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In this paper, we demonstrate the capabilities of Pleiades-1a imagery for very high resolution (VHR) crop yield estimation by utilizing the predictor variables from the horizontal-spectral information, through Normalized Difference Vegetation Indices (NDVI), and the vertical-volumetric crop characteristics, through the derivation of Crop Canopy Models (CCMs), from the stereo imaging capacity of the satellite. CCMs captured by Unmanned Aerial Vehicles are widely used in precision farming applications, but they are not suitable for the mapping of large or inaccessible areas. We further explore the spatiotemporal relationship of the CCMs and the NDVI for five observation dates during the growing season for eight selected crop fields in Germany with harvester-measured ground truth crop yield. Moreover, we explore different CCM normalization methods, as well as linear and non-linear regression algorithms, for the crop yield estimation. Overall, using the Extremely Randomized Trees regression, the combination of CCMs and NDVI achieves an R2 coefficient of determination of 0.92. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

One-third of Germany's land surface area is covered by forest (around 11.4 million hectares), and thus, it characterizes the landscape. The forest is a habitat for a large number of animal and plant species, a source of raw materials, important for climate protection, and a well-being refuge for people, to name just a few of its many functions. During the annual forest condition surveys, the crown condition of German forests is assessed on the basis of field samples at fixed locations, as the crown condition of forest trees is considered an important indicator of their vitality. Since the start of the surveys in 1984, the mean crown defoliation of all tree species has increased, now averaging about 25% for all tree species. Additionally, it shows a strong rise in the rate of dieback. In 2019, the most significant changes were observed. Due to the drastic changes in recent years, efforts are being made to assess the situation of the forest using different remote sensing methods. There are now a number of freely available products provided to the public, and more will follow as a result of numerous projects in the context of earth-observation (EO)-based monitoring and mapping of the forests in Germany. In 2020, the situation regarding the use of remote sensing for the German forest was already investigated in more detail. However, these results no longer reflect the current situation. The changes of the last 3 years are the content of this publication. For this study, 84 citable research publications were thoroughly analyzed and compared with the situation in 2020. As a major result, we found a shift in the research focus towards disturbance monitoring and a tendency to cover larger areas, including national-scale studies. In addition to the review of the scientific literature, we also reviewed current research projects and related products. In congruence to the recent developments in terms of publications in scientific journals, these projects and products reflect the need for comprehensive, timely, large-area, and complementary EO-based information around forests expressed in multiple political programs. With this review, we provide an update of previous work and link it to current research activities. We conclude that there are still gaps between the information needs of forest managers who usually rely on information from field perspectives and the EO-based information products. [ABSTRACT FROM AUTHOR]

Precision agriculture relies on understanding crop growth dynamics and plant responses to short-term changes in abiotic factors. In this technical note, we present and discuss a technical approach for cost-effective, non-invasive, time-lapse crop monitoring that automates the process of deriving further plant parameters, such as biomass, from 3D object information obtained via stereo images in the red, green, and blue (RGB) color space. The novelty of our approach lies in the automated workflow, which includes a reliable automated data pipeline for 3D point cloud reconstruction from dynamic scenes of RGB images with high spatio-temporal resolution. The setup is based on a permanent rigid and calibrated stereo camera installation and was tested over an entire growing season of winter barley at the Global Change Experimental Facility (GCEF) in Bad Lauchstädt, Germany. For this study, radiometrically aligned image pairs were captured several times per day from 3 November 2021 to 28 June 2022. We performed image preselection using a random forest (RF) classifier with a prediction accuracy of 94.2% to eliminate unsuitable, e.g., shadowed, images in advance and obtained 3D object information for 86 records of the time series using the 4D processing option of the Agisoft Metashape software package, achieving mean standard deviations (STDs) of 17.3–30.4 mm. Finally, we determined vegetation heights by calculating cloud-to-cloud (C2C) distances between a reference point cloud, computed at the beginning of the time-lapse observation, and the respective point clouds measured in succession with an absolute error of 24.9–35.6 mm in depth direction. The calculated growth rates derived from RGB stereo images match the corresponding reference measurements, demonstrating the adequacy of our method in monitoring geometric plant traits, such as vegetation heights and growth spurts during the stand development using automated workflows. [ABSTRACT FROM AUTHOR]

Frequency-domain electromagnetic induction (FDEMI) methods are frequently used in non-invasive, area-wise mapping of the subsurface electromagnetic soil properties. A crucial part of data analysis is the geophysical inversion of the data, resulting in either conductivity and/or magnetic susceptibility subsurface distributions. We present a novel 1D stochastic optimization approach that combines dimension-adapting reversible jump Markov chain Monte Carlo (MCMC) with artificial bee colony (ABC) optimization for geophysical inversion, with specific application to frequency-domain electromagnetic induction (FDEMI) data. Several solution models of simplified model geometry and a variable number of model knots, which are found by the inversion method, are used to create re-sampled resulting average models. We present synthetic test inversions using conductivity models based on 14 direct-push (DP) EC logs from Greece, Italy, and Germany, as well as field data applications using multi-coil FDEMI devices from three sites in Azerbaijan and Germany. These examples show that the method can effectively lead to solutions that resemble the known DP input models or image reasonable stratigraphic and archaeological features in the field data. Neighboring 1D solutions on field data examples show high coherence along profiles even though each 1D inversion is independently handled. The computational effort for one 1D inversion is less than 120,000 forward calculations, which is much less than usually needed in MCMC inversions, whereas the resulting models show more plausible solutions due to the dimension-adapting properties of the inversion method. [ABSTRACT FROM AUTHOR]

Over the past two decades, and particularly since 2018, Central Europe has experienced several droughts with strong impacts on ecosystems and food production. It is expected that under accelerating climate change, droughts and resulting vegetation and ecosystem stress will further increase. Against this background, there is a need for techniques and datasets that allow for monitoring of the timing, extent and effects of droughts. Vegetation indices (VIs) based on satellite Earth observation (EO) can be used to directly assess vegetation stress over large areas. Here, we use a MODIS Enhanced Vegetation Index (EVI) time series to analyze and characterize the vegetation stress on Germany's croplands and grasslands that has occurred since 2000. A special focus is put on the years from 2018 to 2022, an extraordinary 5-year period characterized by a high frequency of droughts and heat waves. The study reveals strong variations in agricultural drought patterns during the past major drought years in Germany (such as 2003 or 2018), as well as large regional differences in climate-related vegetation stress. The northern parts of Germany showed a higher tendency to be affected by drought effects, particularly after 2018. Further, correlation analyses showed a strong relationship between annual yields of maize, potatoes and winter wheat and previous vegetation stress, where the timing of strongest relationships could be related to crop-specific development stages. Our results support the potential of VI time series for robustly monitoring and predicting effects of climate-related vegetation development and agricultural yields. [ABSTRACT FROM AUTHOR]

Extracting 3D tree models based on terrestrial laser scanning (TLS) point clouds is a challenging task as trees are complex objects. Current TLS devices acquire high-density data that allow a detailed reconstruction of the tree topology. However, in dense forests a fully automatic reconstruction of trees is often limited by occlusion, wind influences and co-registration issues. In this paper, a semi-automatic method for extracting branching and stem structure based on equirectangular projections (range and intensity maps) is presented. The digitization of branches and stems is based on 2D maps, which enables simple navigation and raster processing. The modeling is performed for each viewpoint individually instead of using a registered point cloud. Previously reconstructed 2D-skeletons are transformed between the maps. Therefore, wind influences, orientation imperfections of scans and data gaps can be overcome. The method is applied to a TLS dataset acquired in a forest in Germany. In total 34 scans were carried out within a managed forest to measure approximately 90 spruce trees with minimal occlusions. The results demonstrate the feasibility of the presented approach to extract tree models with a high completeness and correctness and provide an excellent input for further modeling applications. [ABSTRACT FROM AUTHOR]

Nighttime lights are routinely used as a proxy for economic activity when official statistics are unavailable and are increasingly applied to study the effects of shocks or policy interventions at small geographic scales. The implicit assumption is that the ability of nighttime lights to pick up changes in GDP does not depend on local characteristics of the region under investigation or the scale of aggregation. This study uses panel data on regional GDP growth from six countries, and nighttime lights from the Defense Meteorological Satellite Program (DMSP) to investigate potential nonlinearities and measurement errors in the light production function. Our results for high statistical capacity countries (the United States and Germany) show that nightlights are significantly less responsive to changes in GDP at higher baseline level of GDP, higher population densities, and for agricultural GDP. We provide evidence that these nonlinearities are too large to be caused by differences in measurement errors across regions. We find similar but noisier relationships in other high-income countries (Italy and Spain) and emerging economies (Brazil and China). We also present results for different aggregation schemes and find that the overall relationship, including the nonlinearity, is stable across regions of different shapes and sizes but becomes noisier when regions become few and large. These findings have important implications for studies using nighttime lights to evaluate the economic effects of shocks or policy interventions. On average, nighttime lights pick up changes in GDP across many different levels of aggregation, down to relatively small geographies. However, the nonlinearity we document in this paper implies that some studies may fail to detect policy-relevant effects in places where lights react little to changes in economic activity or they may mistakenly attribute this heterogeneity to the treatment effect of their independent variable of interest. [ABSTRACT FROM AUTHOR]

