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Prof. Natascha Oppelt
Kiel University

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0 Earth Observation
0 Hyperspectral Imaging
0 Water Quality Modeling
0 water quality monitoring

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agricultural
Bathymetry
Arctic sea ice
Earth Observation

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Journal article
Published: 09 March 2021 in Remote Sensing of Environment
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Atmospheric correction over inland and coastal waters is one of the major remaining challenges in aquatic remote sensing, often hindering the quantitative retrieval of biogeochemical variables and analysis of their spatial and temporal variability within aquatic environments. The Atmospheric Correction Intercomparison Exercise (ACIX-Aqua), a joint NASA – ESA activity, was initiated to enable a thorough evaluation of eight state-of-the-art atmospheric correction (AC) processors available for Landsat-8 and Sentinel-2 data processing. Over 1000 radiometric matchups from both freshwaters (rivers, lakes, reservoirs) and coastal waters were utilized to examine the quality of derived aquatic reflectances (ρ̂w). This dataset originated from two sources: Data gathered from the international scientific community (henceforth called Community Validation Database, CVD), which captured predominantly inland water observations, and the Ocean Color component of AERONET measurements (AERONET-OC), representing primarily coastal ocean environments. This volume of data permitted the evaluation of the AC processors individually (using all the matchups) and comparatively (across seven different Optical Water Types, OWTs) using common matchups. We found that the performance of the AC processors differed for CVD and AERONET-OC matchups, likely reflecting inherent variability in aquatic and atmospheric properties between the two datasets. For the former, the median errors in ρ̂w560 and ρ̂w664 were found to range from 20 to 30% for best-performing processors. Using the AERONET-OC matchups, our performance assessments showed that median errors within the 15–30% range in these spectral bands may be achieved. The largest uncertainties were associated with the blue bands (25 to 60%) for best-performing processors considering both CVD and AERONET-OC assessments. We further assessed uncertainty propagation to the downstream products such as near-surface concentration of chlorophyll-a (Chla) and Total Suspended Solids (TSS). Using satellite matchups from the CVD along with in situ Chla and TSS, we found that 20–30% uncertainties in ρ̂w490≤λ≤743nm yielded 25–70% uncertainties in derived Chla and TSS products for top-performing AC processors. We summarize our results using performance matrices guiding the satellite user community through the OWT-specific relative performance of AC processors. Our analysis stresses the need for better representation of aerosols, particularly absorbing ones, and improvements in corrections for sky- (or sun-) glint and adjacency effects, in order to achieve higher quality downstream products in freshwater and coastal ecosystems.

ACS Style

Nima Pahlevan; Antoine Mangin; Sundarabalan V. Balasubramanian; Brandon Smith; Krista Alikas; Kohei Arai; Claudio Barbosa; Simon Bélanger; Caren Binding; Mariano Bresciani; Claudia Giardino; Daniela Gurlin; Yongzhen Fan; Tristan Harmel; Peter Hunter; Joji Ishikaza; Susanne Kratzer; Moritz K. Lehmann; Martin Ligi; Ronghua Ma; François-Régis Martin-Lauzer; Leif Olmanson; Natascha Oppelt; Yanqun Pan; Steef Peters; Nathalie Reynaud; Lino A. Sander de Carvalho; Stefan Simis; Evangelos Spyrakos; François Steinmetz; Kerstin Stelzer; Sindy Sterckx; Thierry Tormos; Andrew Tyler; Quinten Vanhellemont; Mark Warren. ACIX-Aqua: A global assessment of atmospheric correction methods for Landsat-8 and Sentinel-2 over lakes, rivers, and coastal waters. Remote Sensing of Environment 2021, 258, 112366 .

AMA Style

Nima Pahlevan, Antoine Mangin, Sundarabalan V. Balasubramanian, Brandon Smith, Krista Alikas, Kohei Arai, Claudio Barbosa, Simon Bélanger, Caren Binding, Mariano Bresciani, Claudia Giardino, Daniela Gurlin, Yongzhen Fan, Tristan Harmel, Peter Hunter, Joji Ishikaza, Susanne Kratzer, Moritz K. Lehmann, Martin Ligi, Ronghua Ma, François-Régis Martin-Lauzer, Leif Olmanson, Natascha Oppelt, Yanqun Pan, Steef Peters, Nathalie Reynaud, Lino A. Sander de Carvalho, Stefan Simis, Evangelos Spyrakos, François Steinmetz, Kerstin Stelzer, Sindy Sterckx, Thierry Tormos, Andrew Tyler, Quinten Vanhellemont, Mark Warren. ACIX-Aqua: A global assessment of atmospheric correction methods for Landsat-8 and Sentinel-2 over lakes, rivers, and coastal waters. Remote Sensing of Environment. 2021; 258 ():112366.

Chicago/Turabian Style

Nima Pahlevan; Antoine Mangin; Sundarabalan V. Balasubramanian; Brandon Smith; Krista Alikas; Kohei Arai; Claudio Barbosa; Simon Bélanger; Caren Binding; Mariano Bresciani; Claudia Giardino; Daniela Gurlin; Yongzhen Fan; Tristan Harmel; Peter Hunter; Joji Ishikaza; Susanne Kratzer; Moritz K. Lehmann; Martin Ligi; Ronghua Ma; François-Régis Martin-Lauzer; Leif Olmanson; Natascha Oppelt; Yanqun Pan; Steef Peters; Nathalie Reynaud; Lino A. Sander de Carvalho; Stefan Simis; Evangelos Spyrakos; François Steinmetz; Kerstin Stelzer; Sindy Sterckx; Thierry Tormos; Andrew Tyler; Quinten Vanhellemont; Mark Warren. 2021. "ACIX-Aqua: A global assessment of atmospheric correction methods for Landsat-8 and Sentinel-2 over lakes, rivers, and coastal waters." Remote Sensing of Environment 258, no. : 112366.

