•The study provides a comprehensive comparison of methods for CCC retrieval from Sentinel-2 imagery.•The results of the study demonstrate that both the physical-based and statistical approaches ...results are comparable.•INFORM inversion using LUT is the unbiased predictor of CCC from remote sensing data.•The hybrid approach yielded accuracies similar to the LUT inversion and may be the preferred method for large scale mapping.
The Sentinel-2 Multi-Spectral Imager (MSI) has three spectral bands centered at 705, 740, and 783 nm wavelengths that exploit the red-edge information useful for quantifying plant biochemical traits. This sensor configuration is expected to improve the prediction accuracy of vegetation chlorophyll content. In this work, we assessed the performance of several statistical and physical-based methods in retrieving canopy chlorophyll content (CCC) from Sentinel-2 in a heterogeneous mixed mountain forest. Amongst the algorithms presented in the literature, 13 different vegetation indices (VIs), a non-parametric statistical approach, and two radiative transfer models (RTM) were used to assess the CCC prediction accuracy. A field campaign was conducted in July 2017 to collect in situ measurements of CCC in Bavarian forest national park, and the cloud-free Sentinel-2 image was acquired on 13 July 2017. The leave-one-out cross-validation technique was used to compare the VIs and the non-parametric approach. Whereas physical-based methods were calibrated using simulated data and validated using the in situ reference dataset. The statistical-based approaches, such as the modified simple ratio (mSR) vegetation index and the partial least square regression (PLSR) outperformed all other techniques. As such the modified simple ratio (mSR3) (665, 865) gave the lowest cross-validated RMSE of 0.21 g/m2 (R2 = 0.75). The PLSR resulted in the highest R2 of 0.78, and slightly higher RMSE =0.22 g/m2 than mSR3. The physical-based approach-INFORM inversion using look-up table resulted in an RMSE =0.31 g/m2, and R2 = 0.67. Although mapping CCC using these methods revealed similar spatial distribution patterns, over and underestimation of low and high CCC values were observed mainly in the statistical approaches. Further validation using in situ data from different terrestrial ecosystems is imperative for both the statistical and physical-based approaches' effectiveness to quantify CCC before selecting the best operational algorithm to map CCC from Sentinel-2 for long-term terrestrial ecosystems monitoring across the globe.
Monitoring global biodiversity from space through remotely sensing geospatial patterns has high potential to add to our knowledge acquired by field observation. Although a framework of essential ...biodiversity variables (EBVs) is emerging for monitoring biodiversity, its poor alignment with remote sensing products hinders interpolation between field observations. This study compiles a comprehensive, prioritized list of remote sensing biodiversity products that can further improve the monitoring of geospatial biodiversity patterns, enhancing the EBV framework and its applicability. The ecosystem structure and ecosystem function EBV classes, which capture the biological effects of disturbance as well as habitat structure, are shown by an expert review process to be the most relevant, feasible, accurate and mature for direct monitoring of biodiversity from satellites. Biodiversity products that require satellite remote sensing of a finer resolution that is still under development are given lower priority (for example, for the EBV class species traits). Some EBVs are not directly measurable by remote sensing from space, specifically the EBV class genetic composition. Linking remote sensing products to EBVs will accelerate product generation, improving reporting on the state of biodiversity from local to global scales.