Many analyses of the perturbed ionospheric D-region and its influence on the propagation of ground-based and satellite signals are based on data obtained in ionospheric remote sensing by very low/low frequency (VLF/LF) signals. One of the most significant causes of errors in these analyses is the lack of data related to the analysed area and time period preceding the considered perturbation. In this paper, we examine the influence of the estimation of the quiet ionosphere parameters on the determination of the electron density (Ne) and total electron content in the D-region (TECD) during the influence of a solar X-ray flare. We present a new procedure in which parameters describing the quiet ionosphere are calculated based on observations of the analysed area by a VLF/LF signal at the observed time. The developed procedure is an upgrade of the quiet ionospheric D-region (QIonDR) model that allows for a more precise analysis of the D-region intensively perturbed by a solar X-ray flare. The presented procedure is applied to data obtained in ionospheric remote sensing by the DHO signal emitted in Germany and received in Serbia during 30 solar X-ray flares. We give analytical expressions for the dependencies of the analysed parameters on the X-ray flux maximum at the times of the X-ray flux maximum and the most intense D-region perturbation. The results show that the obtained Ne and TECD are larger than in the cases when the usual constant values of the quiet ionosphere parameters are used. [ABSTRACT FROM AUTHOR]

In the past, many algorithms have been applied for three-dimensional (3-D) single tree extraction using Airborne Laser Scanner (ALS) data. Clustering based algorithms are widely used in different applications but rarely being they used in the field of forestry using ALS data as an input. In this paper, a comparative qualitative study was conducted using the iterative partitioning and hierarchical clustering based mechanisms and full waveform ALS data as an input to extract the individual trees/tree crowns in their most appropriate shape. The full waveform LIght Detection And Ranging (LIDAR) data was collected from the Waldkirch black forest area in the south-western part of Germany in August 2005 with density of 4-5 points/m². Both the clustering algorithms were used in their original and modified form for a comparative qualitative analysis of the results obtained in the form of individual clusters containing 3-D points for each tree/tree crown. A total of 378 trees were found in all the 1.2 ha area with height ranging from 15 m to 50.9 m. The forest contains mainly older trees with deciduous, coniferous and mixed stands. The findings showed that among the three kind of clustering methods applied (normal k-means, modified k-means and hierarchical clustering), the modified k-means algorithm using external seed points and scaling down the height for initialization of the clustering process was the most promising method for the extraction of clusters of individual trees/tree crowns. A 3-D reconstruction of extracted individual clusters was carried out using QHull algorithm. In this study, the result was not possible to validate quantitatively due to lack of the field inventory data. [ABSTRACT FROM AUTHOR]

Accurate detection and delineation of individual trees and their crowns in dense forest environments are essential for forest management and ecological applications. This study explores the potential of combining leaf-off and leaf-on structure from motion (SfM) data products from unoccupied aerial vehicles (UAVs) equipped with RGB cameras. The main objective was to develop a reliable method for precise tree stem detection and crown delineation in dense deciduous forests, demonstrated at a structurally diverse old-growth forest in the Hainich National Park, Germany. Stem positions were extracted from the leaf-off point cloud by a clustering algorithm. The accuracy of the derived stem co-ordinates and the overall UAV-SfM point cloud were assessed separately, considering different tree types. Extracted tree stems were used as markers for individual tree crown delineation (ITCD) through a region growing algorithm on the leaf-on data. Stem positioning showed high precision values (0.867). Including leaf-off stem positions enhanced the crown delineation, but crown delineations in dense forest canopies remain challenging. Both the number of stems and crowns were underestimated, suggesting that the number of overstory trees in dense forests tends to be higher than commonly estimated in remote sensing approaches. In general, UAV-SfM point clouds prove to be a cost-effective and accurate alternative to LiDAR data for tree stem detection. The combined datasets provide valuable insights into forest structure, enabling a more comprehensive understanding of the canopy, stems, and forest floor, thus facilitating more reliable forest parameter extraction. [ABSTRACT FROM AUTHOR]

Spatiotemporally accurate estimates of crop traits are essential for both scientific modeling and practical decision making in sustainable agricultural management. Besides efficient and concise methods to derive these traits, site- and crop-specific reference data are needed to develop and validate retrieval methods. To address this shortcoming, this study first includes the establishment of 'MISPEL', a comprehensive spectral library (SpecLib) containing hyperspectral measurements and reference data for six key traits of ten widely grown crops. Secondly, crop-specific statistical leaf area index (LAI) models for winter wheat are developed based on a hyperspectral (MISPELFR) and a simulated Sentinel-2 (MISPELS2) SpecLib applying four nonparametric methods. Finally, an independent Sentinel-2 model evaluation at the DEMMIN test site in Germany is conducted, including a comparison with the commonly used SNAP-LAI product. To date, MISPEL comprises a set of 1411 spectra of ten crops and more than 6800 associated reference measurements. Cross-validations of winter wheat LAI models revealed that Elastic-net generalized linear model (GLMNET) and Gaussian process (GP) regressions outperformed partial least squares (PLS) and random forest (RF) regressions, showing RSQ values up to 0.86 and a minimal NRMSE of 0.21 using MISPELFR. GLMNET and GP models based on MISPELS2 further outperformed SNAP-based LAI estimates derived for the external validation site. Thus, it is concluded that the presented SpecLib 'MISPEL' and applied methodology have a very high potential for deriving diverse crop traits of multiple crops in view of most recent and future multi-, super-, and hyperspectral satellite missions. [ABSTRACT FROM AUTHOR]

Synthetic Aperture Radar (SAR) measurements are unique for mapping forest 3D structure and its changes in time. Tomographic SAR (TomoSAR) configurations exploit this potential by reconstructing the 3D radar reflectivity. The frequency of the SAR measurements is one of the main parameters determining the information content of the reconstructed reflectivity in terms of penetration and sensitivity to the individual vegetation elements. This paper attempts to review and characterize the structural information content of L-band TomoSAR reflectivity reconstructions, and their potential to forest structure mapping. First, the challenges in the accurate TomoSAR reflectivity reconstruction of volume scatterers (which are expected to dominate at L-band) and to extract physical structure information from the reconstructed reflectivity is addressed. Then, the L-band penetration capability is directly evaluated by means of the estimation performance of the sub-canopy ground topography. The information content of the reconstructed reflectivity is then evaluated in terms of complementary structure indices. Finally, the dependency of the TomoSAR reconstruction and of its structural information to both the TomoSAR acquisition geometry and the temporal change of the reflectivity that may occur in the time between the TomoSAR measurements in repeat-pass or bistatic configurations is evaluated. The analysis is supported by experimental results obtained by processing airborne acquisitions performed over temperate forest sites close to the city of Traunstein in the south of Germany. [ABSTRACT FROM AUTHOR]

The analysis of soil and land cover scattering properties and their connection with the parameters of microwave scattering is a longstanding research topic. Recently, the advent of modern space-borne microwave radiometers like SMAP in addition to the trend towards open data for scientific use fostered the development of enhanced models based on data fusion from different platforms permitting more accurate assessments. SMAP was designed to operate on an integrated combination of a radiometer and a radar, both operating in L-band. Unexpected failure of the radar component encouraged scientists to experiment various combination of data from the surviving radiometer with other sources of radar data, notably C-band Sentinel-1 data. In this work, we present a case study on a possible combination of SMAP radiometer data with X-band radar data from TerraSAR-X and COSMO-SkyMed, comparing results with those provided by NASA from their standard production procedures. The study was performed on two test sites, one at an agricultural site in Germany and one in the Brazilian Amazon, to explore very different vegetation conditions. This work is a part of a broader research effort addressing the combination of multiple sources of passive and active microwave sensing data. The research question defining this research effort is whether the use of data from multiple active sources affords either obtaining more accurate estimates of active–passive co-variation parameters for a given observation period, or shortening the minimum observation period by increasing the temporal density of active samples. In this framework, this paper addresses a preliminary comparison of fresh and past results obtained from C-, X-, and L-band active sensing data. The observed relations offer interesting clues on the impact of band selection on soil vegetation analysis. [ABSTRACT FROM AUTHOR]