Review
Published: 09 March 2021 in Insects
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Recently, locust outbreaks around the world have destroyed agricultural and natural vegetation and caused massive damage endangering food security. Unusual heavy rainfalls in habitats of the desert locust (Schistocerca gregaria) and lack of monitoring due to political conflicts or inaccessibility of those habitats lead to massive desert locust outbreaks and swarms migrating over the Arabian Peninsula, East Africa, India and Pakistan. At the same time, swarms of the Moroccan locust (Dociostaurus maroccanus) in some Central Asian countries and swarms of the Italian locust (Calliptamus italicus) in Russia and China destroyed crops despite developed and ongoing monitoring and control measurements. These recent events underline that the risk and damage caused by locust pests is as present as ever and affects 100 million of human lives despite technical progress in locust monitoring, prediction and control approaches. Remote sensing has become one of the most important data sources in locust management. Since the 1980s, remote sensing data and applications have accompanied many locust management activities and contributed to an improved and more effective control of locust outbreaks and plagues. Recently, open-access remote sensing data archives as well as progress in cloud computing provide unprecedented opportunity for remote sensing-based locust management and research. Additionally, unmanned aerial vehicle (UAV) systems bring up new prospects for a more effective and faster locust control. Nevertheless, the full capacity of available remote sensing applications and possibilities have not been exploited yet. This review paper provides a comprehensive and quantitative overview of international research articles focusing on remote sensing application for locust management and research. We reviewed 110 articles published over the last four decades, and categorized them into different aspects and main research topics to summarize achievements and gaps for further research and application development. The results reveal a strong focus on three species—the desert locust, the migratory locust (Locusta migratoria), and the Australian plague locust (Chortoicetes terminifera)—and corresponding regions of interest. There is still a lack of international studies for other pest species such as the Italian locust, the Moroccan locust, the Central American locust (Schistocerca piceifrons), the South American locust (Schistocerca cancellata), the brown locust (Locustana pardalina) and the red locust (Nomadacris septemfasciata). In terms of applied sensors, most studies utilized Advanced Very-High-Resolution Radiometer (AVHRR), Satellite Pour l’Observation de la Terre VEGETATION (SPOT-VGT), Moderate-Resolution Imaging Spectroradiometer (MODIS) as well as Landsat data focusing mainly on vegetation monitoring or land cover mapping. Application of geomorphological metrics as well as radar-based soil moisture data is comparably rare despite previous acknowledgement of their importance for locust outbreaks. Despite great advance and usage of available remote sensing resources, we identify several gaps and potential for future research to further improve the understanding and capacities of the use of remote sensing in supporting locust outbreak- research and management.

ACS Style

Igor Klein; Natascha Oppelt; Claudia Kuenzer. Application of Remote Sensing Data for Locust Research and Management—A Review. Insects 2021, 12, 233 .

AMA Style

Igor Klein, Natascha Oppelt, Claudia Kuenzer. Application of Remote Sensing Data for Locust Research and Management—A Review. Insects. 2021; 12 (3):233.

Chicago/Turabian Style

Igor Klein; Natascha Oppelt; Claudia Kuenzer. 2021. "Application of Remote Sensing Data for Locust Research and Management—A Review." Insects 12, no. 3: 233.

Journal article
Published: 27 November 2020 in Remote Sensing
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The warming climate is threatening to alter inland water resources on a global scale. Within all waterbody types, lake and river systems are vital not only for natural ecosystems but, also, for human society. Snowmelt phenology is also altered by global warming, and snowmelt is the primary water supply source for many river and lake systems around the globe. Hence, (1) monitoring snowmelt conditions, (2) tracking the dynamics of snowmelt-influenced river and lake systems, and (3) quantifying the causal effect of snowmelt conditions on these waterbodies are critical to understand the cryo-hydrosphere interactions under climate change. Previous studies utilized in-situ or multispectral sensors to track either the surface areas or water levels of waterbodies, which are constrained to small-scale regions and limited by cloud cover, respectively. On the contrary, in the present study, we employed the latest Sentinel-1 synthetic aperture radar (SAR) and Sentinel-3 altimetry data to grant a high-resolution, cloud-free, and illumination-independent comprehensive inland water dynamics monitoring strategy. Moreover, in contrast to previous studies utilizing in-house algorithms, we employed freely available cloud-based services to ensure a broad applicability with high efficiency. Based on altimetry and SAR data, the water level and the water-covered extent (WCE) (surface area of lakes and the flooded area of rivers) can be successfully measured. Furthermore, by fusing the water level and surface area information, for Lake Urmia, we can estimate the hypsometry and derive the water volume change. Additionally, for the Brahmaputra River, the variations of both the water level and the flooded area can be tracked. Last, but not least, together with the wet snow cover extent (WSCE) mapped with SAR imagery, we can analyze the influence of snowmelt conditions on water resource variations. The distributed lag model (DLM) initially developed in the econometrics discipline was employed, and the lagged causal effect of snowmelt conditions on inland water resources was eventually assessed.

ACS Style

Ya-Lun Tsai; Igor Klein; Andreas Dietz; Natascha Oppelt. Monitoring Large-Scale Inland Water Dynamics by Fusing Sentinel-1 SAR and Sentinel-3 Altimetry Data and by Analyzing Causal Effects of Snowmelt. Remote Sensing 2020, 12, 3896 .

AMA Style

Ya-Lun Tsai, Igor Klein, Andreas Dietz, Natascha Oppelt. Monitoring Large-Scale Inland Water Dynamics by Fusing Sentinel-1 SAR and Sentinel-3 Altimetry Data and by Analyzing Causal Effects of Snowmelt. Remote Sensing. 2020; 12 (23):3896.

Chicago/Turabian Style

Ya-Lun Tsai; Igor Klein; Andreas Dietz; Natascha Oppelt. 2020. "Monitoring Large-Scale Inland Water Dynamics by Fusing Sentinel-1 SAR and Sentinel-3 Altimetry Data and by Analyzing Causal Effects of Snowmelt." Remote Sensing 12, no. 23: 3896.

