Temperature time series with high spatial and temporal resolutions are important for several applications. The new MODIS Land Surface Temperature (LST) collection 6 provides numerous improvements compared to collection 5. However, being remotely sensed data in the thermal range, LST shows gaps in cloud-covered areas. With a novel method [1] we fully reconstructed the daily global MODIS LST products MOD11A1/MYD11A1 (spatial resolution: 1 km). For this, we combined temporal and spatial interpolation, using emissivity and elevation as covariates for the spatial interpolation. Here we provide a time series of these reconstructed LST data aggregated as daily LST maps at overpass time (approx: 01:30 am, 10:30am, 1:30pm 10:30pm). [1] Metz M., Andreo V., Neteler M. (2017): A new fully gap-free time series of Land Surface Temperature from MODIS LST data. Remote Sensing, 9(12):1333. DOI: http://dx.doi.org/10.3390/rs9121333 The data are provided in GeoTIFF format. The Coordinate Reference System (CRS) is identical to the MOD11A1/MYD11A1 product (Sinusoidal) as provided by NASA. In WKT as reported by GDAL: PROJCRS["unnamed", BASEGEOGCRS["Unknown datum based upon the custom spheroid", DATUM["Not specified (based on custom spheroid)", ELLIPSOID["Custom spheroid",6371007.181,0, LENGTHUNIT["metre",1, ID["EPSG",9001]]]], PRIMEM["Greenwich",0, ANGLEUNIT["degree",0.0174532925199433, ID["EPSG",9122]]]], CONVERSION["unnamed", METHOD["Sinusoidal"], PARAMETER["Longitude of natural origin",0, ANGLEUNIT["degree",0.0174532925199433], ID["EPSG",8802]], PARAMETER["False easting",0, LENGTHUNIT["Meter",1], ID["EPSG",8806]], PARAMETER["False northing",0, LENGTHUNIT["Meter",1], ID["EPSG",8807]]], CS[Cartesian,2], AXIS["easting",east, ORDER[1], LENGTHUNIT["Meter",1]], AXIS["northing",north, ORDER[2], LENGTHUNIT["Meter",1]]] Acknowledgments: We are grateful to the NASA Land Processes Distributed Active Archive Center (LP DAAC) for making the MODIS LST data available. The dataset is based on MODIS Collection V006. Meaning of pixel values: The pixel values are coded in Kelvin * 50 Data type: raster, UInt16 Spatial resolution: 926.62543314 m Spatial extent Sinusoidal (W, S, E, N): 0, 4447802.079066, 2223901.039533, 6671703.118599 Spatial extent in EPSG:4326 (W, S, E, N): 0, 40, 40, 60

The data set documents a Germany-wide and spatio-temporally consistent 1 × 1 km² analysis-ready time series (ARD-TS) of interpolated DOYs (days of the year) covering 56 beginning phenological development stages (phases) of 9 main crop types for the period between 1993 and 2021. The derivation is based on Germany-wide observations of the German Weather Service (DWD), which were statistically filtered and interpolated.

The German Weather Service operates a phenological observation network. About 1200 observers monitor 160 phenological phases of wild and cultivated plants. The PHASE model was developed to interpolate the phenological observations for the entire territory of Germany (Gerstmann et al. (2016) Rel.Identifer TAB 8). The model combines the concept of growing degree days (GDD) with a geostatistical interpolation procedure. The PHASE model was applied to create a Germany-wide and spatio-temporally consistent 1 × 1 km² analysis-ready time series (ARD-TS) of interpolated DOYs (days of the year) covering 56 beginning phenological development stages (phases) of 9 main crop types for the period between 1993 and 2021. The dataset includes the following information:

• Germany-wide interpolated temperature data from the German Weather Service (DWD),
• raster datasets of interpolated crop-specific and Germany-wide incipient phenological development stages for the period between 1993 and 2020. The value in each pixel of these rasters represents the Day Of the Year (DOY) of the respective beginning phenological plant development stage,
• accuracy metrics (RMSE, MSE, MAE, and R²) for each Germany-wide interpolation result.

The code of the phase model is documented in a software repository (Rel.Identifer TAB 9 and 10). The temporally static model input data are also stored there.

Research question
The dataset allows the spatio-temporal definition of phenological windows for any available year and user-defined region (Möller et al. (2020) Rel.Identifer TAB 1). Such information is important for various agricultural issues such as the derivation of weather or biodiversity indices, crop classification, soil erosion or crop yield modeling (Bucheli et al. (2022) Rel.Identifer TAB 5; Gerstmann et al. (2018) ; Rel.Identifer TAB 3; Möller et al. (2017, 2018) ; Rel.Identifer TAB 2 and 4; Riedsel et al. (2022) ; Rel.Identifer TAB 6).

All relvant papers are listed under RelatedIdentifier.
A form for creating an individual WCS can be found here:
https://sf.julius-kuehn.de/openapi/phase/

This data set is a spatial and temporal variation map of temperate grassland types in Eurasia, China regional classification map (1980S).The data is in TIF raster format, and the spatial resolution is 1km. The values of the three-level classification of thermal grassland are 1-8, respectively: :1- Temperate meadow grassland;2- Typical temperate grassland;3- Temperate desertification grassland;4- Temperate grassland desert;5- temperate desert and three non-temperate grassland types (6- alpine grassland, 7- other vegetation area, 8- non-vegetation area). Based on the data set of vegetation map of the people's Republic of China (1:1 000 000 000) hosted by the Institute of Botany, Chinese Academy of Sciences, and combined with historical and meteorological data, the vegetation map of the people's Republic of China contains 11 vegetation type groups, 55 vegetation types and 960 vegetation types in 1980s Based on the historical meteorological data from 1980 to 1989, combined with satellite data for further analysis and correction, and spatial interpolation calculation, we obtained the three-level classification of temperate grassland in China. The data can be used to analyze the spatial and temporal variation of temperate grassland in Eurasia.