The increasing availability and variety of global satellite products and the rapid development of new algorithms has provided great potential to generate a new level of data with different spatial, temporal, and spectral resolutions. However, the ability of these synthetic spatiotemporal datasets to accurately map and monitor our planet on a field or regional scale remains underexplored. This study aimed to support future research efforts in estimating crop yields by identifying the optimal spatial (10 m, 30 m, or 250 m) and temporal (8 or 16 days) resolutions on a regional scale. The current study explored and discussed the suitability of four different synthetic (Landsat (L)-MOD13Q1 (30 m, 8 and 16 days) and Sentinel-2 (S)-MOD13Q1 (10 m, 8 and 16 days)) and two real (MOD13Q1 (250 m, 8 and 16 days)) NDVI products combined separately to two widely used crop growth models (CGMs) (World Food Studies (WOFOST), and the semi-empiric Light Use Efficiency approach (LUE)) for winter wheat (WW) and oil seed rape (OSR) yield forecasts in Bavaria (70,550 km2) for the year 2019. For WW and OSR, the synthetic products' high spatial and temporal resolution resulted in higher yield accuracies using LUE and WOFOST. The observations of high temporal resolution (8-day) products of both S-MOD13Q1 and L-MOD13Q1 played a significant role in accurately measuring the yield of WW and OSR. For example, L- and S-MOD13Q1 resulted in an R2 = 0.82 and 0.85, RMSE = 5.46 and 5.01 dt/ha for WW, R2 = 0.89 and 0.82, and RMSE = 2.23 and 2.11 dt/ha for OSR using the LUE model, respectively. Similarly, for the 8- and 16-day products, the simple LUE model (R2 = 0.77 and relative RMSE (RRMSE) = 8.17%) required fewer input parameters to simulate crop yield and was highly accurate, reliable, and more precise than the complex WOFOST model (R2 = 0.66 and RRMSE = 11.35%) with higher input parameters. Conclusively, both S-MOD13Q1 and L-MOD13Q1, in combination with LUE, were more prominent for predicting crop yields on a regional scale than the 16-day products; however, L-MOD13Q1 was advantageous for generating and exploring the long-term yield time series due to the availability of Landsat data since 1982, with a maximum resolution of 30 m. In addition, this study recommended the further use of its findings for implementing and validating the long-term crop yield time series in different regions of the world. [ABSTRACT FROM AUTHOR]

Germany aims to reduce soil sealing to under 30 hectares per day by 2030 to address negative environmental impacts from the expansion of impervious surfaces. As cities adapt to climate change, spatially explicit very high-resolution information about the distribution of impervious surfaces is becoming increasingly important for urban planning and decision-making. This study proposes a method for mapping impervious surfaces in Google Earth Engine (GEE) using a data fusion approach of 0.9 m colour-infrared true orthophotos, digital elevation models, and vector data. We conducted a pixel-based random forest (RF) classification utilizing spectral indices, Grey-Level Co-occurrence Matrix texture features, and topographic features. Impervious surfaces were mapped with 0.9 m precision resulting in an Overall Accuracy of 92.31% and Kappa-Coefficient of 84.62%. To address challenges posed by high-resolution imagery, we superimposed the RF classification results with land use data from Germany's Authoritative Real Estate Cadastre Information System (ALKIS). The results show that 25.26% of the city of Wuppertal is covered by impervious surfaces coinciding with a government-funded study from 2020 based on Sentinel-2 Copernicus data that defined a proportion of 25.22% as built-up area. This demonstrates the effectiveness of our method for semi-automated mapping of impervious surfaces in GEE to support urban planning on a local to regional scale. [ABSTRACT FROM AUTHOR]

In this paper, we present the estimation of surface NO 2 concentrations over Germany using a machine learning approach. TROPOMI satellite observations of tropospheric NO 2 vertical column densities (VCDs) and several meteorological parameters are used to train the neural network model for the prediction of surface NO 2 concentrations. The neural network model is validated against ground-based in situ air quality monitoring network measurements and regional chemical transport model (CTM) simulations. Neural network estimation of surface NO 2 concentrations show good agreement with in situ monitor data with Pearson correlation coefficient (R) of 0.80. The results also show that the machine learning approach is performing better than regional CTM simulations in predicting surface NO 2 concentrations. We also performed a sensitivity analysis for each input parameter of the neural network model. The validated neural network model is then used to estimate surface NO 2 concentrations over Germany from 2018 to 2020. Estimated surface NO 2 concentrations are used to investigate the spatio-temporal characteristics, such as seasonal and weekly variations of NO 2 in Germany. The estimated surface NO 2 concentrations provide comprehensive information of NO 2 spatial distribution which is very useful for exposure estimation. We estimated the annual average NO 2 exposure for 2018, 2019 and 2020 is 15.53, 15.24 and 13.27 µ g/m 3 , respectively. While the annual average NO 2 concentration of 2018, 2019 and 2020 is only 12.79, 12.60 and 11.15 µ g/m 3 . In addition, we used the surface NO 2 data set to investigate the impacts of the coronavirus disease 2019 (COVID-19) pandemic on ambient NO 2 levels in Germany. In general, 10–30% lower surface NO 2 concentrations are observed in 2020 compared to 2018 and 2019, indicating the significant impacts of a series of restriction measures to reduce the spread of the virus. [ABSTRACT FROM AUTHOR]

In this paper, we propose a Stack Auto-encoder (SAE)-Driven and Semi-Supervised (SSL)-Based Deep Neural Network (DNN) to extract buildings from relatively low-cost satellite near infrared images. The novelty of our scheme is that we employ only an extremely small portion of labeled data for training the deep model which constitutes less than 0.08% of the total data. This way, we significantly reduce the manual effort needed to complete an annotation process, and thus the time required for creating a reliable labeled dataset. On the contrary, we apply novel semi-supervised techniques to estimate soft labels (targets) of the vast amount of existing unlabeled data and then we utilize these soft estimates to improve model training. Overall, four SSL schemes are employed, the Anchor Graph, the Safe Semi-Supervised Regression (SAFER), the Squared-loss Mutual Information Regularization (SMIR), and an equal importance Weighted Average of them (WeiAve). To retain only the most meaning information of the input data, labeled and unlabeled ones, we also employ a Stack Autoencoder (SAE) trained under an unsupervised manner. This way, we handle noise in the input signals, attributed to dimensionality redundancy, without sacrificing meaningful information. Experimental results on the benchmarked dataset of Vaihingen city in Germany indicate that our approach outperforms all state-of-the-art methods in the field using the same type of color orthoimages, though the fact that a limited dataset is utilized (10 times less data or better, compared to other approaches), while our performance is close to the one achieved by high expensive and much more precise input information like the one derived from Light Detection and Ranging (LiDAR) sensors. In addition, the proposed approach can be easily expanded to handle any number of classes, including buildings, vegetation, and ground. [ABSTRACT FROM AUTHOR]

Lidar remote sensing has proven to be a powerful tool for estimating ground elevation, canopy height, and additional vegetation parameters, which in turn are valuable information for the investigation of ecosystems. Spaceborne lidar systems, like the Global Ecosystem Dynamics Investigation (GEDI), can deliver these height estimates on a near global scale. This paper analyzes the accuracy of the first version of GEDI ground elevation and canopy height estimates in two study areas with temperate forests in the Free State of Thuringia, central Germany. Digital terrain and canopy height models derived from airborne laser scanning data are used as reference heights. The influence of various environmental and acquisition parameters (e.g., canopy cover, terrain slope, beam type) on GEDI height metrics is assessed. The results show a consistently high accuracy of GEDI ground elevation estimates under most conditions, except for areas with steep slopes. GEDI canopy height estimates are less accurate and show a bigger influence of some of the included parameters, specifically slope, vegetation height, and beam sensitivity. A number of relatively high outliers (around 9–13% of the measurements) is present in both ground elevation and canopy height estimates, reducing the estimation precision. Still, it can be concluded that GEDI height metrics show promising results and have potential to be used as a basis for further investigations. [ABSTRACT FROM AUTHOR]

