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We present a data set on remote sensing reflectance (RRS) at 1nm resolution from 350 to 800nm obtained from measurements in the coastal and open ocean areas of the South China Sea and Sulu Sea from 18 to 27 November 2011. For the measurements we used radiometric hyperspectral (3.3 nm sampling, 10 nm FWHM) underwater profile measurements down to the 0.1 % light level using RAMSES (TriOS GmbH, Germany) sensors which measured depth resolved the upwelling radiance and downwelling irradiance, both corrected by incident sunlight fluctuations with a second RAMSES sensor measuring the above water downwelling irradiance. The later sensor data were also used to finally calculate RRS. We followed the protocol by Mueller et al. (2003) further modified by Matsuoka et al. (2007) and Stramski et al. (2008), as described for our instrument set-up in Taylor et al. (2011). Our method is further described and assessed for its uncertainty in Tilstone et al. (2020). The campaign is described in detail in Cheah et al. (2013) and was also optical constituents hyperspectral absorption data (Bracher et al. 2021a, b) and phytoplankton pigments (Bracher 2014) were measured. We are indebted to Maria Altenburg-Soppa, Sonja Wiegmann and Joseph Palermo for their assistance in the sampling on RV Sonne and acknowledge the help of the chief scientist Birgit Quack, the crew and captain of the RV Sonne during SHIVA-Sonne in performing our measurements.

We present a data set on remote sensing reflectance (RRS) at 1nm resolution from 350 to 800nm obtained from measurements in the North Sea and Sogne Fjord from 30 April to 7 May 2016. For the measurements we used radiometric hyperspectral (3.3 nm sampling, 10 nm FWHM) underwater profile measurements down to the 0.1 % light level using RAMSES (TriOS GmbH, Germany) sensors which measured depth resolved the upwelling radiance and downwelling irradiance, both corrected by incident sunlight fluctuations with a second RAMSES sensor measuring the above water downwelling irradiance. The later sensor data were also used to finally calculate RRS. We followed the protocol by Mueller et al. (2003) further modified by Matsuoka et al. (2007) and Stramski et al. (2008), as described for our instrument set-up in Taylor et al. (2011). Our method is further described and assessed for its uncertainty in Tilstone et al. (2020). The same campaign was sampled for optical constituents hyperspectral absorption data in Bracher et al. (2021a-d) and for phytoplankton pigments in Bracher and Wiegmann (2019).

Phenology is the study of reoccurring events during a year or season. It can be linked to the behavior of animals, such as phases of mating, breeding, or movement and to events such as green-up, bud burst, flowering, or senescence when referring to vegetation, as a response to changing environmental factors throughout a season. While these changes can be tracked on the level of individual species, their observation is usually restricted to small spatial extents. To broaden the extent of the observed area remote sensing data have been proven useful. As remote sensing data capture the seasonal change rather on a pixel than on a species level, they enable to analyze the phenology of the observed vegetation on a different scale, which is known as land surface phenology. Land surface phenological metrics that can, for example, be derived from time series of vegetation indices, allow to analyze the observed spatial and temporal patterns in relation to ecosystem processes (e.g., primary productivity). Subsequently, the derived metrics can be grouped based on their similarities into ecosystem functional types (EFT), defined as areas with comparable energy and matter flows between the environment and the biotic community. However, the spatial resolution of the data used is crucial, which becomes even more critical when looking at heterogeneous ecosystems such as the Brazilian savanna, known as the Cerrado. The Cerrado covers an extent of approximately 2 mio. km², hosts many endemic species and is considered as a biodiversity hotspot that provides several ecosystem services of national and even global importance. However, due to a lack of extensive conservation regulations the Cerrado is prone to land cover changes for agricultural expansion, highlighting the need for detailed mapping and monitoring approaches. To reveal and analyze the spatial patterns of the remaining share of natural vegetation based on their land surface phenology, we analyzed a dense 8-day time series of combined enhanced vegetation data derived from Landsat 7 ETM+ and Landsat 8 images. Data gaps that were due to cloud contamination or sensor errors were filled using a radial basis convolution filter, enabling to subsequently derive phenological metrics for the season 2013/2014 using TIMESAT (Eklundh and Jönsson 2017). As these variables, such as start and end of season, amplitude or the base value, relate to the seasonality and primary productivity of the observed vegetation, we clustered them based on their similarities and defined 8 ecosystem functional types (EFT) of the Cerrado. The GeoTiff file contains the 8 EFTs that are explained in detail in Schwieder et al. 2020. For further questions please contact Marcel Schwieder. Class labels: 0 = Unclassified 1 = FORMBMS 2 = SAFORMS 3 = FORHBLS 4 = GLSAVLB 5 = GLSAVHB 6 = FORHBHS 7 = VEGINMS 8 = VEGINLS