Journal article
Published: 14 August 2020 in Remote Sensing
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Hyperspectral remote-sensing instruments on unmanned aerial vehicles (UAVs), aircraft and satellites offer new opportunities for sea ice observations. We present the first study using airborne hyperspectral imagery of Arctic sea ice and evaluate two atmospheric correction approaches (ATCOR-4 (Atmospheric and Topographic Correction version 4; v7.0.0) and empirical line calibration). We apply an existing, field data-based model to derive the depth of melt ponds, to airborne hyperspectral AisaEAGLE imagery and validate results with in situ measurements. ATCOR-4 results roughly match the shape of field spectra but overestimate reflectance resulting in high root-mean-square error (RMSE) (between 0.08 and 0.16). Noisy reflectance spectra may be attributed to the low flight altitude of 200 ft and Arctic atmospheric conditions. Empirical line calibration resulted in smooth, accurate spectra (RMSE < 0.05) that enabled the assessment of melt pond bathymetry. Measured and modeled pond bathymetry are highly correlated (r = 0.86) and accurate (RMSE = 4.04 cm), and the model explains a large portion of the variability (R2 = 0.74). We conclude that an accurate assessment of melt pond bathymetry using airborne hyperspectral data is possible subject to accurate atmospheric correction. Furthermore, we see the necessity to improve existing approaches with Arctic-specific atmospheric profiles and aerosol models and/or by using multiple reference targets on the ground.

ACS Style

Marcel König; Gerit Birnbaum; Natascha Oppelt. Mapping the Bathymetry of Melt Ponds on Arctic Sea Ice Using Hyperspectral Imagery. Remote Sensing 2020, 12, 2623 .

AMA Style

Marcel König, Gerit Birnbaum, Natascha Oppelt. Mapping the Bathymetry of Melt Ponds on Arctic Sea Ice Using Hyperspectral Imagery. Remote Sensing. 2020; 12 (16):2623.

Chicago/Turabian Style

Marcel König; Gerit Birnbaum; Natascha Oppelt. 2020. "Mapping the Bathymetry of Melt Ponds on Arctic Sea Ice Using Hyperspectral Imagery." Remote Sensing 12, no. 16: 2623.

Journal article
Published: 12 August 2020 in The Cryosphere
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Melt ponds are key elements in the energy balance of Arctic sea ice. Observing their temporal evolution is crucial for understanding melt processes and predicting sea ice evolution. Remote sensing is the only technique that enables large-scale observations of Arctic sea ice. However, monitoring melt pond deepening in this way is challenging because most of the optical signal reflected by a pond is defined by the scattering characteristics of the underlying ice. Without knowing the influence of meltwater on the reflected signal, the water depth cannot be determined. To solve the problem, we simulated the way meltwater changes the reflected spectra of bare ice. We developed a model based on the slope of the log-scaled remote sensing reflectance at 710 nm as a function of depth that is widely independent from the bottom albedo and accounts for the influence of varying solar zenith angles. We validated the model using 49 in situ melt pond spectra and corresponding depths from shallow ponds on dark and bright ice. Retrieved pond depths are accurate (root mean square error, RMSE=2.81 cm; nRMSE=16 %) and highly correlated with in situ measurements (r=0.89; p=4.34×10-17). The model further explains a large portion of the variation in pond depth (R2=0.74). Our results indicate that our model enables the accurate retrieval of pond depth on Arctic sea ice from optical data under clear sky conditions without having to consider pond bottom albedo. This technique is potentially transferrable to hyperspectral remote sensors on unmanned aerial vehicles, aircraft and satellites.

ACS Style

Marcel König; Natascha Oppelt. A linear model to derive melt pond depth on Arctic sea ice from hyperspectral data. The Cryosphere 2020, 14, 2567 -2579.

AMA Style

Marcel König, Natascha Oppelt. A linear model to derive melt pond depth on Arctic sea ice from hyperspectral data. The Cryosphere. 2020; 14 (8):2567-2579.

Chicago/Turabian Style

Marcel König; Natascha Oppelt. 2020. "A linear model to derive melt pond depth on Arctic sea ice from hyperspectral data." The Cryosphere 14, no. 8: 2567-2579.

Journal article
Published: 21 June 2020 in Remote Sensing
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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.

ACS Style

Matthias P. Wagner; Natascha Oppelt. Deep Learning and Adaptive Graph-Based Growing Contours for Agricultural Field Extraction. Remote Sensing 2020, 12, 1990 .

AMA Style

Matthias P. Wagner, Natascha Oppelt. Deep Learning and Adaptive Graph-Based Growing Contours for Agricultural Field Extraction. Remote Sensing. 2020; 12 (12):1990.

Chicago/Turabian Style

Matthias P. Wagner; Natascha Oppelt. 2020. "Deep Learning and Adaptive Graph-Based Growing Contours for Agricultural Field Extraction." Remote Sensing 12, no. 12: 1990.

Journal article
Published: 29 May 2020 in Remote Sensing
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In China, freshwater is an increasingly scarce resource and wetlands are under great pressure. This study focuses on China’s second largest freshwater lake in the middle reaches of the Yangtze River—the Dongting Lake—and its surrounding wetlands, which are declared a protected Ramsar site. The Dongting Lake area is also a research region of focus within the Sino-European Dragon Programme, aiming for the international collaboration of Earth Observation researchers. ESA’s Copernicus Programme enables comprehensive monitoring with area-wide coverage, which is especially advantageous for large wetlands that are difficult to access during floods. The first year completely covered by Sentinel-1 SAR satellite data was 2016, which is used here to focus on Dongting Lake’s wetland dynamics. The well-established, threshold-based approach and the high spatio-temporal resolution of Sentinel-1 imagery enabled the generation of monthly surface water maps and the analysis of the inundation frequency at a 10 m resolution. The maximum extent of the Dongting Lake derived from Sentinel-1 occurred in July 2016, at 2465 km2, indicating an extreme flood year. The minimum size of the lake was detected in October, at 1331 km2. Time series analysis reveals detailed inundation patterns and small-scale structures within the lake that were not known from previous studies. Sentinel-1 also proves to be capable of mapping the wetland management practices for Dongting Lake polders and dykes. For validation, the lake extent and inundation duration derived from the Sentinel-1 data were compared with excerpts from the Global WaterPack (frequently derived by the German Aerospace Center, DLR), high-resolution optical data, and in situ water level data, which showed very good agreement for the period studied. The mean monthly extent of the lake in 2016 from Sentinel-1 was 1798 km2, which is consistent with the Global WaterPack, deviating by only 4%. In summary, the presented analysis of the complete annual time series of the Sentinel-1 data provides information on the monthly behavior of water expansion, which is of interest and relevance to local authorities involved in water resource management tasks in the region, as well as to wetland conservationists concerned with the Ramsar site wetlands of Dongting Lake and to local researchers.