Rapid and accurate yield estimates at both field and regional levels remain the goal of sustainable agriculture and food security. Hereby, the identification of consistent and reliable methodologies providing accurate yield predictions is one of the hot topics in agricultural research. This study investigated the relationship of spatiotemporal fusion modelling using STRAFM on crop yield prediction for winter wheat (WW) and oil-seed rape (OSR) using a semi-empirical light use efficiency (LUE) model for the Free State of Bavaria (70,550 km2), Germany, from 2001 to 2019. A synthetic normalised difference vegetation index (NDVI) time series was generated and validated by fusing the high spatial resolution (30 m, 16 days) Landsat 5 Thematic Mapper (TM) (2001 to 2012), Landsat 7 Enhanced Thematic Mapper Plus (ETM+) (2012), and Landsat 8 Operational Land Imager (OLI) (2013 to 2019) with the coarse resolution of MOD13Q1 (250 m, 16 days) from 2001 to 2019. Except for some temporal periods (i.e., 2001, 2002, and 2012), the study obtained an R2 of more than 0.65 and a RMSE of less than 0.11, which proves that the Landsat 8 OLI fused products are of higher accuracy than the Landsat 5 TM products. Moreover, the accuracies of the NDVI fusion data have been found to correlate with the total number of available Landsat scenes every year (N), with a correlation coefficient (R) of +0.83 (between R2 of yearly synthetic NDVIs and N) and −0.84 (between RMSEs and N). For crop yield prediction, the synthetic NDVI time series and climate elements (such as minimum temperature, maximum temperature, relative humidity, evaporation, transpiration, and solar radiation) are inputted to the LUE model, resulting in an average R2 of 0.75 (WW) and 0.73 (OSR), and RMSEs of 4.33 dt/ha and 2.19 dt/ha. The yield prediction results prove the consistency and stability of the LUE model for yield estimation. Using the LUE model, accurate crop yield predictions were obtained for WW (R2 = 0.88) and OSR (R2 = 0.74). Lastly, the study observed a high positive correlation of R = 0.81 and R = 0.77 between the yearly R2 of synthetic accuracy and modelled yield accuracy for WW and OSR, respectively. [ABSTRACT FROM AUTHOR]

The retention of structural elements such as habitat trees in forests managed for timber production is essential for fulfilling the objectives of biodiversity conservation. This paper seeks to predict tree-related microhabitats (TreMs) by close-range remote sensing parameters. TreMs, such as cavities or crown deadwood, are an established tool to quantify the suitability of habitat trees for biodiversity conservation. The aim to predict TreMs based on remote sensing (RS) parameters is supposed to assist a more objective and efficient selection of retention elements. The RS parameters were collected by the use of terrestrial laser scanning as well as unmanned aerial vehicles structure from motion point cloud generation to provide a 3D distribution of plant tissue. Data was recorded on 135 1-ha plots in Germany. Statistical models were used to test the influence of 28 RS predictors, which described TreM richness (R2: 0.31) and abundance (R2: 0.31) in moderate precision and described a deviance of 44% for the abundance and 38% for richness of TreMs. Our results indicate that multiple RS techniques can achieve moderate predictions of TreM occurrence. This method allows a more efficient and objective selection of retention elements such as habitat trees that are keystone features for biodiversity conservation, even if it cannot be considered a full replacement of TreM inventories due to the moderate statistical relationship at this stage. [ABSTRACT FROM AUTHOR]

In this paper we aim to show a proof-of-principle approach to detect and monitor weed management using glyphosate-based herbicides in agricultural practices. In a case study in Germany, we demonstrate the application of Sentinel-2 multispectral time-series data. Spectral broadband vegetation indices were analysed to observe vegetation traits and weed damage arising from herbicide-based management. The approach has been validated with stakeholder information about herbicide treatment using commercial products. As a result, broadband NDVI calculated from Sentinel-2 data showed explicit feedback after the glyphosate-based herbicide treatment. Vegetation damage could be detected after just two days following of glyphosate-based herbicide treatment. This trend was observed in three different application scenarios, i.e., during growing stage, before harvest and after harvest. The findings of the study demonstrate the feasibility of satellite based broadband NDVI data for the detection of glyphosate-based herbicide treatment and, e.g., the monitoring of latency to harvesting. The presented results can be used to implement monitoring concepts to provide the necessary transparency about weed treatment in agricultural practices and to support environmental monitoring. [ABSTRACT FROM AUTHOR]

Radar data assimilation has been operational at the Deutscher Wetterdienst for several years and is essential for generating accurate precipitation forecasts. The current work attempts to further enhance the radar data assimilation by improving the latent heat nudging (LHN) scheme and by reducing the observation error (OE) caused by the representation error of the efficient modular volume radar operator (EMVORADO). First of all, a series of hindcasts for a one-month convective period over Germany are performed. Compared with radar reflectivity and satellite observations, it is found that the LHN scheme that implicitly adjusts temperature performs better, and the beam broadening effect and the choice of the scattering schemes in EMVORADO are important. Moreover, the Mie scheme with the new parameterization to reduce the brightband effect not only proves to be the best in hindcasts but also that it results in the smallest standard deviations and the shortest horizontal correlation length scales of the OE in data assimilation experiments. [ABSTRACT FROM AUTHOR]

Storms can cause significant damage to forest areas, affecting biodiversity and infrastructure and leading to economic loss. Thus, rapid detection and mapping of windthrows are crucially important for forest management. Recent advances in computer vision have led to highly-accurate image classification algorithms such as Convolutional Neural Network (CNN) architectures. In this study, we tested and implemented an algorithm based on CNNs in an ArcGIS environment for automatic detection and mapping of damaged areas. The algorithm was trained and tested on a forest area in Bavaria, Germany.. It is a based on a modified U-Net architecture that was optimized for the pixelwise classification of multispectral aerial remote sensing data. The neural network was trained on labeled damaged areas from after-storm aerial orthophotos of a ca. 109 k m 2 forest area with RGB and NIR bands and 0.2-m spatial resolution. Around 10 7 pixels of labeled data were used in the process. Once the network is trained, predictions on further datasets can be computed within seconds, depending on the size of the input raster and the computational power used. The overall accuracy on our test dataset was 92 % . During visual validation, labeling errors were found in the reference data that somewhat biased the results because the algorithm in some instance performed better than the human labeling procedure, while missing areas affected by shadows. Our results are very good in terms of precision, and the methods introduced in this paper have several additional advantages compared to traditional methods: CNNs automatically detect high- and low-level features in the data, leading to high classification accuracies, while only one after-storm image is needed in comparison to two images for approaches based on change detection. Furthermore, flight parameters do not affect the results in the same way as for approaches that require DSMs and DTMs as the classification is only based on the image data themselves, and errors occurring in the computation of DSMs and DTMs do not affect the results with respect to the z component. The integration into the ArcGIS Platform allows a streamlined workflow for forest management, as the results can be accessed by mobile devices in the field to allow for high-accuracy ground-truthing and additional mapping that can be synchronized back into the database. Our results and the provided automatic workflow highlight the potential of deep learning on high-resolution imagery and GIS for fast and efficient post-disaster damage assessment as a first step of disaster management. [ABSTRACT FROM AUTHOR]

Monitoring within-field crop variability at fine spatial and temporal resolution can assist farmers in making reliable decisions during their agricultural management; however, it traditionally involves a labor-intensive and time-consuming pointwise manual process. To the best of our knowledge, few studies conducted a comparison of Sentinel-2 with UAV data for crop monitoring in the context of precision agriculture. Therefore, prospects of crop monitoring for characterizing biophysical plant parameters and leaf nitrogen of wheat and barley crops were evaluated from a more practical viewpoint closer to agricultural routines. Multispectral UAV and Sentinel-2 imagery was collected over three dates in the season and compared with reference data collected at 20 sample points for plant leaf nitrogen (N), maximum plant height, mean plant height, leaf area index (LAI), and fresh biomass. Higher correlations of UAV data to the agronomic parameters were found on average than with Sentinel-2 data with a percentage increase of 6.3% for wheat and 22.2% for barley. In this regard, VIs calculated from spectral bands in the visible part performed worse for Sentinel-2 than for the UAV data. In addition, large-scale patterns, formed by the influence of an old riverbed on plant growth, were recognizable even in the Sentinel-2 imagery despite its much lower spatial resolution. Interestingly, also smaller features, such as the tramlines from controlled traffic farming (CTF), had an influence on the Sentinel-2 data and showed a systematic pattern that affected even semivariogram calculation. In conclusion, Sentinel-2 imagery is able to capture the same large-scale pattern as can be derived from the higher detailed UAV imagery; however, it is at the same time influenced by management-driven features such as tramlines, which cannot be accurately georeferenced. In consequence, agronomic parameters were better correlated with UAV than with Sentinel-2 data. Crop growers as well as data providers from remote sensing services may take advantage of this knowledge and we recommend the use of UAV data as it gives additional information about management-driven features. For future perspective, we would advise fusing UAV with Sentinel-2 imagery taken early in the season as it can integrate the effect of agricultural management in the subsequent absence of high spatial resolution data to help improve crop monitoring for the farmer and to reduce costs. [ABSTRACT FROM AUTHOR]

