. By combining optical and SAR data, overall accuracies could be increased by 6\% and 9\%, respectively, compared to single sensor approaches. While no increase in overall accuracy could be achieved by stratifying the classification in natural landscape regions, the class-wise accuracies for all but the cereal classes could be improved, on average, by 7\%. In comparison to census data, the crop areas could be approximated well with, on average, only 1\% of deviation in class-specific acreages. Using this streamlined approach, similar accuracies for the most widespread crop types as well as for smaller permanent crop classes were reached as in other Germany-wide crop type studies, indicating its potential for repeated nationwide crop type mapping.},
  language  = {en}
}
@article{ClaussYanKuenzer2016,
  author    = {Clauss, Kersten and Yan, Huimin and Kuenzer, Claudia},
  title     = {Mapping Paddy Rice in China in 2002, 2005, 2010 and 2014 with MODIS Time Series},
  series = {Remote Sensing},
  volume    = {8},
  journal   = {Remote Sensing},
  number    = {5},
  doi       = {10.3390/rs8050434},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-180557},
  year      = {2016},
  abstract  = {Rice is an important food crop and a large producer of green-house relevant methane. Accurate and timely maps of paddy fields are most important in the context of food security and greenhouse gas emission modelling. During their life-cycle, rice plants undergo a phenological development that influences their interaction with waves in the visible light and infrared spectrum. Rice growth has a distinctive signature in time series of remotely-sensed data. We used time series of MODIS (Moderate Resolution Imaging Spectroradiometer) products MOD13Q1 and MYD13Q1 and a one-class support vector machine to detect these signatures and classify paddy rice areas in continental China. Based on these classifications, we present a novel product for continental China that shows rice areas for the years 2002, 2005, 2010 and 2014 at 250-m resolution. Our classification has an overall accuracy of 0.90 and a kappa coefficient of 0.77 compared to our own reference dataset for 2014 and correlates highly with rice area statistics from China's Statistical Yearbooks (R2 of 0.92 for 2010, 0.92 for 2005 and 0.90 for 2002). Moderate resolution time series analysis allows accurate and timely mapping of rice paddies over large areas with diverse cropping schemes.},
  language  = {en}
}
@article{KnauerGessnerFensholtetal.2017,
  author    = {Knauer, Kim and Gessner, Ursula and Fensholt, Rasmus and Forkuor, Gerald and Kuenzer, Claudia},
  title     = {Monitoring agricultural expansion in Burkina Faso over 14 years with 30 m resolution time series: the role of population growth and implications for the environment},
  series = {Remote Sensing},
  volume    = {9},
  journal   = {Remote Sensing},
  number    = {2},
  doi       = {10.3390/rs9020132},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-171905},
  year      = {2017},
  abstract  = {Burkina Faso ranges amongst the fastest growing countries in the world with an annual population growth rate of more than three percent. This trend has consequences for food security since agricultural productivity is still on a comparatively low level in Burkina Faso. In order to compensate for the low productivity, the agricultural areas are expanding quickly. The mapping and monitoring of this expansion is difficult, even on the basis of remote sensing imagery, since the extensive farming practices and frequent cloud coverage in the area make the delineation of cultivated land from other land cover and land use types a challenging task. However, as the rapidly increasing population could have considerable effects on the natural resources and on the regional development of the country, methods for improved mapping of LULCC (land use and land cover change) are needed. For this study, we applied the newly developed ESTARFM (Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model) framework to generate high temporal (8-day) and high spatial (30 m) resolution NDVI time series for all of Burkina Faso for the years 2001, 2007, and 2014. For this purpose, more than 500 Landsat scenes and 3000 MODIS scenes were processed with this automated framework. The generated ESTARFM NDVI time series enabled extraction of per-pixel phenological features that all together served as input for the delineation of agricultural areas via random forest classification at 30 m spatial resolution for entire Burkina Faso and the three years. For training and validation, a randomly sampled reference dataset was generated from Google Earth images and based on expert knowledge. The overall accuracies of 92\% (2001), 91\% (2007), and 91\% (2014) indicate the well-functioning of the applied methodology. The results show an expansion of agricultural area of 91\% between 2001 and 2014 to a total of 116,900 km\(^2\). While rainfed agricultural areas account for the major part of this trend, irrigated areas and plantations also increased considerably, primarily promoted by specific development projects. This expansion goes in line with the rapid population growth in most provinces of Burkina Faso where land was still available for an expansion of agricultural area. The analysis of agricultural encroachment into protected areas and their surroundings highlights the increased human pressure on these areas and the challenges of environmental protection for the future.},
  language  = {en}
}
@phdthesis{Redlich2020,
  author    = {Redlich, Sarah},
  title     = {Opportunities and obstacles of ecological intensification: Biological pest control in arable cropping systems},
  doi       = {10.25972/OPUS-17122},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-171228},
  school      = {Universit{\"a}t W{\"u}rzburg},