ACS Style

Juliane Huth; Ursula Gessner; Igor Klein; Hervé Yesou; Xijun Lai; Natascha Oppelt; Claudia Kuenzer. Analyzing Water Dynamics Based on Sentinel-1 Time Series—a Study for Dongting Lake Wetlands in China. Remote Sensing 2020, 12, 1761 .

AMA Style

Juliane Huth, Ursula Gessner, Igor Klein, Hervé Yesou, Xijun Lai, Natascha Oppelt, Claudia Kuenzer. Analyzing Water Dynamics Based on Sentinel-1 Time Series—a Study for Dongting Lake Wetlands in China. Remote Sensing. 2020; 12 (11):1761.

Chicago/Turabian Style

Juliane Huth; Ursula Gessner; Igor Klein; Hervé Yesou; Xijun Lai; Natascha Oppelt; Claudia Kuenzer. 2020. "Analyzing Water Dynamics Based on Sentinel-1 Time Series—a Study for Dongting Lake Wetlands in China." Remote Sensing 12, no. 11: 1761.

Journal article
Published: 08 April 2020 in Remote Sensing
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Knowledge of the location and extent of agricultural fields is required for many applications, including agricultural statistics, environmental monitoring, and administrative policies. Furthermore, many mapping applications, such as object-based classification, crop type distinction, or large-scale yield prediction benefit significantly from the accurate delineation of fields. Still, most existing field maps and observation systems rely on historic administrative maps or labor-intensive field campaigns. These are often expensive to maintain and quickly become outdated, especially in regions of frequently changing agricultural patterns. However, exploiting openly available remote sensing imagery (e.g., from the European Union’s Copernicus programme) may allow for frequent and efficient field mapping with minimal human interaction. We present a new approach to extracting agricultural fields at the sub-pixel level. It consists of boundary detection and a field polygon extraction step based on a newly developed, modified version of the growing snakes active contours model we refer to as graph-based growing contours. This technique is capable of extracting complex networks of boundaries present in agricultural landscapes, and is largely automatic with little supervision required. The whole detection and extraction process is designed to work independently of sensor type, resolution, or wavelength. As a test case, we applied the method to two regions of interest in a study area in the northern Germany using multi-temporal Sentinel-2 imagery. Extracted fields were compared visually and quantitatively to ground reference data. The technique proved reliable in producing polygons closely matching reference data, both in terms of boundary location and statistical proxies such as median field size and total acreage.

ACS Style

Matthias P. Wagner; Natascha Oppelt. Extracting Agricultural Fields from Remote Sensing Imagery Using Graph-Based Growing Contours. Remote Sensing 2020, 12, 1205 .

AMA Style

Matthias P. Wagner, Natascha Oppelt. Extracting Agricultural Fields from Remote Sensing Imagery Using Graph-Based Growing Contours. Remote Sensing. 2020; 12 (7):1205.

Chicago/Turabian Style

Matthias P. Wagner; Natascha Oppelt. 2020. "Extracting Agricultural Fields from Remote Sensing Imagery Using Graph-Based Growing Contours." Remote Sensing 12, no. 7: 1205.

Preprint content
Published: 23 March 2020
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Seasonal snow cover extent (SCE) is a critical component not only for the global radiation balance and climatic behavior but also for water availability of mountainous and arid regions, vegetation growth, permafrost, and winter tourism. However, due to the effects of the global warming, SCE has been observed to behave in much more irregular and extreme patterns in both temporal and spatial aspects. Therefore, a continuous SCE monitoring strategy is necessary to understand the effect of climate change on the cryosphere and to assess the corresponding impacts on human society and the environment. Nevertheless, although conventional optical sensor-based sensing approaches are mature, they suffer from cloud coverage and illumination dependency. Consequently, spaceborne Synthetic Aperture Radar (SAR) provides a pragmatic solution for achieving all-weather and day-and-night monitoring at low cost, especially after the launch of the Sentinel-1 constellation. 

In the present study, we propose a new global SCE mapping approach, which utilizes dual-polarization intensity-composed bands, polarimetric H/A/α decomposition information, topographical factors, and a land cover layer to detect the SCE. By including not only amplitude but also phase information, we overcome the limitations of previous studies, which can only map wet SCE. Additionally, a layer containing the misclassification probability is provided as well for measuring the uncertainty. Based on the validation with in-situ stations and optical imagery, around 85% accuracy of the classification is ensured. Consequently, by implementing the proposed method globally, we can provide a novel way to map high resolution (20 m) and cloud-free SCE even under cloud covered/night conditions. Preparations to combine this product with the optical-based DLR Global SnowPack are already ongoing, offering the opportunity to provide a daily snow mapping service in the near future which is totally independent from clouds or polar darkness.

ACS Style

Ya-Lun Tsai; Soner Uereyen; Andreas Dietz; Claudia Kuenzer; Natascha Oppelt. Global Snow Cover Extent Mapping Using Sentinel-1. 2020, 1 .

AMA Style

Ya-Lun Tsai, Soner Uereyen, Andreas Dietz, Claudia Kuenzer, Natascha Oppelt. Global Snow Cover Extent Mapping Using Sentinel-1. . 2020; ():1.