The Sentinel satellite fleet of the Copernicus Programme offers new potential to map and monitor plant traits at fine spatial and temporal resolutions. Among these traits, leaf area index (LAI) is a ...crucial indicator of vegetation growth and an essential variable in biodiversity studies. Numerous studies have shown that the radiative transfer approach has been a successful method to retrieve LAI from remote-sensing data. However, the suitability and adaptability of this approach largely depend on the type of remote-sensing data, vegetation cover and the ecosystem studied. Saltmarshes are important wetland ecosystems threatened by sea level rise among other human- and animal-induced changes. Therefore, monitoring their vegetation status is crucial for their conservation, yet few LAI assessments exist for these ecosystems. In this study, the retrieval of LAI in a saltmarsh ecosystem is examined using Sentinel-2 and RapidEye data through inversion of the PROSAIL radiative transfer model. Field measurements of LAI and some other plant traits were obtained during two succeeding field campaigns in July 2015 and 2016 on the saltmarsh of Schiermonnikoog, a barrier island of the Netherlands. RapidEye (2015) and Sentinel-2 (2016) data were acquired concurrent to the time of the field campaigns. The broadly employed PROSAIL model was inverted using two look-up tables (LUTs) generated in the spectral band’s settings of the two sensors and in which each contained 500,000 records. Different solutions from the LUTs, as well as, different Sentinel-2 spectral subsets were considered to examine the LAI retrieval. Our results showed that generally the LAI retrieved from Sentinel-2 had higher accuracy compared to RapidEye-retrieved LAI. Utilising the mean of the first 10 best solutions from the LUTs resulted in higher R2 (0.51 and 0.59) and lower normalised root means square error (NRMSE) (0.24 and 0.16) for both RapidEye and Sentinel-2 data respectively. Among different Sentinel-2 spectral subsets, the one comprised of the four near-infrared (NIR) and shortwave infrared (SWIR) spectral bands resulted in higher estimation accuracy (R2 = 0.44, NRMSE = 0.21) in comparison to using other studied spectral subsets. The results demonstrated the feasibility of broadband multispectral sensors, particularly Sentinel-2 for retrieval of LAI in the saltmarsh ecosystem via inversion of PROSAIL. Our results highlight the importance of proper parameterisation of radiative transfer models and capacity of Sentinel-2 spectral range and resolution, with impending high-quality global observation aptitude, for retrieval of plant traits at a global scale.
Accurate measurement of canopy chlorophyll content (CCC) is essential for the understanding of terrestrial ecosystem dynamics through monitoring and evaluating properties such as carbon and water ...flux, productivity, light use efficiency as well as nutritional and environmental stresses. Information on the amount and distribution of CCC helps to assess and report biodiversity indicators related to ecosystem processes and functional aspects. Therefore, measuring CCC continuously and globally from earth observation data is critical to monitor the status of the biosphere. However, generic and robust methods for regional and global mapping of CCC are not well defined. This study aimed at examining the spatiotemporal consistency and scalability of selected methods for CCC mapping across biomes. Four methods (i.e., radiative transfer models (RTMs) inversion using a look-up table (LUT), the biophysical processor approach integrated into the Sentinel application platform (SNAP toolbox), simple ratio vegetation index (SRVI), and partial least square regression (PLSR)) were evaluated. Similarities and differences among CCC products generated by applying the four methods on actual Sentinel-2 data in four biomes (temperate forest, tropical forest, wetland, and Arctic tundra) were examined by computing statistical measures and spatiotemporal consistency pairwise comparisons. Pairwise comparison of CCC predictions by the selected methods demonstrated strong agreement. The highest correlation (R2 = 0.93, RMSE = 0.4371 g/m2) was obtained between CCC predictions of PROSAIL inversion by LUT and SNAP toolbox approach in a wetland when a single Sentinel-2 image was used. However, when time-series data were used, it was PROSAIL inversion against SRVI (R2 = 0.88, RMSE = 0.19) that showed greatest similarity to the single date predictions (R2 = 0.83, RMSE = 0.17 g/m2) in this biome. Generally, the CCC products obtained using the SNAP toolbox approach resulted in a systematic over/under-estimation of CCC. RTMs inversion by LUT (INFORM and PROSAIL) resulted in a non-biased, spatiotemporally consistent prediction of CCC with a range closer to expectations. Therefore, the RTM inversion using LUT approaches particularly, INFORM for ‘forest’ and PROSAIL for ‘short vegetation’ ecosystems, are recommended for CCC mapping from Sentinel-2 data for worldwide mapping of CCC. Additional validation of the two RTMs with field data of CCC across biomes is required in the future.