Near-surface humidity is a crucial variable in many atmospheric processes, mostly related to the development of clouds and rain. The humidity at the height of a few tens of meters above ground level is highly influenced by surface characteristics. Measuring the near-surface humidity at high resolution, where most of the humidity's sinks and sources are found, is a challenging task using classical tools. A novel approach for measuring the humidity is based on commercial microwave links (CML), which provide a large part of the cellular networks backhaul. This study focuses on employing humidity measurements with high spatio–temporal resolution in Germany. One major goal is to assess the errors and the environmental influence by comparing the CML-derived humidity to in-situ humidity measurements at weather stations and reanalysis (COSMO-Rea6) products. The method of retrieving humidity from the CML has been improved as compared to previous studies due to the use of new data at high temporal resolution. The results show a similar correlation on average and generally good agreement between both the CML retrievals and the reanalysis, and 32 weather stations near Siegen, West Germany (CML—0.84, Rea6—0.85). Higher correlations are observed for CML-derived humidity during the daytime (0.85), especially between 9–17 LT (0.87) and a maximum at 12 LT (0.90). During the night, the correlations are lower on average (0.81), with a minimum at 3 LT (0.74). These results are discussed with attention to the diurnal boundary layer (BL) height variation which has a strong effect on the BL humidity temporal profile. Further metrics including root mean square errors, mean values and standard deviations, were also calculated. [ABSTRACT FROM AUTHOR]

Phenological metrics extracted from satellite data (phenometrics) have been increasingly used to access timely, spatially explicit information on crop phenology, but have rarely been calibrated and validated with field observations. In this study, we developed a calibration procedure to make phenometrics more comparable to ground-based phenological stages by optimising the settings of Best Index Slope Extraction (BISE) and smoothing algorithms together with thresholds. We used a six-year daily Moderate Resolution Imaging Spectrometer (MODIS) Normalized Difference Vegetation Index (NDVI) time series and 211 ground-observation records from four major crop species (winter wheat/barley, oilseed rape, and sugar beet) in central Germany. Results showed the superiority of the Savitzky–Golay algorithm in combination with BISE. The satellite-derived senescence dates matched ripeness stages of winter crops and the dates with maximum NDVI were closely related to the field-observed heading stage of winter cereals. We showed that the emergence of winter crops corresponded to the dates extracted with a threshold of 0.1, which translated into 8.89 days of root-mean-square error (RMSE) improvement compared to the standard threshold of 0.5. The method with optimised settings and thresholds can be easily transferred and applied to areas with similar growing conditions. Altogether, the results improve our understanding of how satellite-derived phenometrics can explain in situ phenological observations. [ABSTRACT FROM AUTHOR]

In this study, an analysis of multi-temporal and multi-frequency Synthetic Aperture Radar data is performed to investigate the backscatter behavior of various semantic classes in the context of flood mapping in central Europe. The focus is mainly on partially submerged vegetation such as forests and agricultural fields. The test area is located at River Saale, Saxony-Anhalt, Germany, which is covered by a time series of 39 TerraSAR-X data acquired within the time interval December 2009 to June 2013. The data set is supplemented by ALOS PALSAR L-band and RADARSAT-2 C-band data. The time series covers two inundations in January 2011 and June 2013 which allows evaluating backscatter variations between flood periods and normal water level conditions using different radar wavelengths. According to the results, there is potential in detecting flooding beneath vegetation in all microwave wavelengths, even in X-band for sparse vegetation or leaf-off forests. [ABSTRACT FROM AUTHOR]

Monitoring of (surface) urban heat islands (UHI) is possible through satellite remote sensing of the land surface temperature (LST). Previous UHI studies are based on medium and high spatial resolution images, which are in the best-case scenario available about four times per day. This is not adequate for monitoring diurnal UHI development. High temporal resolution LST data (a few measurements per hour) over a whole city can be acquired by instruments onboard geostationary satellites. In northern Germany, geostationary LST data are available in pixels sized 3,300 by 6,700 m. For UHI monitoring, this resolution is too coarse, it should be comparable instead to the width of a building block: usually not more than 100 m. Thus, an LST downscaling is proposed that enhances the spatial resolution by a factor of about 2,000, which is much higher than in any previous study. The case study presented here (Hamburg, Germany) yields promising results. The latter, available every 15 min in 100 m spatial resolution, showed a high explained variance (R2: 0.71) and a relatively low root mean square error (RMSE: 2.2 K). For lower resolutions the downscaling scheme performs even better (R2: 0.80, RMSE: 1.8 K for 500 m; R2: 0.82, RMSE: 1.6 K for 1,000 m). [ABSTRACT FROM AUTHOR]

This study explores ICESat/GLAS waveform data in Thuringian Forest, a low mountain range located in central Germany. Lidar remote sensing has been proven to directly derive tree height as a key variable of forest structure. The GLAS signal is, however, very sensitive to surface topography because of the large footprint size. This study therefore focuses on forests in a mountainous area to assess the potential of GLAS data to derive terrain elevation and tree height. The work enhances the empirical knowledge about the interaction between GLAS waveform and landscape structure regarding a special temperate forest site with a complex terrain. An algorithm to retrieve tree height directly from GLA01 waveform data is proposed and compared to an approach using GLA14 Gaussian parameters. The results revealed that GLAS height estimates were accurate for areas with a slope up to 10° whereas waveforms of areas above 15° were problematic. Slopes between 10-15° have been found to be a critical crossover. Further, different waveform shape types and landscape structure classes were developed as a new possibility to explore the waveform in its whole structure. Based on the detailed analysis of some waveform examples, it could be demonstrated that the waveform shape can be regarded as a product of the complex interaction between surface and canopy structure. Consequently, there is a great variety of waveform shapes which in turn considerably hampers GLAS tree height extraction in areas with steep slopes and complex forest conditions. [ABSTRACT FROM AUTHOR]

Deep learning (DL)—in particular convolutional neural networks (CNN)—methods are widely spread in object detection and recognition of remote sensing images. In the domain of DL, there is a need for large numbers of training samples. These samples are mostly generated based on manual identification. Identifying and labelling these objects is very time-consuming. The developed approach proposes a partially automated procedure for the sample creation and avoids manual labelling of rooftop photovoltaic (PV) systems. By combining address data of existing rooftop PV systems from the German Plant Register, the Georeferenced Address Data and the Official House Surroundings Germany, a partially automated generation of training samples is achieved. Using a selection of 100,000 automatically generated samples, a network using a RetinaNet-based architecture combining ResNet101, a feature pyramid network, a classification and a regression network is trained, applied on a large area and post-filtered by intersection with additional automatically identified locations of existing rooftop PV systems. Based on a proof-of-concept application, a second network is trained with the filtered selection of approximately 51,000 training samples. In two independent test applications using high-resolution aerial images of Saarland in Germany, buildings with PV systems are detected with a precision of at least 92.77 and a recall of 84.47. [ABSTRACT FROM AUTHOR]

The prolonged drought of recent years combined with the steadily increasing bark beetle infestation (Ips typographus) is causing enormous damage in Germany's spruce forests. This preliminary study investigates whether early spruce infestation by the bark beetle (green attack) can be detected using indices based on airborne spatial high-resolution (0.3 m) hyperspectral data and field spectrometer measurements. In particular, a new hyperspectral index based on airborne data has been defined and compared with other common indices for bark beetle detection. It shows a very high overall accuracy (OAA = 98.84%) when validated with field data. Field measurements and a long-term validation in a second study area serve the validation of the robustness and transferability of the index to other areas. In comparison with commonly used indices, the defined index has the ability to detect a larger proportion of infested spruces in the green attack phase (60% against 20% for commonly used indices). This index confirms the high potential of the red-edge domain to distinguish infested spruces at an early stage. Overall, our index has great potential for forest preservation strategies aimed at the detection of infested spruces in order to mitigate the outbreaks. [ABSTRACT FROM AUTHOR]

Wetland environments, with their excellent conservation conditions, provide geoarchaeological archives of past human activities. However, the subsurface soil is difficult to access due to high groundwater tables, unstable sediments, and the high cost of excavation. In this study, we present a ground-based non- and minimal-invasive prospection concept adapted to the conditions of wetlands. We investigated the Fossa Carolina in South Germany, a canal that was intended in 792/793 AD by Charlemagne to bridge the Central European Watershed. Although the resulting Carolingian banks and the fairway with wooden revetments are very imposing, archaeological traces of off-site construction activities have not been identified hitherto. Based on a geophysically surveyed intensive linear magnetic anomaly parallel to the Carolingian canal, we aimed to prove potential off-site traces of Carolingian construction activities. In this context, we built up a high-resolution cross-section using highly depth-accurate direct push sensing and ground-truthing. Our results showed the exact geometry of the canal and the former banks. Thus, the magnetic mass anomaly could be clearly located between the buried organic-rich topsoil and the Carolingian banks. The thermoluminescence dating showed that the position of the magnetic mass anomaly reflected Carolingian activities during the construction phases, specifically due to heat exposure. Moreover, we found hints of the groundwater supply to the 5-metre wide navigable fairway. [ABSTRACT FROM AUTHOR]