Chicago/Turabian Style

Ya-Lun Tsai; Soner Uereyen; Andreas Dietz; Claudia Kuenzer; Natascha Oppelt. 2020. "Global Snow Cover Extent Mapping Using Sentinel-1." , no. : 1.

Preprint content
Published: 23 March 2020
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Seagrass meadows cover large benthic areas of the Baltic Sea, but eutrophication and climate change imply declining seagrass coverage. Apart from acoustic methods and traditional diver mappings, optical remote sensing techniques allow for mapping seagrass. Optical satellite analyses of seagrass mapping may supplement acoustic methods in shallow coastal waters with observations that are more frequent and have a larger spatial coverage.

In the clear Greek Mediterranean Sea, Sentinel-2 was already applied successfully to detect bathymetry and seagrass meadows. We are now testing whether Sentinel-2 data are also suitable for analysing the sublittoral in the turbid waters of the Baltic Sea. We focus on an extensive shallow water area near Kiel/Germany. Based on Sentinel-2 data, we analyse water depth and differentiate between seagrass covered and bare sandy ground. We derive these parameters using empirical and process-based models. First results show that Sentinel-2 allows to determine water depths up to 4 m (RMSE ~ 0.2 m). Comparisons with LiDAR water depths show that inaccuracies increase in overgrown areas. Our study also shows that the atmospheric correction algorithm influences sublittoral ground mappings with Sentinel-2 data. For instance, the absolute water depths of the process-based modelling differ up to 2.5 m on average depending on the atmospheric correction algorithm (ACOLITE, Sen2Cor, iCOR).

Comparing Sentinel-2 seagrass classifications with diver mappings and aerial imagery emphasises that empiric approaches provide plausible sublittoral ground classifications up to approximately 4 m water depth. Combining these results with seagrass mappings based on acoustic measurements (deeper than 4 m water) provides a synthesised sublittoral classification map of the study area up to the present growth limit of seagrass (~ 7 m in the study area).

The Baltic Sea is considered as a very turbid environment, nevertheless we show that satellite-based remote sensing has a great potential for shedding light into the  "white ribbon". The spatial coverage and temporal resolution of the analysed Sentinel-2 data increases the knowledge about the occurrence of seagrass and its spatio-temporal dynamics. Nevertheless, the influence of the selected atmospheric correction approach on the results shows that further research in remote sensing is necessary to assess seagrass meadows reliably.

ACS Style

Katja Kuhwald; Philipp Held; Florian Gausepohl; Jens Schneider Von Deimling; Natascha Oppelt. Mapping seagrass and water depths using Sentinel-2. 2020, 1 .

AMA Style

Katja Kuhwald, Philipp Held, Florian Gausepohl, Jens Schneider Von Deimling, Natascha Oppelt. Mapping seagrass and water depths using Sentinel-2. . 2020; ():1.

Chicago/Turabian Style

Katja Kuhwald; Philipp Held; Florian Gausepohl; Jens Schneider Von Deimling; Natascha Oppelt. 2020. "Mapping seagrass and water depths using Sentinel-2." , no. : 1.

Journal article
Published: 10 February 2020 in ISPRS International Journal of Geo-Information
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A growing world population, increasing prosperity in emerging countries, and shifts in energy and food demands necessitate a continuous increase in global agricultural production. Simultaneously, risks of extreme weather events and a slowing productivity growth in recent years has caused concerns about meeting the demands in the future. Crop monitoring and timely yield predictions are an important tool to mitigate risk and ensure food security. A common approach is to combine the temporal simulation of dynamic crop models with a geospatial component by assimilating remote sensing data. To ensure reliable assimilation, handling of uncertainties in both models and the assimilated input data is crucial. Here, we present a new approach for data assimilation using particle swarm optimization (PSO) in combination with statistical distance metrics that allow for flexible handling of model and input uncertainties. We explored the potential of the newly proposed method in a case study by assimilating canopy cover (CC) information, obtained from Sentinel-2 data, into the AquaCrop-OS model to improve winter wheat yield estimation on the pixel- and field-level and compared the performance with two other methods (simple updating and extended Kalman filter). Our results indicate that the performance of the new method is superior to simple updating and similar or better than the extended Kalman filter updating. Furthermore, it was particularly successful in reducing bias in yield estimation.

ACS Style

Matthias P. Wagner; Thomas Slawig; Alireza Taravat; Natascha Oppelt. Remote Sensing Data Assimilation in Dynamic Crop Models Using Particle Swarm Optimization. ISPRS International Journal of Geo-Information 2020, 9, 105 .

AMA Style

Matthias P. Wagner, Thomas Slawig, Alireza Taravat, Natascha Oppelt. Remote Sensing Data Assimilation in Dynamic Crop Models Using Particle Swarm Optimization. ISPRS International Journal of Geo-Information. 2020; 9 (2):105.

Chicago/Turabian Style

Matthias P. Wagner; Thomas Slawig; Alireza Taravat; Natascha Oppelt. 2020. "Remote Sensing Data Assimilation in Dynamic Crop Models Using Particle Swarm Optimization." ISPRS International Journal of Geo-Information 9, no. 2: 105.

Preprint content
Published: 09 December 2019
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Melt ponds are key elements in the energy balance of Arctic sea ice. Observing their temporal evolution is crucial for understanding melt processes and predicting sea ice evolution. Remote sensing is the only technique that enables large-scale observations of Arctic sea ice. However, monitoring vertical melt pond evolution in this way is challenging because most of the optical signal reflected by a pond is defined by the scattering characteristics of the underlying ice. Without knowing the influence of melt water on the reflected signal, the water depth cannot be determined. To solve the problem, we simulated the way melt water changes the reflected spectra of bare ice. We developed a model based on the slope of the log-scaled remote sensing reflectance at 710 nm. We validated the model using 49 in situ melt pond spectra and corresponding depths from ponds on dark and bright ice. Retrieved pond depths are precise (RMSE = 2.81 cm) and highly correlated with in situ measurements (r = 0.89; p = 4.34e−17). The model further explains a large portion of the variation in pond depth (R2 = 0.74). Our results indicate that pond depth is retrievable from optical data under clear sky conditions. This technique is potentially transferrable to hyperspectral remote sensors on UAVs, aircraft and satellites.