Climate change, increasing population and changes in land use are all rapidly driving the need to be able to better understand surface water dynamics. The targets set by the United Nations under ...Sustainable Development Goal 6 in relation to freshwater ecosystems also make accurate surface water monitoring increasingly vital. However, the last decades have seen a steady decline in in situ hydrological monitoring and the availability of the growing volume of environmental data from free and open satellite systems is increasingly being recognized as an essential tool for largescale monitoring of water resources. The scientific literature holds many promising studies on satellite-based surface-water mapping, but a systematic evaluation has been lacking. Therefore, a round robin exercise was organized to conduct an intercomparison of 14 different satellite-based approaches for monitoring inland surface dynamics with Sentinel-1, Sentinel-2, and Landsat 8 imagery. The objective was to achieve a better understanding of the pros and cons of different sensors and models for surface water detection and monitoring. Results indicate that, while using a single sensor approach (applying either optical or radar satellite data) can provide comprehensive results for very specific localities, a dual sensor approach (combining data from both optical and radar satellites) is the most effective way to undertake largescale national and regional surface water mapping across bioclimatic gradients.
The digital transformation taking place in all areas of life has led to a massive increase in digital data - in particular, related to the places where and the ways how we live. To facilitate an ...exploration of the new opportunities arising from this development the Urban Thematic Exploitation Platform (U-TEP) has been set-up. This enabling instrument represents a virtual environment that combines open access to multi-source data repositories with dedicated data processing, analysis and visualisation functionalities. Moreover, it includes mechanisms for the development and sharing of technology and knowledge. After an introduction of the underlying methodical concept, this paper introduces four selected use cases that were carried out on the basis of U-TEP: two technology-driven applications implemented by users from the remote sensing and software engineering community (generation of cloud-free mosaics, processing of drone data) and two examples related to concrete use scenarios defined by planners and decision makers (data analytics related to global urbanization, monitoring of regional land-use dynamics). The experiences from U-TEP's pre-operations phase show that the system can effectively support the derivation of new data, facts and empirical evidence that helps scientists and decision-makers to implement improved strategies for sustainable urban development.
The Group on Earth Observations Biodiversity Observation Network (GEO BON) is developing the Essential Biodiversity Variables (EBVs) as the key variables needed, on a regular and global basis, to ...understand and monitor changes in the Earth's biodiversity. A subset of these EBVs can be derived from space‐based remote sensing, within this paper referred to as remotely sensed EBVs (RS‐EBVs). Given the global, periodic and standardized character of satellite remote sensing measures, RS‐EBVs may be seen as easier to generate than non‐remotely sensed EBVs, which have to be assembled from disparate and local sources of information. Particularly because they are global and periodic, RS‐EBVs are of special relevance for monitoring the state of and changes to biodiversity, notably the structure and function of ecosystems. If well developed, RS‐EBVs can provide key information for global biodiversity assessments as well as for national governments to meet their obligations under the Convention on Biological Diversity (CBD), in particular to formulate and implement appropriate management responses to biodiversity losses. However, the relevance and usage of globally produced RS‐EBVs in wide‐scale ecological modelling, such as in species distribution and abundance studies or in ecosystem integrity analyses, are still to be demonstrated, in particular when it comes to deriving biodiversity indicators for policy making and implementation. The biodiversity community at large, from those conducting scientific ecological studies to those involved in the development of remote sensing applications for biodiversity monitoring, can gain value from RS‐EBVs, but doing so requires close cooperation with space agencies. Effective interaction is only likely to result if the biodiversity community understands how space agencies determine their observation and product requirements. To develop these requirements, space agencies need to precisely specify the physical measurements for their spaceflight instruments, as well as the algorithmic approaches, to generate RS‐EBV products from these measurements. Here, we address the biodiversity community to discuss the role space agencies should play in the development of EBVs arising from satellite remote sensing. Importantly, we explain the necessity for translating the observational needs of the biodiversity community into specific satellite remote sensing measurement and algorithm requirements. By summarizing the prerequisite conditions that are required for obtaining a collective and strong engagement of space agencies in the co‐development of RS‐EBVs, we aim to facilitate collaborative efforts between the biodiversity community and the space agencies, which can ultimately contribute to a global and comprehensive biodiversity knowledge system.