An accurate understanding of urban objects is critical for urban modeling, intelligent infrastructure planning and city management. The semantic segmentation of light detection and ranging (LiDAR) point clouds is a fundamental approach for urban scene analysis. Over the last years, several methods have been developed to segment urban furniture with point clouds. However, the traditional processing of large amounts of spatial data has become increasingly costly, both time-wise and financially. Recently, deep learning (DL) techniques have been increasingly used for 3D segmentation tasks. Yet, most of these deep neural networks (DNNs) were conducted on benchmarks. It is, therefore, arguable whether DL approaches can achieve the state-of-the-art performance of 3D point clouds segmentation in real-life scenarios. In this research, we apply an adapted DNN (ARandLA-Net) to directly process large-scale point clouds. In particular, we develop a new paradigm for training and validation, which presents a typical urban scene in central Europe (Munzingen, Freiburg, Baden-Württemberg, Germany). Our dataset consists of nearly 390 million dense points acquired by Mobile Laser Scanning (MLS), which has a rather larger quantity of sample points in comparison to existing datasets and includes meaningful object categories that are particular to applications for smart cities and urban planning. We further assess the DNN on our dataset and investigate a number of key challenges from varying aspects, such as data preparation strategies, the advantage of color information and the unbalanced class distribution in the real world. The final segmentation model achieved a mean Intersection-over-Union (mIoU) score of 54.4% and an overall accuracy score of 83.9%. Our experiments indicated that different data preparation strategies influenced the model performance. Additional RGB information yielded an approximately 4% higher mIoU score. Our results also demonstrate that the use of weighted cross-entropy with inverse square root frequency loss led to better segmentation performance than when other losses were considered. [ABSTRACT FROM AUTHOR]

Land surface models (LSMs) simulate water and energy cycles at the atmosphere–soil interface, however, the physical processes in the subsurface are typically oversimplified and lateral water movement is neglected. Here, a cross-evaluation of land surface model results (with and without lateral flow processes), the National Aeronautics and Space Administration (NASA) Soil Moisture Active/Passive (SMAP) mission soil moisture product, and cosmic-ray neutron sensor (CRNS) measurements is carried out over a temperate climate region with cropland and forests over western Germany. Besides a traditional land surface model (the Community Land Model (CLM) version 3.5), a coupled land surface-subsurface model (CLM-ParFlow) is applied. Compared to CLM stand-alone simulations, the coupled CLM-ParFlow model considered both vertical and lateral water movement. In addition to standard validation metrics, a triple collocation (TC) analysis has been performed to help understanding the random error variances of different soil moisture datasets. In this study, it is found that the three soil moisture datasets are consistent. The coupled and uncoupled model simulations were evaluated at CRNS sites and the coupled model simulations showed less bias than the CLM-standalone model (−0.02 cm3 cm−3 vs. 0.07 cm3 cm−3), similar random errors, but a slightly smaller correlation with the measurements (0.67 vs. 0.71). The TC-analysis showed that CLM-ParFlow reproduced better soil moisture dynamics than CLM stand alone and with a higher signal-to-noise ratio. This suggests that the representation of subsurface physics is of major importance in land surface modeling and that coupled land surface-subsurface modeling is of high interest. [ABSTRACT FROM AUTHOR]

By 2050, two-third of the world's population will live in cities. In this study, we develop a framework for analyzing urban growth-related imperviousness in North Rhine-Westphalia (NRW) from the 1980s to date using Landsat data. For the baseline 2017-time step, official geodata was extracted to generate labelled data for ten classes, including three classes representing low, middle, and high level of imperviousness. We used the output of the 2017 classification and information based on radiometric bi-temporal change detection for retrospective classification. Besides spectral bands, we calculated several indices and various temporal composites, which were used as an input for Random Forest classification. The results provide information on three imperviousness classes with accuracies exceeding 75%. According to our results, the imperviousness areas grew continuously from 1985 to 2017, with a high imperviousness area growth of more than 167,000 ha, comprising around 30% increase. The information on the expansion of urban areas was integrated with population dynamics data to estimate the progress towards SDG 11. With the intensity analysis and the integration of population data, the spatial heterogeneity of urban expansion and population growth was analysed, showing that the urban expansion rates considerably excelled population growth rates in some regions in NRW. The study highlights the applicability of earth observation data for accurately quantifying spatio-temporal urban dynamics for sustainable urbanization and targeted planning. [ABSTRACT FROM AUTHOR]

With the development of low-cost, lightweight, integrated thermal infrared-multispectral cameras, unmanned aerial systems (UAS) have recently become a flexible complement to eddy covariance (EC) station methods for mapping surface energy fluxes of vegetated areas. These sensors facilitate the measurement of several site characteristics in one flight (e.g., radiometric temperature, vegetation indices, vegetation structure), which can be used alongside in-situ meteorology data to provide spatially-distributed estimates of energy fluxes at very high resolution. Here we test one such system (MicaSense Altum) integrated into an off-the-shelf long-range vertical take-off and landing (VTOL) unmanned aerial vehicle, and apply and evaluate our method by comparing flux estimates with EC-derived data, with specific and novel focus on heterogeneous vegetation communities at three different sites in Germany. Firstly, we present an empirical method for calibrating airborne radiometric temperature in standard units (K) using the Altum multispectral and thermal infrared instrument. Then we provide detailed methods using the two-source energy balance model (TSEB) for mapping net radiation (Rn), sensible (H), latent (LE) and ground (G) heat fluxes at <0.82 m resolution, with root mean square errors (RMSE) less than 45, 37, 39, 52 W m−2 respectively. Converting to radiometric temperature using our empirical method resulted in a 19% reduction in RMSE across all fluxes compared to the standard conversion equation provided by the manufacturer. Our results show the potential of this UAS for mapping energy fluxes at high resolution over large areas in different conditions, but also highlight the need for further surveys of different vegetation types and land uses. [ABSTRACT FROM AUTHOR]

The annual certification of organic agriculture products includes an in situ inspection of the fields declared organic. This inspection is more difficult, time-consuming, and costly for large farms or in production regions located in remote areas. The global objective of this research is to assess how spatial remote sensing may support the organic crop certification process by developing a method that would enable certification bodies to target for priority in situ control crop fields declared as organic but that would show on satellite imagery an appearance closer to conventional fields. For this purpose, the ability of multispectral satellite images to discriminate between organic and conventional maize fields was assessed through the use of a set of four satellite images of different spatial and spectral resolutions acquired at different crop growth stages over a large number of maize fields (32) that are part of an operational farm in Germany. In support of this main objective, a set of in situ measurements (leaf hyperspectral reflectance, chlorophyll, and nitrogen content and dry matter percentage, crop canopy cover, height, wet biomass and dry matter percentage, soil chemical composition) was conducted to characterize the nature of the biochemical and biophysical differences between organic and conventional maize fields. The results of this research showed that highly significant biochemical and biophysical differences between a large number of organic and conventional maize fields may exist at identified crop growth stages and that these differences may be sufficiently pronounced to enable the complete discrimination between crop management modes using satellite images issued from quite common multispectral satellite sensors through the use of spectral or spatial heterogeneity indices. These results are very encouraging and suggest, for the first time, that satellite images could effectively support the organic maize certification process. [ABSTRACT FROM AUTHOR]

Grapevine yellows (GY) are serious phytoplasma-caused diseases affecting viticultural areas worldwide. At present, two principal agents of GY are known to infest grapevines in Germany: Bois noir (BN) and Palatinate grapevine yellows (PGY). Disease management is mostly based on prophylactic measures as there are no curative in-field treatments available. In this context, sensor-based disease detection could be a useful tool for winegrowers. Therefore, hyperspectral imaging (400–2500 nm) was applied to identify phytoplasma-infected greenhouse plants and shoots collected in the field. Disease detection models (Radial-Basis Function Network) have successfully been developed for greenhouse plants of two white grapevine varieties infected with BN and PGY. Differentiation of symptomatic and healthy plants was possible reaching satisfying classification accuracies of up to 96%. However, identification of BN-infected but symptomless vines was difficult and needs further investigation. Regarding shoots collected in the field from different red and white varieties, correct classifications of up to 100% could be reached using a Multi-Layer Perceptron Network for analysis. Thus, hyperspectral imaging seems to be a promising approach for the detection of different GY. Moreover, the 10 most important wavelengths were identified for each disease detection approach, many of which could be found between 400 and 700 nm and in the short-wave infrared region (1585, 2135, and 2300 nm). These wavelengths could be used further to develop multispectral systems. [ABSTRACT FROM AUTHOR]