ACS Style

Marcel König; Natascha Oppelt. A linear model to derive melt pond depth from hyperspectral data. 2019, 2019, 1 -17.

AMA Style

Marcel König, Natascha Oppelt. A linear model to derive melt pond depth from hyperspectral data. . 2019; 2019 ():1-17.

Chicago/Turabian Style

Marcel König; Natascha Oppelt. 2019. "A linear model to derive melt pond depth from hyperspectral data." 2019, no. : 1-17.

Journal article
Published: 14 August 2019 in Remote Sensing
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In the present study, we explore the value of employing both vegetation indexes as well as land surface temperature derived from Project for On-Board Autonomy – Vegetation (PROBA-V) and Moderate Resolution Imaging Spectroradiometer (MODIS) sensors, respectively, to support the detection of total (wet + dry) snow cover extent (SCE) based on a simple tuning machine learning approach and provide reliability maps for further analysis. We utilize Sentinel-1-based synthetic aperture radar (SAR) observations, including backscatter coefficient, interferometric coherence, and polarimetric parameters, and four topographical factors as well as vegetation and temperature information to detect the total SCE with a land cover-dependent random forest-based approach. Our results show that the overall accuracy and F-measure are over 90% with an ’Area Under the receiver operating characteristic Curve (ROC)’ (AUC) score of approximately 80% over five study areas located in different mountain ranges, continents, and hemispheres. These accuracies are also confirmed by a comprehensive validation approach with different data sources, attesting the robustness and global transferability. Additionally, based on the reliability maps, we find an inversely proportional relationship between classification reliability and vegetation density. In conclusion, comparing to a previous study only utilizing SAR-based observations, the method proposed in the present study provides a complementary approach to achieve a higher total SCE mapping accuracy while maintaining global applicability with reliable accuracy and corresponding uncertainty information.

ACS Style

Ya-Lun S. Tsai; Andreas Dietz; Natascha Oppelt; Claudia Kuenzer. A Combination of PROBA-V/MODIS-based Products with Sentinel-1 SAR Data for Detecting Wet and Dry Snow Cover in Mountainous Areas. Remote Sensing 2019, 11, 1904 .

AMA Style

Ya-Lun S. Tsai, Andreas Dietz, Natascha Oppelt, Claudia Kuenzer. A Combination of PROBA-V/MODIS-based Products with Sentinel-1 SAR Data for Detecting Wet and Dry Snow Cover in Mountainous Areas. Remote Sensing. 2019; 11 (16):1904.

Chicago/Turabian Style

Ya-Lun S. Tsai; Andreas Dietz; Natascha Oppelt; Claudia Kuenzer. 2019. "A Combination of PROBA-V/MODIS-based Products with Sentinel-1 SAR Data for Detecting Wet and Dry Snow Cover in Mountainous Areas." Remote Sensing 11, no. 16: 1904.

Review
Published: 19 June 2019 in Remote Sensing
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The importance of snow cover extent (SCE) has been proven to strongly link with various natural phenomenon and human activities; consequently, monitoring snow cover is one the most critical topics in studying and understanding the cryosphere. As snow cover can vary significantly within short time spans and often extends over vast areas, spaceborne remote sensing constitutes an efficient observation technique to track it continuously. However, as optical imagery is limited by cloud cover and polar darkness, synthetic aperture radar (SAR) attracted more attention for its ability to sense day-and-night under any cloud and weather condition. In addition to widely applied backscattering-based method, thanks to the advancements of spaceborne SAR sensors and image processing techniques, many new approaches based on interferometric SAR (InSAR) and polarimetric SAR (PolSAR) have been developed since the launch of ERS-1 in 1991 to monitor snow cover under both dry and wet snow conditions. Critical auxiliary data including DEM, land cover information, and local meteorological data have also been explored to aid the snow cover analysis. This review presents an overview of existing studies and discusses the advantages, constraints, and trajectories of the current developments.

ACS Style

Ya-Lun S. Tsai; Andreas Dietz; Natascha Oppelt; Claudia Kuenzer. Remote Sensing of Snow Cover Using Spaceborne SAR: A Review. Remote Sensing 2019, 11, 1456 .

AMA Style

Ya-Lun S. Tsai, Andreas Dietz, Natascha Oppelt, Claudia Kuenzer. Remote Sensing of Snow Cover Using Spaceborne SAR: A Review. Remote Sensing. 2019; 11 (12):1456.

Chicago/Turabian Style

Ya-Lun S. Tsai; Andreas Dietz; Natascha Oppelt; Claudia Kuenzer. 2019. "Remote Sensing of Snow Cover Using Spaceborne SAR: A Review." Remote Sensing 11, no. 12: 1456.

Journal article
Published: 12 April 2019 in Remote Sensing
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Traditional studies on mapping wet snow cover extent (SCE) often feature limitations, especially in vegetated and mountainous areas. The aim of this study is to propose a new total and wet SCE mapping strategy based on freely accessible spaceborne synthetic aperture radar (SAR) data. The approach is transferable on a global scale as well as for different land cover types (including densely vegetated forest and agricultural regions), and is based on the use of backscattering coefficient, interferometric SAR coherence, and polarimetric parameters. Furthermore, four topographical factors were included in the simple tuning of random forest-based land cover type-dependent classification strategy. Results showed the classification accuracy was above 0.75, with an F-measure higher than 0.70, in all five selected regions of interest located around globally distributed mountain ranges. Whilst excluding forest-type land cover classes, the accuracy and F-measure increases to 0.80 and 0.75. In cross-location model set, the accuracy can also be maintained at 0.80 with non-forest accuracy up to 0.85. It has been found that the elevation and polarimetric parameters are the most critical factors, and that the quality of land cover information would also affect the subsequent mapping reliability. In conclusion, through comprehensive validation using optical satellite and in-situ data, our land cover-dependent total SCE mapping approach has been confirmed to be robustly applicable, and the holistic SCE map for different months were eventually derived.