  year      = {2020},
  abstract  = {Modern agriculture is the basis of human existence, a blessing, but also a curse. It provides nourishment and well-being to the ever-growing human population, yet destroys biodiversity-mediated processes that underpin productivity: ecosystem services such as water filtration, pollination and biological pest control. Ecological intensification is a promising alternative to conventional farming, and aims to sustain yield and ecosystem health by actively managing biodiversity and essential ecosystem services. Here, I investigate opportunities and obstacles for ecological intensification. My research focuses on 1) the relative importance of soil, management and landscape variables for biodiversity and wheat yield (Chapter II); 2) the influence of multi-scale landscape-level crop diversity on biological pest control in wheat (Chapter III) and 3) on overall and functional bird diversity (Chapter IV). I conclude 4) by introducing a guide that helps scientists to increase research impact by acknowledging the role of stakeholder engagement for the successful implementation of ecological intensification (Chapter V). Ecological intensification relies on the identification of natural pathways that are able to sustain current yields. Here, we crossed an observational field study of arthropod pests and natural enemies in 28 real-life wheat systems with an orthogonal on-field insecticide-fertilizer experiment. Using path analysis, we quantified the effect of 34 factors (soil characteristics, recent and historic crop management, landscape heterogeneity) that directly or indirectly (via predator-prey interactions) contribute to winter wheat yield. Reduced soil preparation and high crop rotation diversity enhanced crop productivity independent of external agrochemical inputs. Concurrently, biological control by arthropod natural enemies could be restored by decreasing average field sizes on the landscape scale, extending crop rotations and reducing soil disturbance. Furthermore, reductions in agrochemical inputs decreased pest abundances, thereby facilitating yield quality. Landscape-level crop diversity is a promising tool for ecological intensification. However, biodiversity enhancement via diversification measures does not always translate into agricultural benefits due to antagonistic species interactions (intraguild predation). Additionally, positive effects of crop diversity on biological control may be masked by inappropriate study scales or correlations with other landscape variables (e.g. seminatural habitat). Therefore, the multiscale and context-dependent impact of crop diversity on biodiversity and ecosystem services is ambiguous. In 18 winter wheat fields along a crop diversity gradient, insect- and bird-mediated pest control was assessed using a natural enemy exclusion experiment with cereal grain aphids. Although birds did not influence the strength of insect-mediated pest control, crop diversity (rather than seminatural habitat cover) enhanced aphid regulation by up to 33\%, particularly on small spatial scales. Crop diversification, an important Greening measure in the European Common Agricultural Policy, can improve biological control, and could lower dependence on insecticides, if the functional identity of crops is taken into account. Simple measures such as 'effective number of crop types' help in science communication. Although avian pest control did not respond to landscape-level crop diversity, birds may still benefit from increased crop resources in the landscape, depending on their functional grouping (feeding guild, conservation status, habitat preference, nesting behaviour). Observational studies of bird functional diversity on 14 wheat study fields showed that non-crop landscape heterogeneity rather than crop diversity played a key role in determining the richness of all birds. Insect-feeding, non-farmland and non-threatened birds increased across multiple spatial scales (up to 3000 m). Only crop-nesting farmland birds declined in heterogeneous landscapes. Thus, crop diversification may be less suitable for conserving avian diversity, but abundant species benefit from overall habitat heterogeneity. Specialist farmland birds may require more targeted management approaches. Identifying ecological pathways that favour biodiversity and ecosystem services provides opportunities for ecological intensification that increase the likelihood of balancing conservation and productivity goals. However, change towards a more sustainable agriculture will be slow to come if research findings are not implemented on a global scale. During dissemination activities within the EU project Liberation, I gathered information on the advantages and shortcomings of ecological intensification and its implementation. Here, I introduce a guide ('TREE') aimed at scientists that want to increase the impact of their research. TREE emphasizes the need to engage with stakeholders throughout the planning and research process, and actively seek and promote science dissemination and knowledge implementation. This idea requires scientists to leave their comfort zone and consider socioeconomic, practical and legal aspects often ignored in classical research. Ecological intensification is a valuable instrument for sustainable agriculture. Here, I identified new pathways that facilitate ecological intensification. Soil quality, disturbance levels and spatial or temporal crop diversification showed strong positive correlations with natural enemies, biological pest control and yield, thereby lowering the dependence on agrochemical inputs. Differences between functional groups caused opposing, scale-specific responses to landscape variables. Opposed to our predictions, birds did not disturb insect-mediated pest control in our study system, nor did avian richness relate to landscape-level crop diversity. However, dominant functional bird groups increased with non-crop landscape heterogeneity. These findings highlight the value of combining different on-field and landscape approaches to ecological intensification. Concurrently, the success of ecological intensification can be increased by involving stakeholders throughout the research process. This increases the quality of science and reduces the chance of experiencing unscalable obstacles to implementation.},