The monitoring of land cover and land use change is critical for assessing the provision of ecosystem services. One of the sources for long-term land cover change quantification is through the classification of historical and/or current maps. Little research has been done on historical maps using Object-Based Image Analysis (OBIA). This study applied an object-based classification using eCognition tool for analyzing the land cover based on historical maps in the Main river catchment, Upper Franconia, Germany. This allowed land use change analysis between the 1850s and 2015, a time span which covers the phase of industrialization of landscapes in central Europe. The results show a strong increase in urban area by 2600%, a severe loss of cropland (−24%), a moderate reduction in meadows (−4%), and a small gain in forests (+4%). The method proved useful for the application on historical maps due to the ability of the software to create semantic objects. The confusion matrix shows an overall accuracy of 82% for the automatic classification compared to manual reclassification considering all 17 sample tiles. The minimum overall accuracy was 65% for historical maps of poor quality and the maximum was 91% for very high-quality ones. Although accuracy is between high and moderate, coarse land cover patterns in the past and trends in land cover change can be analyzed. We conclude that such long-term analysis of land cover is a prerequisite for quantifying long-term changes in ecosystem services. [ABSTRACT FROM AUTHOR]

Chlorophyll content, as the primary pigment driving photosynthesis, is directly affected by many natural and anthropogenic disturbances and stressors. Accurate and timely estimation of canopy chlorophyll content (CCC) is essential for effective ecosystem monitoring to allow for successful management interventions to occur. Hyperspectral remote sensing offers the possibility to accurately estimate and map canopy chlorophyll content. In the past, research has predominantly focused on the use of hyperspectral data on canopy chlorophyll content retrieval of crops and grassland ecosystems. Therefore, in this study, a temperate mixed forest, the Bavarian Forest National Park in Germany, was chosen as the study site. We compared different statistical models (narrowband vegetation indices (VIs), partial least squares regression (PLSR) and random forest (RF)) in their accuracy to predict CCC using airborne hyperspectral data. The airborne hyperspectral imagery was acquired by the AisaFenix sensor (623 bands; 3.5 nm spectral resolution in the visible near-infrared (VNIR) region, and 12 nm spectral resolution in the shortwave infrared (SWIR) region; 3 m spatial resolution) on July 6, 2017. In situ leaf chlorophyll content and leaf area index measurements were sampled from the upper canopy of coniferous, mixed, and deciduous forest stands in July and August 2017. The study yielded the highest retrieval accuracies with PLSR (root mean square error (RMSE) = 0.25 g/m2, R2 = 0.66). It further indicated specific spectral regions within the visible (390–400 nm and 470–540 nm), red edge (680–780 nm), near-infrared (1050–1100 nm) and shortwave infrared regions (2000–2270 nm) that were important for CCC retrieval. The results showed that forest CCC can be mapped with relatively high accuracies using image spectroscopy. [ABSTRACT FROM AUTHOR]

Forests in Germany cover around 11.4 million hectares and, thus, a share of 32% of Germany's surface area. Therefore, forests shape the character of the country's cultural landscape. Germany's forests fulfil a variety of functions for nature and society, and also play an important role in the context of climate levelling. Climate change, manifested via rising temperatures and current weather extremes, has a negative impact on the health and development of forests. Within the last five years, severe storms, extreme drought, and heat waves, and the subsequent mass reproduction of bark beetles have all seriously affected Germany's forests. Facing the current dramatic extent of forest damage and the emerging long-term consequences, the effort to preserve forests in Germany, along with their diversity and productivity, is an indispensable task for the government. Several German ministries have and plan to initiate measures supporting forest health. Quantitative data is one means for sound decision-making to ensure the monitoring of the forest and to improve the monitoring of forest damage. In addition to existing forest monitoring systems, such as the federal forest inventory, the national crown condition survey, and the national forest soil inventory, systematic surveys of forest condition and vulnerability at the national scale can be expanded with the help of a satellite-based earth observation. In this review, we analysed and categorized all research studies published in the last 20 years that focus on the remote sensing of forests in Germany. For this study, 166 citation indexed research publications have been thoroughly analysed with respect to publication frequency, location of studies undertaken, spatial and temporal scale, coverage of the studies, satellite sensors employed, thematic foci of the studies, and overall outcomes, allowing us to identify major research and geoinformation product gaps. [ABSTRACT FROM AUTHOR]

Undocumented building constructions are buildings or stories that were built years ago, but are missing in the official digital cadastral maps (DFK). The detection of undocumented building constructions is essential to urban planning and monitoring. The state of Bavaria, Germany, uses two semi-automatic detection methods for this task that suffer from a high false alarm rate. To solve this problem, we propose a novel framework to detect undocumented building constructions using a Convolutional Neural Network (CNN) and official geodata, including high resolution optical data and the Normalized Digital Surface Model (nDSM). More specifically, an undocumented building pixel is labeled as "building" by the CNN but does not overlap with a building polygon of the DFK. The class of old or new undocumented building can be further separated when a Temporal Digital Surface Model (tDSM) is introduced in the stage of decision fusion. In a further step, undocumented story construction is detected as the pixels that are "building" in both DFK and predicted results from CNN, but shows a height deviation from the tDSM. By doing so, we have produced a seamless map of undocumented building constructions for one-quarter of the state of Bavaria, Germany at a spatial resolution of 0.4 m, which has proved that our framework is robust to detect undocumented building constructions at large-scale. Considering that the official geodata exploited in this research is advantageous because of its high quality and large coverage, a transferability analysis experiment is also designed in our research to investigate the sampling strategies for building detection at large-scale. Our results indicate that building detection results in unseen areas at large-scale can be improved when training samples are collected from different districts. In an area where training samples are available, local training sampless collection and training can save much time and effort. [ABSTRACT FROM AUTHOR]

Soil degradation is a major threat for European soils and therefore, the European Commission recommends intensifying research on soil monitoring to capture changes over time and space. Imaging spectroscopy is a promising technique to create spatially accurate topsoil maps based on hyperspectral remote sensing data. We tested the application of a local partial least squares regression (PLSR) to airborne HySpex and simulated satellite EnMAP (Environmental Mapping and Analysis Program) data acquired in north-eastern Germany to quantify the soil organic carbon (SOC) content. The approach consists of two steps: (i) the local PLSR uses the European LUCAS (land use/cover area frame statistical survey) Soil database to quantify the SOC content for soil samples from the study site in order to avoid the need for wet chemistry analyses, and subsequently (ii) a remote sensing model is calibrated based on the local PLSR SOC results and the corresponding image spectra. This two-step approach is compared to a traditional PLSR approach using measured SOC contents from local samples. The prediction accuracy is high for the laboratory model in the first step with R2 = 0.86 and RPD = 2.77. The HySpex airborne prediction accuracy of the traditional approach is high and slightly superior to the two-step approach (traditional: R2 = 0.78, RPD = 2.19; two-step: R2 = 0.67, RPD = 1.79). Applying the two-step approach to simulated EnMAP imagery leads to a lower but still reasonable prediction accuracy (traditional: R2 = 0.77, RPD = 2.15; two-step: R2 = 0.48, RPD = 1.41). The two-step models of both sensors were applied to all bare soils of the respective images to produce SOC maps. This local PLSR approach, based on large scale soil spectral libraries, demonstrates an alternative to SOC measurements from wet chemistry of local soil samples. It could allow for repeated inexpensive SOC mapping based on satellite remote sensing data as long as spectral measurements of a few local samples are available for model calibration. [ABSTRACT FROM AUTHOR]

Image segmentation is a cost-effective way to obtain information about the sizes and structural composition of agricultural parcels in an area. To accurately obtain such information, the parameters of the segmentation algorithm ought to be optimized using supervised or unsupervised methods. The difficulty in obtaining reference data makes unsupervised methods indispensable. In this study, we evaluated an existing unsupervised evaluation metric that minimizes a global score (GS), which is computed by summing up the intra-segment uniformity and inter-segment dissimilarity within a segmentation output. We modified this metric and proposed a new metric that uses absolute difference to compute the GS. We compared this proposed metric with the existing metric in two optimization approaches based on the Multiresolution Segmentation (MRS) algorithm to optimally delineate agricultural parcels from Sentinel-2 images in Lower Saxony, Germany. The first approach searches for optimal scale while keeping shape and compactness constant, while the second approach uses Bayesian optimization to optimize the three main parameters of the MRS algorithm. Based on a reference data of agricultural parcels, the optimal segmentation result of each optimization approach was evaluated by calculating the quality rate, over-segmentation, and under-segmentation. For both approaches, our proposed metric outperformed the existing metric in different agricultural landscapes. The proposed metric identified optimal segmentations that were less under-segmented compared to the existing metric. A comparison of the optimal segmentation results obtained in this study to existing benchmark results generated via supervised optimization showed that the unsupervised Bayesian optimization approach based on our proposed metric can potentially be used as an alternative to supervised optimization, particularly in geographic regions where reference data is unavailable or an automated evaluation system is sought. [ABSTRACT FROM AUTHOR]