ACS Style

Ya-Lun S. Tsai; Andreas Dietz; Natascha Oppelt; Claudia Kuenzer. Wet and Dry Snow Detection Using Sentinel-1 SAR Data for Mountainous Areas with a Machine Learning Technique. Remote Sensing 2019, 11, 895 .

AMA Style

Ya-Lun S. Tsai, Andreas Dietz, Natascha Oppelt, Claudia Kuenzer. Wet and Dry Snow Detection Using Sentinel-1 SAR Data for Mountainous Areas with a Machine Learning Technique. Remote Sensing. 2019; 11 (8):895.

Chicago/Turabian Style

Ya-Lun S. Tsai; Andreas Dietz; Natascha Oppelt; Claudia Kuenzer. 2019. "Wet and Dry Snow Detection Using Sentinel-1 SAR Data for Mountainous Areas with a Machine Learning Technique." Remote Sensing 11, no. 8: 895.

Journal article
Published: 25 March 2019 in Remote Sensing
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Grassland contributes to carbon storage and animal feed production. Its yield is largely determined by the cutting times of grassland. Previous studies have used remote sensing data for grassland biomass estimation, but only a few studies have focused on SAR remote sensing approaches for automatic grassland cutting status detection. Due to the occurrence of multiple cuttings in a year, it is crucial to effectively monitor grassland cutting events in order to achieve accurate biomass estimations of a whole season. In this study, we examined the capabilities of multilayer perceptron neural networks for automatic grassland cutting status detection using SAR imagery. The proposed model inputs are a time series dataset of VV and VH Sentinel-1 C-band SAR and second-order texture metrics (homogeneity, entropy, contrast and dissimilarity). The proposed approach has been successfully tested on a dataset collected from several fields in Germany in 2016, with an overall accuracy of 85.71% for the validation set.

ACS Style

Alireza Taravat; Matthias P. Wagner; Natascha Oppelt. Automatic Grassland Cutting Status Detection in the Context of Spatiotemporal Sentinel-1 Imagery Analysis and Artificial Neural Networks. Remote Sensing 2019, 11, 711 .

AMA Style

Alireza Taravat, Matthias P. Wagner, Natascha Oppelt. Automatic Grassland Cutting Status Detection in the Context of Spatiotemporal Sentinel-1 Imagery Analysis and Artificial Neural Networks. Remote Sensing. 2019; 11 (6):711.

Chicago/Turabian Style

Alireza Taravat; Matthias P. Wagner; Natascha Oppelt. 2019. "Automatic Grassland Cutting Status Detection in the Context of Spatiotemporal Sentinel-1 Imagery Analysis and Artificial Neural Networks." Remote Sensing 11, no. 6: 711.

Journal article
Published: 27 February 2019 in Urban Science
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A rapid increase in the world’s population over the last century has triggered the transformation of the earth surface, especially in urban areas, where more than half of the global population live. Ghana is no exception and a high population growth rate, coupled with economic development over the last three decades, has transformed the Greater Accra region into a hotspot for massive urban growth. The urban extent of the region has expanded extensively, mainly at the expense of the vegetative cover in the region. Although urbanization presents several opportunities, the environmental and social problems cannot be underestimated. Therefore, the need to estimate the rate and extent of land use/land cover changes in the region and the main drivers of these changes is imperative. Geographic Information Systems (GIS) and remote sensing techniques provide effective tools in studying and monitoring land-use/land-cover change over space and time. A post classification change detection of multiple Landsat images was conducted to map and analyse the extent and rate of land use/land cover change in the region between 1991 and 2015. Subsequently, the urban extent of the region was forecasted for the year 2025 using the Markov Chain and the Multi-Layer Perceptron neural network, together with drivers representing proximity, biophysical, and socio-economic variables. The results from the research revealed that built-up areas increased by 277% over the 24-year study period. However, forest areas experienced massive reduction, diminishing from 34% in 1991 to 6.5% in 2015. The 2025 projected land use map revealed that the urban extent will massively increase to cover 70% of the study area, as compared to 44% in 2015. The urban extent is also anticipated to spill into the adjoining districts mainly on the western and eastern sides of the region. The success of this research in generating a future land-use map for 2025, together with the other significant findings, demonstrates the usefulness of spatial models as tools for sustainable city planning and environmental management, especially for urban planners in developing countries.

ACS Style

Bright Addae; Natascha Oppelt. Land-Use/Land-Cover Change Analysis and Urban Growth Modelling in the Greater Accra Metropolitan Area (GAMA), Ghana. Urban Science 2019, 3, 26 .

AMA Style

Bright Addae, Natascha Oppelt. Land-Use/Land-Cover Change Analysis and Urban Growth Modelling in the Greater Accra Metropolitan Area (GAMA), Ghana. Urban Science. 2019; 3 (1):26.

Chicago/Turabian Style

Bright Addae; Natascha Oppelt. 2019. "Land-Use/Land-Cover Change Analysis and Urban Growth Modelling in the Greater Accra Metropolitan Area (GAMA), Ghana." Urban Science 3, no. 1: 26.