  language  = {en}
}
@article{DhillonDahmsKuebertFlocketal.2022,
  author    = {Dhillon, Maninder Singh and Dahms, Thorsten and K{\"u}bert-Flock, Carina and Steffan-Dewenter, Ingolf and Zhang, Jie and Ullmann, Tobias},
  title     = {Spatiotemporal Fusion Modelling Using STARFM: Examples of Landsat 8 and Sentinel-2 NDVI in Bavaria},
  series = {Remote Sensing},
  volume    = {14},
  journal   = {Remote Sensing},
  number    = {3},
  issn      = {2072-4292},
  doi       = {10.3390/rs14030677},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-323471},
  year      = {2022},
  abstract  = {The increasing availability and variety of global satellite products provide a new level of data with different spatial, temporal, and spectral resolutions; however, identifying the most suited resolution for a specific application consumes increasingly more time and computation effort. The region's cloud coverage additionally influences the choice of the best trade-off between spatial and temporal resolution, and different pixel sizes of remote sensing (RS) data may hinder the accurate monitoring of different land cover (LC) classes such as agriculture, forest, grassland, water, urban, and natural-seminatural. To investigate the importance of RS data for these LC classes, the present study fuses NDVIs of two high spatial resolution data (high pair) (Landsat (30 m, 16 days; L) and Sentinel-2 (10 m, 5-6 days; S), with four low spatial resolution data (low pair) (MOD13Q1 (250 m, 16 days), MCD43A4 (500 m, one day), MOD09GQ (250 m, one-day), and MOD09Q1 (250 m, eight day)) using the spatial and temporal adaptive reflectance fusion model (STARFM), which fills regions' cloud or shadow gaps without losing spatial information. These eight synthetic NDVI STARFM products (2: high pair multiply 4: low pair) offer a spatial resolution of 10 or 30 m and temporal resolution of 1, 8, or 16 days for the entire state of Bavaria (Germany) in 2019. Due to their higher revisit frequency and more cloud and shadow-free scenes (S = 13, L = 9), Sentinel-2 (overall R\(^2\) = 0.71, and RMSE = 0.11) synthetic NDVI products provide more accurate results than Landsat (overall R\(^2\) = 0.61, and RMSE = 0.13). Likewise, for the agriculture class, synthetic products obtained using Sentinel-2 resulted in higher accuracy than Landsat except for L-MOD13Q1 (R\(^2\) = 0.62, RMSE = 0.11), resulting in similar accuracy preciseness as S-MOD13Q1 (R\(^2\) = 0.68, RMSE = 0.13). Similarly, comparing L-MOD13Q1 (R\(^2\) = 0.60, RMSE = 0.05) and S-MOD13Q1 (R\(^2\) = 0.52, RMSE = 0.09) for the forest class, the former resulted in higher accuracy and precision than the latter. Conclusively, both L-MOD13Q1 and S-MOD13Q1 are suitable for agricultural and forest monitoring; however, the spatial resolution of 30 m and low storage capacity makes L-MOD13Q1 more prominent and faster than that of S-MOD13Q1 with the 10-m spatial resolution.},
  language  = {en}
}
@article{ReinermannGessnerAsametal.2019,
  author    = {Reinermann, Sophie and Gessner, Ursula and Asam, Sarah and Kuenzer, Claudia and Dech, Stefan},
  title     = {The Effect of Droughts on Vegetation Condition in Germany: An Analysis Based on Two Decades of Satellite Earth Observation Time Series and Crop Yield Statistics},
  series = {Remote Sensing},
  volume    = {11},
  journal   = {Remote Sensing},
  number    = {15},
  doi       = {10.3390/rs11151783},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-225165},
  pages     = {1783, 1-21},
  year      = {2019},
  abstract  = {Central Europe experienced several droughts in the recent past, such as in the year 2018, which was characterized by extremely low rainfall rates and high temperatures, resulting in substantial agricultural yield losses. Time series of satellite earth observation data enable the characterization of past drought events over large temporal and spatial scales. Within this study, Moderate Resolution Spectroradiometer (MODIS) Enhanced Vegetation Index (EVI) (MOD13Q1) 250 m time series were investigated for the vegetation periods of 2000 to 2018. The spatial and temporal development of vegetation in 2018 was compared to other dry and hot years in Europe, like the drought year 2003. Temporal and spatial inter- and intra-annual patterns of EVI anomalies were analyzed for all of Germany and for its cropland, forest, and grassland areas individually. While vegetation development in spring 2018 was above average, the summer months of 2018 showed negative anomalies in a similar magnitude as in 2003, which was particularly apparent within grassland and cropland areas in Germany. In contrast, the year 2003 showed negative anomalies during the entire growing season. The spatial pattern of vegetation status in 2018 showed high regional variation, with north-eastern Germany mainly affected in June, north-western parts in July, and western Germany in August. The temporal pattern of satellite-derived EVI deviances within the study period 2000-2018 were in good agreement with crop yield statistics for Germany. The study shows that the EVI deviation of the summer months of 2018 were among the most extreme in the study period compared to other years. The spatial pattern and temporal development of vegetation condition between the drought years differ.},
  language  = {en}
}