This data set provides the collocated data of remote sensing reflectance (Rrs) at 9 bands extracted from the merged ocean color products from GlobColour archive (https://www.globcolour.info/), satellite sea surface temperature from CMEMS (https://marine.copernicus.eu/), and chlorophyll a concentrations (Chl-a) derived from a global database of in situ HPLC pigment data collected from 2002 to 2012. The total Chl-a, Chl-a of six phytoplankton functional types (PFTs) that are diatoms, dinoflagellates, haptophytes, green algae, prokaryotes and Prochlorococcus, and two fractions of prokaryotes and Prochlorococcus are included in this data set. PFT Chl-a and fractions are derived using an updated diagnostic pigment analysis (DPA) method (Soppa et al., 2014; Losa et al., 2017), that was originally developed by Vidussi et al. (2001), adapted in Uitz et al. (2006) and further refined by Hirata et al. (2011) and Brewin et al. (2015). Matchups of satellite Rrs to in situ PFT data (which were also matchups to SST) were extracted from global 4-km daily merged products. Extraction and averaging protocol including quality control were described in detail in Xi et al. (2020).

This dataset provides a static extent of salt pans and salt playas (a total area of ~10 thousand km²) across the arid/semiarid western United States (US). The considered region (an area of ~700 thousand km²), also given in this dataset, includes the endorheic (i.e., hydrologically landlocked) areas in the Great Basin, the Mojave Desert, the north part of the Chihuahua Desert, and other sporadic endorheic basins in the western US, with a small fraction extending to northern Mexico (see Figure 2 in Safaee and Wang (2020) for the area boundary). The salt pan/playa extents were mapped using a calibrated support vector machine (SVM) from the 30-m-resolution multispectral Landsat-8 Operational Land Imager (OLI) images that were acquired in June during 2013 to 2015. The mapping result may represent the maximum salt pan/playa condition during this three-year period. In addition, this dataset also provides the OLI-based training pool that was used by the SVM to perform the binary classification between salt-covered surface and others (soil and barren land). This training pool was generated using a stratified sampling method which randomly selected an equal number of pixels from each of the eight training sites worldwide, for both salt and non-salt regions. A total of 8000 sampling points (4000 for salt and 4000 for non-salt) are included in the sample pool. Refer to Safaee and Wang (2020) for more technical details. Please also see Data_description (within zip file, or see "Further details").

High-latitude ecosystems play an important role in the global carbon cycle and in regulating the climate system and are presently undergoing rapid environmental change. Accurate land cover data sets are required to both document these changes as well as to provide land-surface information for benchmarking and initializing Earth system models. Earth system models also require specific land cover classification systems based on plant functional types (PFTs), rather than species or ecosystems, and so post-processing of existing land cover data is often required. This study compares over Siberia, multiple land cover data sets against one another and with auxiliary data to identify key uncertainties that contribute to variability in PFT classifications that would introduce errors in Earth system modeling. Land cover classification systems from GLC 2000, GlobCover 2005 and 2009, and MODIS collections 5 and 5.1 are first aggregated to a common legend, and then compared to high-resolution land cover classification systems, vegetation continuous fields (MODIS VCFs) and satellite-derived tree heights (to discriminate against sparse, shrub, and forest vegetation). The GlobCover data set, with a lower threshold for tree cover and taller tree heights and a better spatial resolution, tends to have better distributions of tree cover compared to high-resolution data. It has therefore been chosen to build new PFT maps for the ORCHIDEE land surface model at 1 km scale. Compared to the original PFT data set, the new PFT maps based on GlobCover 2005 and an updated cross-walking approach mainly differ in the characterization of forests and degree of tree cover. The partition of grasslands and bare soils now appears more realistic compared with ground truth data. This new vegetation map provides a framework for further development of new PFTs in the ORCHIDEE model like shrubs, lichens and mosses, to represent the water and carbon cycles in northern latitudes better. Updated land cover data sets are critical for improving and maintaining the relevance of Earth system models for assessing climate and human impacts on biogeochemistry and biophysics. NetCDF file includes 16 Plant Functional Type Maps:PFT1 - Bare SoilPFT2 - Tropical Broad_leaved EvergreenPFT3 - Tropical Broad_leaved RaingreenPFT4 - Temperate Needleleaf EvergreenPFT5 - Temperate Broad_leaved EvergreenPFT6 - Temperate Broad_leaved SummergreenPFT7 - Boreal Needleleaf EvergreenPFT8 - Boreal Broad-leaved SummergreenPFT9 - Boreal Needleleaf SummergreenPFT10 - C3 GrassPFT11 - C4 GrassPFT12 - C3 AgriculturePFT13 - C4 AgriculturePFT14 - WaterPFT15 - Snow/IcePFT16 - No Data Supplement to: Ottlé, Catherine; Lescure, Julie; Maignan, Fabienne; Poulter, Benjamin; Wang, Tao; Delbart, Nicolas (2013): Use of various remote sensing land cover products for plant functional type mapping over Siberia. Earth System Science Data, 5(2), 331-348