Original research article
Published: 22 February 2019 in Frontiers in Earth Science
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Multispectral remote sensing may be a powerful tool for areal retrieval of biogeophysical parameters in the Arctic sea ice. The MultiSpectral Instrument on board the Sentinel-2 (S-2) satellites of the European Space Agency offers new possibilities for Arctic research; S-2A and S-2B provide 13 spectral bands between 443 and 2,202 nm and spatial resolutions between 10 and 60 m, which may enable the monitoring of large areas of Arctic sea ice. For an accurate retrieval of parameters such as surface albedo, the elimination of atmospheric influences in the data is essential. We therefore provide an evaluation of five currently available atmospheric correction processors for S-2 (ACOLITE, ATCOR, iCOR, Polymer, and Sen2Cor). We evaluate the results of the different processors using in situ spectral measurements of ice and snow and open water gathered north of Svalbard during RV Polarstern cruise PS106.1 in summer 2017. We used spectral shapes to assess performance for ice and snow surfaces. For open water, we additionally evaluated intensities. ACOLITE, ATCOR, and iCOR performed well over sea ice and Polymer generated the best results over open water. ATCOR, iCOR and Sen2Cor failed in the image-based retrieval of atmospheric parameters (aerosol optical thickness, water vapor). ACOLITE estimated AOT within the uncertainty range of AERONET measurements. Parameterization based on external data, therefore, was necessary to obtain reliable results. To illustrate consequences of processor selection on secondary products we computed average surface reflectance of six bands and normalized difference melt index (NDMI) on an image subset. Medians of average reflectance and NDMI range from 0.80–0.97 to 0.12–0.18 while medians for TOA are 0.75 and 0.06, respectively.

ACS Style

Marcel König; Martin Hieronymi; Natascha Oppelt. Application of Sentinel-2 MSI in Arctic Research: Evaluating the Performance of Atmospheric Correction Approaches Over Arctic Sea Ice. Frontiers in Earth Science 2019, 7, 1 .

AMA Style

Marcel König, Martin Hieronymi, Natascha Oppelt. Application of Sentinel-2 MSI in Arctic Research: Evaluating the Performance of Atmospheric Correction Approaches Over Arctic Sea Ice. Frontiers in Earth Science. 2019; 7 ():1.

Chicago/Turabian Style

Marcel König; Martin Hieronymi; Natascha Oppelt. 2019. "Application of Sentinel-2 MSI in Arctic Research: Evaluating the Performance of Atmospheric Correction Approaches Over Arctic Sea Ice." Frontiers in Earth Science 7, no. : 1.

Journal article
Published: 02 January 2019 in Journal of Limnology
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Submerged aquatic vegetation (SAV) plays an important role in freshwater lake ecosystems. Due to its sensitivity to environmental changes, several SAV species serve as bioindicators for the trophic state of freshwater lakes. Variations in water temperature, light availability and nutrient concentration affect SAV growth and species composition. To monitor the trophic state as required by the European Water Framework Directive (WFD), SAV needs to be monitored regularly. This study analyses the development of macrophyte patches at Lake Starnberg, Germany, by exploring four Sentinel-2A acquired within the main growing season in August and September 2015. Two different methods of littoral bottom coverage assessment are compared, i.e. a semi-empirical method using depth-invariant indices and a physically based, bio-optical method using WASI-2D (Water Colour Simulator). For a precise Sentinel-2 imaging by date and hour, satellite measurements were supported by lake bottom spectra delivered by in situ data based reflectance models. Both methods identified vegetated and non-vegetated patches in shallow water areas. Furthermore, tall- and meadow-growing SAV growth classes could be differentiated. Both methods revealed similar results when focusing on the identification of sediment and SAV patches (R² from 0.56 to 0.81), but not for a differentiation on SAV class growth level (R²

ACS Style

Christine Fritz; Katja Kuhwald; Thomas Schneider; Juergen Geist; Natascha Oppelt. Sentinel-2 for mapping the spatio-temporal development of submerged aquatic vegetation at Lake Starnberg (Germany). Journal of Limnology 2019, 78, 1 .

AMA Style

Christine Fritz, Katja Kuhwald, Thomas Schneider, Juergen Geist, Natascha Oppelt. Sentinel-2 for mapping the spatio-temporal development of submerged aquatic vegetation at Lake Starnberg (Germany). Journal of Limnology. 2019; 78 (1):1.

Chicago/Turabian Style

Christine Fritz; Katja Kuhwald; Thomas Schneider; Juergen Geist; Natascha Oppelt. 2019. "Sentinel-2 for mapping the spatio-temporal development of submerged aquatic vegetation at Lake Starnberg (Germany)." Journal of Limnology 78, no. 1: 1.

Journal article
Published: 17 May 2018 in Sustainability
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Soil compaction caused by field traffic is one of the main threats to agricultural landscapes. Compacted soils have a reduced hydraulic conductivity, lower plant growth and increased surface runoff resulting in numerous environmental issues such as increased nutrient leaching and flood risk. Mitigating soil compaction, therefore, is a major goal for a sustainable agriculture and environmental protection. To prevent undesirable effects of field traffic, it is essential to know where and when soil compaction may occur. This study developed a model for soil compaction risk assessment of arable soils at regional scale. A combination of (i) soil, weather, crop type and machinery information; (ii) a soil moisture model and (iii) soil compaction models forms the SaSCiA-model (Spatially explicit Soil Compaction risk Assessment). The SaSCiA-model computes daily maps of soil compaction risk and associated area statistics for varying depths at actual field conditions and for entire regions. Applications with open access data in two different study areas in northern Germany demonstrated the model’s applicability. Soil compaction risks strongly varied in space and time throughout the year. SaSCiA allows a detailed spatio-temporal analysis of soil compaction risk at the regional scale, which exceed those of currently available models. Applying SaSCiA may support farmers, stakeholders and consultants in making decision for a more sustainable agriculture.

ACS Style

Michael Kuhwald; Katja Dörnhöfer; Natascha Oppelt; Rainer Duttmann. Spatially Explicit Soil Compaction Risk Assessment of Arable Soils at Regional Scale: The SaSCiA-Model. Sustainability 2018, 10, 1618 .

AMA Style

Michael Kuhwald, Katja Dörnhöfer, Natascha Oppelt, Rainer Duttmann. Spatially Explicit Soil Compaction Risk Assessment of Arable Soils at Regional Scale: The SaSCiA-Model. Sustainability. 2018; 10 (5):1618.

Chicago/Turabian Style

Michael Kuhwald; Katja Dörnhöfer; Natascha Oppelt; Rainer Duttmann. 2018. "Spatially Explicit Soil Compaction Risk Assessment of Arable Soils at Regional Scale: The SaSCiA-Model." Sustainability 10, no. 5: 1618.