Katja Dörnhöfer

Water constituents and water depth retrieval from Sentinel-2A – a first evaluation in an oligotrophic lake

Satellite remote sensing may assist in meeting the needs of lake monitoring. In this study, we aim to evaluate the potential of Sentinel-2 to assess and monitor water constituents and bottom characteristics of lakes at spatio-temporal synoptic scales. In a field campaign at Lake Starnberg, Germany, we collected validation data concurrently to a Sentinel-2A (S2-A) overpass. We compared the results of three different atmospheric corrections, i.e., Sen2Cor, ACOLITE and MIP, with in situ reflectance measurements, whereof MIP performed best (r = 0.987, RMSE = 0.002 sr−1). Using the bio-optical modelling tool WASI-2D, we retrieved absorption by coloured dissolved organic matter (aCDOM(440)), backscattering and concentration of suspended particulate matter (SPM) in optically deep water; water depths, bottom substrates and aCDOM(440) were modelled in optically shallow water. In deep water, SPM and aCDOM(440) showed reasonable spatial patterns. Comparisons with in situ data (mean: 0.43 m−1) showed an underestimation of S2-A derived aCDOM(440) (mean: 0.14 m−1); S2-A backscattering of SPM was slightly higher than backscattering from in situ data (mean: 0.027 m−1 vs. 0.019 m−1). Chlorophyll-a concentrations (~1 mg·m−3) of the lake were too low for a retrieval. In shallow water, retrieved water depths exhibited a high correlation with echo sounding data (r = 0.95, residual standard deviation = 0.12 m) up to 2.5 m (Secchi disk depth: 4.2 m), though water depths were slightly underestimated (RMSE = 0.56 m). In deeper water, Sentinel-2A bands were incapable of allowing a WASI-2D based separation of macrophytes and sediment which led to erroneous water depths. Overall, the results encourage further research on lakes with varying optical properties and trophic states with Sentinel-2A.

Detecting land use and land cover changes in Northern German agricultural landscapes to assess ecosystem service dynamics.

Land use and land cover (LULC) and their changes in share and number of classes can be documented by remote sensing techniques. Information on LULC is needed for the assessment of ecosystem services and is used as input data for mapping and modelling. This information is important for decision-making and management of ecosystems and landscapes. In this study, LULC were analysed in two agricultural areas in Northern Germany by means of a pixel-based maximum likelihood classification approach of 11 Landsat TM 5 scenes between 1987 and 2011 followed by a post-classification refinement using the tool IRSeL. In this time period, grassland declined by about 50 % in both case study areas. This loss in grassland area can be associated with changes in provisioning ecosystem services as the supply of fodder and crops and the number of livestock declined from 1987 to 2007. Furthermore, an on-going increase in maize cultivation area, which is nowadays more and more used as biomass for biogas production, documents the addition of another provisioning service, i.e., biomass for energy. Combining remote sensing and research on ecosystem services supports the assessment and monitoring of ecosystem services on different temporal, spatial, and semantic scales.

IRSeL—An approach to enhance continuity and accuracy of remotely sensed land cover data

Abstract Land cover data gives the opportunity to study interactions between land cover status and environmental issues such as hydrologic processes, soil properties, or biodiversity. Land cover data often are based on classification of remote sensing data that seldom provides the requisite accuracy, spatial availability and temporal observational frequency for environmental studies. Thus, there is a high demand for accurate and spatio-temporal complete time series of land cover. In the past considerable research was undertaken to increase land cover classification accuracy, while less effort was spent on interpolation techniques. The purpose of this article is to present a space–time interpolation and revision approach for remotely sensed land cover data. The approach leverages special properties known for agricultural areas such as crop rotations or temporally static land cover classes. The newly developed IRSeL-tool (Interpolation and improvement of Remotely Sensed Land cover) corrects classification errors and interpolates missing land cover pixels. The easy-to-use tool solely requires an initial land cover data set. The IRSeL specific interpolation and revision technique, the data input requirements and data output structure are described in detail. A case study in an area around the city of Neumunster in Northern Germany from 2006 to 2012 was performed for IRSeL validation with initial land cover data sets (Landsat TM image classifications) for the years 2006, 2007, 2009, 2010 and 2011. The results of the case study showed that IRSeL performs well; including years with no classification data overall accuracy values for IRSeL interpolated pixels range from 0.63 to 0.81. IRSeL application significantly increases the accuracy of the land cover data; overall accuracy values rise 0.08 in average resulting in overall accuracy values of at least 0.86. Considering estimated reliabilities, the IRSeL tool provides a temporally and spatially completed and revised land cover data set that allows drawing conclusions for land cover related studies.

Multi-sensor satellite and in situ monitoring of phytoplankton development in a eutrophic-mesotrophic lake

Phytoplankton indicated by its photosynthetic pigment chlorophyll-a is an important pointer on lake ecology and a regularly monitored parameter within the European Water Framework Directive. Along with eutrophication and global warming cyanobacteria gain increasing importance concerning human health aspects. Optical remote sensing may support both the monitoring of horizontal distribution of phytoplankton and cyanobacteria at the lake surface and the reduction of spatial uncertainties associated with limited water sample analyses. Temporal and spatial resolution of using only one satellite sensor, however, may constrain its information value. To discuss the advantages of a multi-sensor approach the sensor-independent, physically based model MIP (Modular Inversion and Processing System) was applied at Lake Kummerow, Germany, and lake surface chlorophyll-a was derived from 33 images of five different sensors (MODIS-Terra, MODIS-Aqua, Landsat 8, Landsat 7 and Sentinel-2A). Remotely sensed lake average chlorophyll-a concentration showed a reasonable development and varied between 2.3±0.4 and 35.8±2.0mg·m-3 from July to October 2015. Match-ups between in situ and satellite chlorophyll-a revealed varying performances of Landsat 8 (RMSE: 3.6 and 19.7mg·m-3), Landsat 7 (RMSE: 6.2mg·m-3), Sentinel-2A (RMSE: 5.1mg·m-3) and MODIS (RMSE: 12.8mg·m-3), whereas an in situ data uncertainty of 48% needs to be respected. The temporal development of an index on harmful algal blooms corresponded well with the cyanobacteria biomass development during summer months. Satellite chlorophyll-a maps allowed to follow spatial patterns of chlorophyll-a distribution during a phytoplankton bloom event. Wind conditions mainly explained spatial patterns. Integrating satellite chlorophyll-a into trophic state assessment resulted in different trophic classes. Our study endorsed a combined use of satellite and in situ chlorophyll-a data to alleviate weaknesses of both approaches and to better characterise and understand phytoplankton development in lakes.

Mapping benthic substrate coverage and bathymetry using bio-optical modelling — An enmap case study in the coastal waters of helgoland

Kelp forests, which are dense stands of brown seaweeds of the taxonomic order Laminariales, often are used as indicator species for assessing the environmental state of coastal waters. To this end, large scale monitoring approaches are required apart from traditional labor-intensive point sampling techniques. Bio-optical modelling with hyperspectral remote sensing data provides the possibility to take on the task. Yet, hyperspectral imagery mainly originates from airborne data. Within in the scope of the EnMAP Preparatory Program the bio-optical model WASI-2D was tested to the coastal area of Helgoland. A simulated EnMAP scene was spectrally unmixed for the two substrate types Laminariales and sediment. Inverse modelling yielded plausible patterns and gradients of substrate coverages and bathymetry. Comparisons with in situ data revealed results tended to overestimate sediment and to underestimate Laminariales coverage and bathymetry. WASI-2D, however, was able to resolve the complex bottom and bathymetric structure at the rocky coast of Helgoland.