There is a demand for regularly updated, broad-scale, accurate land cover information in Victoria from multiple stakeholders. This paper documents the methods used to generate an annual dominant land cover (DLC) map for Victoria, Australia from 2009 to 2013. Vegetation phenology parameters derived from an annual time series of the Moderate Resolution Imaging Spectroradiometer Vegetation Indices 16-day 250 m (MOD13Q1) product were used to generate annual DLC maps, using a three-tiered hierarchical classification scheme. Classification accuracy at the broadest (primary) class level was over 91% for all years, while it ranged from 72 to 81% at the secondary class level. The most detailed class level (tertiary) had accuracy levels ranging from 61 to 68%. The approach used was able to accommodate variable climatic conditions, which had substantial impacts on vegetation growth patterns and agricultural production across the state between both regions and years. The production of an annual dataset with complete spatial coverage for Victoria provides a reliable base data set with an accuracy that is fit-for-purpose for many applications. The Victorian Dominant Land Cover data set is a spatial grid of land cover classification for the state of Victoria from 2009-2013 created by the Spatial Sciences Group of the Agriculture Research Division in the Victorian Department of Economic Development, Jobs, Transport and Resources. For each year, three spatial grids are provided which give differing levels of dominant land cover class detail (primary, secondary and tertiary). The land cover classes are derived from satellite imagery (MODIS). Supplement to: Sheffield, Kathryn; Morse-McNabb, Elizabeth; Clark, Rob; Robson, Susan; Lewis, Hayden (2015): Mapping dominant annual land cover from 2009 to 2013 across Victoria, Australia using satellite imagery. Scientific Data, 2, 150069

Projects include:* Estonian Ministry of Education and Research* Estonian Research Council* European Commission, Award: FP7, H2020, FP7-ENV-2007-1-226224* Federal Ministry for Economic Affairs and Energy, Germany, Award: LAKESAT 50EE1340* Federal Ministry of Education and Research Germany, Award: 03G0218A* Helmholtz Infrastructure Initiative, Award: FRAM* NASA ROSES, Award: 80HQTR19C0015, 80NSSC 21K0499, 80NSSC22K1389* New Zealand Ministry for Business, Innovation & Employment, Award: UOWX1503, UOWX1802, KENTR1601* USGS Landsat Science Team Award, Award: 140G0118C0011* Vietnam National Foundation for Science and Technology Development (NAFOSTED), grant number 105.08-2019.329 The GLObal Reflectance community dataset for Imaging and optical sensing of Aquatic environments (GLORIA) includes 7,572 curated hyperspectral remote sensing reflectance measurements at 1 nm intervals within the 350 to 900 nm wavelength range. In addition, at least one co-located water quality measurement, chlorophyll a, total suspended solids, absorption by dissolved substances, and Secchi depth, is provided. The data were contributed by researchers affiliated with 53 institutions worldwide and come from 450 different water bodies, making GLORIA the de-facto state of knowledge of in situ coastal and inland aquatic optical diversity.

The data consists of multiple different files which include data on the Land Use and Land Cover and their change within the region, as well as locally collected precipitation and temperature data, NDCI and GLH data showcasing algal bloom activity, and vegetation indices. Most of the data was collected remotely using Satellite data, this data was collected during the Summer of 2021 and consists of monthly data points for the 2017-2020 time period, covering the greater Okavango Delta region. The weather data was collected locally on a monthly basis for the same 2017-2020 time period near the Kasane airport in Northern Botswana. The data was collected to determine the driving landscape and climate factors behind the increased algal and toxic cyanobacterial activity within the waters of the Okavango Delta. As mentioned earlier, the bulk of the data used was remote satellite data, specifically Sentinel-2 data. Sentinel-2 data is freely available . The data was collected using Google Earth Engine and the Copernicus website (https://scihub.copernicus.eu/dhus/# /home), and worked with in both SNAP and ERDAS Imagine software. The LULC maps were created using ERDAS Imagine unsupervised classification. The weather data was locally collected using a meteorological station located on the Kasane airport.