Agricultural vegetation development and harvest date monitoring over large areas requires frequent remote sensing observations. In regions with persistent cloud coverage during the vegetation season this is only feasible with active systems, such as SAR, and is limited for optical data. To date, optical remote sensing vegetation indices are more frequently used to monitor agricultural vegetation status because they are easily processed, and the characteristics are widely known. This study evaluated the correlations of three Sentinel-2 optical indices with Sentinel-1 SAR indices over agricultural areas to gain knowledge about their relationship. We compared Sentinel-2 Normalized Difference Vegetation Index, Normalized Difference Water Index, and Plant Senescence Radiation Index with Sentinel-1 SAR VV and VH backscatter, VH/VV ratio, and Sentinel-1 Radar Vegetation Index. The study was conducted on 22 test sites covering approximately 35,000 ha of four different main European agricultural land use types, namely grassland, maize, spring barley, and winter wheat, in Lower Saxony, Germany, in 2018. We investigated the relationship between Sentinel-1 and Sentinel-2 indices for each land use type considering three phenophases (growing, green, senescence). The strength of the correlations of optical and SAR indices differed among land use type and phenophase. There was no generic correlation between optical and SAR indices in our study. However, when the data were split by land use types and phenophases, the correlations increased remarkably. Overall, the highest correlations were found for the Radar Vegetation Index and VH backscatter. Correlations for grassland were lower than for the other land use types. Adding auxiliary data to a multiple linear regression analysis revealed that, in addition to land use type and phenophase information, the lower quartile and median SAR values per field, and a spatial variable, improved the models. Other auxiliary data retrieved from a digital elevation model, Sentinel-1 orbit direction, soil type information, and other SAR values had minor impacts on the model performance. In conclusion, despite the different nature of the signal generation, there were distinct relationships between optical and SAR indices which were independent of environmental variables but could be stratified by land use type and phenophase. These relationships showed similar patterns across different test sites. However, a regional clustering of landscapes would significantly improve the relationships. [ABSTRACT FROM AUTHOR]

Field mapping and information on agricultural landscapes is of increasing importance for many applications. Monitoring schemes and national cadasters provide a rich source of information but their maintenance and regular updating is costly and labor-intensive. Automatized mapping of fields based on remote sensing imagery may aid in this task and allow for a faster and more regular observation. Although remote sensing has seen extensive use in agricultural research topics, such as plant health monitoring, crop type classification, yield prediction, and irrigation, field delineation and extraction has seen comparatively little research interest. In this study, we present a field boundary detection technique based on deep learning and a variety of image features, and combine it with the graph-based growing contours (GGC) method to extract agricultural fields in a study area in northern Germany. The boundary detection step only requires red, green, and blue (RGB) data and is therefore largely independent of the sensor used. We compare different image features based on color and luminosity information and evaluate their usefulness for the task of field boundary detection. A model based on texture metrics, gradient information, Hessian matrix eigenvalues, and local statistics showed good results with accuracies up to 88.2%, an area under the ROC curve (AUC) of up to 0.94, and F1 score of up to 0.88. The exclusive use of these universal image features may also facilitate transferability to other regions. We further present modifications to the GGC method intended to aid in upscaling of the method through process acceleration with a minimal effect on results. We combined the boundary detection results with the GGC method for field polygon extraction. Results were promising, with the new GGC version performing similarly or better than the original version while experiencing an acceleration of 1.3× to 2.3× on different subsets and input complexities. Further research may explore other applications of the GGC method outside agricultural remote sensing and field extraction. [ABSTRACT FROM AUTHOR]

This study compares the performance of the five widely used crop growth models (CGMs): World Food Studies (WOFOST), Coalition for Environmentally Responsible Economies (CERES)-Wheat, AquaCrop, cropping systems simulation model (CropSyst), and the semi-empiric light use efficiency approach (LUE) for the prediction of winter wheat biomass on the Durable Environmental Multidisciplinary Monitoring Information Network (DEMMIN) test site, Germany. The study focuses on the use of remote sensing (RS) data, acquired in 2015, in CGMs, as they offer spatial information on the actual conditions of the vegetation. Along with this, the study investigates the data fusion of Landsat (30 m) and Moderate Resolution Imaging Spectroradiometer (MODIS) (500 m) data using the spatial and temporal reflectance adaptive reflectance fusion model (STARFM) fusion algorithm. These synthetic RS data offer a 30-m spatial and one-day temporal resolution. The dataset therefore provides the necessary information to run CGMs and it is possible to examine the fine-scale spatial and temporal changes in crop phenology for specific fields, or sub sections of them, and to monitor crop growth daily, considering the impact of daily climate variability. The analysis includes a detailed comparison of the simulated and measured crop biomass. The modelled crop biomass using synthetic RS data is compared to the model outputs using the original MODIS time series as well. On comparison with the MODIS product, the study finds the performance of CGMs more reliable, precise, and significant with synthetic time series. Using synthetic RS data, the models AquaCrop and LUE, in contrast to other models, simulate the winter wheat biomass best, with an output of high R2 (>0.82), low RMSE (<600 g/m2) and significant p-value (<0.05) during the study period. However, inputting MODIS data makes the models underperform, with low R2 (<0.68) and high RMSE (>600 g/m2). The study shows that the models requiring fewer input parameters (AquaCrop and LUE) to simulate crop biomass are highly applicable and precise. At the same time, they are easier to implement than models, which need more input parameters (WOFOST and CERES-Wheat). [ABSTRACT FROM AUTHOR]

This study investigates how short-term lidar measurements can be used in combination with a mast measurement to improve vertical extrapolation of wind speed. Several methods are developed and analyzed for their performance in estimating the mean wind speed, the wind speed distribution, and the energy yield of an idealized wind turbine at the target height of the extrapolation. These methods range from directly using the wind shear of the short-term measurement to a classification approach based on commonly available environmental parameters using linear regression. The extrapolation strategies are assessed using data of ten wind profiles up to 200 m measured at different sites in Germany. Different mast heights and extrapolation distances are investigated. The results show that, using an appropriate extrapolation strategy, even a very short-term lidar measurement can significantly reduce the uncertainty in the vertical extrapolation of wind speed. This observation was made for short as well as for very large extrapolation distances. Among the investigated methods, the linear regression approach yielded better results than the other methods. Integrating environmental variables into the extrapolation procedure further increased the performance of the linear regression approach. Overall, the extrapolation error in (theoretical) energy yield was decreased by around 50% to 70% on average for a lidar measurement of approximately one to two months depending on the extrapolation height and distance. The analysis of seasonal patterns revealed that appropriate extrapolation strategies can also significantly reduce the seasonal bias that is connected to the season during which the short-term measurement is performed. [ABSTRACT FROM AUTHOR]

Satellite and reanalysis precipitation products perform poorly over regions with low-density ground observation networks. In order to improve space-dependent parameterization of precipitation estimation models in data-scarce environments, the delineation boundaries of precipitation regimes should be accurately identified. Existing approaches to characterize precipitation regimes by seasonal or other climatological properties do not account for small scale spatial-temporal variability. Precipitation time series can be used to account for this small-scale variability in regime classification. Unfortunately, precipitation products with global coverage perform poorly at small time scales over data scarce regions. A methodology of using satellite-based cloud-top temperature (CTT) time series as a proxy of precipitation time series for precipitation regime classification was developed, and its potential and uncertainty were analyzed. A precipitation regime in this study was defined on the basis of characteristic small-scale temporal distribution and variability of precipitation at a given place. Dynamic time warping was used to calculate the distance between two time series. Criteria to select the optimal temporal scale of time series for clustering and the number of clusters were also developed. The method was validated over Germany and applied to Tanzania, characterized by complex climatology and low density ground observations. This approach was evaluated against precipitation regime classification based on a satellite precipitation product. Results show that CTT outcompetes satellite-based precipitation for classification of precipitation regime classification. The CTT-based classification can be used as precursor to spatially adapted precipitation estimation algorithms where parameters are calibrated by gauge data or other ground-based precipitation observations, and parameterization can be used for satellite-precipitation estimates, precipitation forecasts in numerical or stochastic weather models, etc. [ABSTRACT FROM AUTHOR]