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.

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.

Heat waves are projected to become more frequent, longer-lasting, and intense. At the same time, urban areas are confronted with the urban heat island (UHI) phenomenon, which adds to the thermal stress experienced during hot spells. Focusing on the Paris area during the hot summer of 2003, we investigated the influence of heat waves on UHI intensity, i.e., the urban-rural temperature contrast. In a first step, this was done based on observed temperatures from an urban and a rural site, showing that per C increase in the daytime temperature, the nighttime UHI intensity increased by 0.086 C. Recognizing the limited spatial representativeness of the urban experimental site, located in a park, we then performed simulations with an urban climate model, covering the wider Paris area for the summer of 2003. First, a validation was done using the aforementioned temperature measurements to do so. Subsequently, we estimated the sensitivity of the nighttime UHI intensity with respect to the daytime temperature, this time using simulated temperatures of the densely built-up areas in the center of Paris, yielding an increase of UHI intensity of 0.19 C per C increase in the daytime temperature. While these results only apply to the domain and period studied, they do confirm recent reports that the UHI intensity increases during heat waves. The results also show that for the cooler parts of the urban fabric (e.g., parks), the UHI intensification during heat waves is around half of that of the dense urban fabric, thus providing some insights into possible mitigation strategies for the future.

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.

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.