Outline of the overall process by which remotely sensed essential biodiversity variables should be developed and matured. The Group on Earth Observations Biodiversity Observation Network (GEO BON) is developing the Essential Biodiversity Variables (EBVs) as the key variables needed, on a regular and global basis, to understand and monitor changes in the Earth's biodiversity. With the emergence of satellite missions with ensured observational continuity and free and open data policies, space agencies provide, over the long term, a unique means to monitor, understand and predict the status and trends of biodiversity. A strong engagement of Space Agencies in the co‐development of EBVs requires both a community buy‐in of the EBVs (and of the subset of remotely sensed EBVs) and an endorsement by a recognized authority such as the Convention on Biological Diversity (CBD). These prerequisite conditions are necessary to obtain a firm commitment of CEOS and its Member Space Agencies to actively participate in the development of the RS‐EBV data products and related algorithms and to align the acquisition scenarios of their satellite assets to the observation needs of the biodiversity community.
Remote sensing studies of vegetation phenology increasingly benefit from freely available satellite imagery acquired with high temporal frequency at fine spatial resolution. Particularly for ...heterogeneous landscapes this is good news, given the drawback of medium-resolution sensors commonly used for phenology retrieval (e.g., MODIS) to properly represent the fine-scale spatial variability of vegetation types. The Sentinel-2 mission acquires spectral data globally at 10 to 60 m resolution every five days. To illustrate the mission's potential for studying vegetation phenology, we retrieved phenological parameters for the Dutch barrier island Schiermonnikoog for a full season of Sentinel-2A observations in 2016. Overlapping orbits resulted in two acquisitions per 10 days, similar to what is achieved globally since the launch of Sentinel-2B. For eight locations on the island's salt marsh we compared greenness chromatic coordinate (GCC) series derived from digital repeat RGB-cameras with vegetation index series derived from Sentinel-2 (NDVI and GCC). For each series, a double hyperbolic tangent model was fitted and thresholds were applied to the modelled data to estimate start-, peak-, and end-of-season (SOS/PS/EOS). Variability in Sentinel-2 derived SOS, when taken as the midpoint between minimum and peak NDVI, was well-explained by camera GCC-based SOS (R2 = 0.74, MSD = 8.0 days, RMSD = 13.0 days). However, EOS estimates from camera GCC series were on average almost two months before NDVI-based estimates. This could partially be explained by the observed exponential relationship between GCC and NDVI, as well as by the combined effect of viewing angle differences and the presence of non-photosynthetic elements in the vegetation canopy. A two-layer canopy radiative transfer model incorporating reduced chlorophyll levels in the upper layer provided a physically-based explanation of the viewing angle effect. Finally, we applied the phenology retrieval approach to NDVI series for all pixels of the island in order to map spatial patterns of phenology at fine resolution. Our results demonstrate the potential of the Sentinel-2 mission for providing spatially-detailed retrievals of phenology.
•We used one year of Sentinel-2A data to retrieve phenological parameters.•The retrievals were compared with those from greenness series for eight RGB-cameras.•We explained deviations by differences in spectral index used and viewing angle.•A two-layer radiative transfer model could reproduce the viewing angle effect.•We showed that Sentinel-2 can provide phenology estimates at fine spatial detail.
We call on conservation and space agencies to agree on a definitive set of biodiversity variables and how these will be tracked from space, to address conservation targets. Methods to derive these ...variables and the set of satellites needed to observe them must also be decided, to ensure continuous monitoring. To stimulate discussion, we propose ten variables that capture biodiversity change on the ground and can be monitored from space (see 'Ten variables'). These range from leaf nitrogen and chlorophyll content to seasonal changes in floods and fires.
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