Remote Sensing of Environment

Clasificación por cuartil de las revistas editadas en Colombia en Scopus en 2021. Se agrupa por institución editora. El grafico es sensible y se pueden seleccionar todas las revistas, uno solo cuartil o varios cuartiles. Este tipo de grafico HTML se puede incrustar en versiones HTML y XML de artículos científicos ofreciendo interacción a los lectores.

This dataset encompasses a large number (> 700) of in-situ observations over elemantary sampling units (about 20m2) of LAI (effective and actual), fAPAR and fraction of vegetation cover (fCover) collected over a network of agricultural sites during the ImagineS project (http://fp7-imagines.eu/) in the period 2013-2016. The ground dataset was collected with digital hemispherical photography (DHP), LAI2200, and AccuPAR devices following well stablished protocols in agreement with CEOS LPV good practices. The ground data is complemented with concomitant Landsat-8/OLI observations, the sun zenith angle at the acquisition and the NDVI. This results in a unique database to calibrate and validate algorithms for retrieval of biophysical variables over crops.

This data set describes a global land/water mask for satellite ocean color studies in a medium resolution (~230m/pixel; or 480 pixels per degree of longitude or latitude). Please acknowledge any use of this data set by citing the following reference: K. Mikelsons, M. Wang, X. Wang, and L. Jiang, Global land mask for satellite ocean color remote sensing, Remote Sens. Environ., 257, 112356 (2021). https://doi.org/10.1016/j.rse.2021.112356 This data set was derived from five data sources described in the following references: [1] Carroll, M. L., Townshend, J. R., DiMiceli, C. M., Noojipady, P., and Sohlberg, R. A. (2009), A new global raster water mask at 250 m resolution, Int. J. Digit. Earth, 2, 291–308. [2] Carroll, M. L., DiMiceli, C. M., Townshend, J. R., Sohlberg, R. A., Elders, A. I., Devadiga, S., Sayer, A. M., and Levy, R. C. (2017), Development of an operational land water mask for MODIS Collection 6, and influence on downstream data products, Int. J. Digit. Earth, 10, 207–218. [3] Pekel, J. F., Cottam, A., Gorelick, N., and Belward, A. S. (2016), High-resolution mapping of global surface water and its long-term changes, Nature, 540, 418–422. [4] Hansen, M. C., Potapov, P. V., Moore, R., Hancher, M., Turubanova, S. A., Tyukavina, A., Thau, D., Stehman, S. V., Goetz, S. J., Loveland, T. R., Kommareddy, A., Egorov, A., Chini, L., Justice, C. O., and Townsend, J. R. G. (2013), High-resolution global maps of 21st-century forest cover change, Science, 342, 850–853. [5] www.OpenStreetMap.org

The file is meant for replication of heat storage in urban areas using Scikit-learn's histogram-based gradient boosted regression tree routine.

Data includes: 1) Landsat and DEM imagery 2) The study area polygons 3) M. micrantha detection info

Ecolight-input-combined.xls Input for ecolight-s runs - biogeophysical and atmospheric parameters used for inversion algorithm and forward modelling. Biogeophysical parameters: Chla=chlorophyll conc mg/m3, PC = phycocyanin (mg/m3), TSS=suspended solids (g/m3), ISS=inorganic solids (g/m3), ag440=absorption by gelbstoff at 440nm(1/m),ap440=absorption by particulate matter at 440 nm (1/m), adp440=absorption by depigmentede matter at 440 nm(1/m), S = slope coefficient, Zsd = sechi disk depth (m). Atmospheric parameters: J = julian day, Lat & Lon = Latitude &longitude, GMT=greenwhich meridian time, Theta=solar zenith in degrees, pres=atm. pressure in inches mercury, am=air mass type, rh=rel humidity, wv=precipitable water vapour in cm, wsm = 24 hour mean sind speed (m/s), ws = wind speed (m/s), vis=visibility in km, ro3=ozone in Dobson units, AOT550 is aerosol optical thickness at 550 nm, cloud=cloud from 0-1, Wind dir in degrees (0 = N). Rrs_combined.xlsx Remote sensing reflectance measured using ASD Fieldspec 3 and methods of Meuller 2003 (Ocean optics protocols). Column headers correspond to sample numbers. First column is wavelength 350 to 999 nm. Data corresponds to Ecolight_inputs_combined.xls. Further details contained in files.

This is the database used to relate spatial variability of spectral severity indices (dNBR, RBR and RdNBR) derived from Sentinel 2 MSI and Landsat 8 OLI in Yeste fire (SE, Spain) with prefire vegetation data derived from Lidar, National Forest Inventory and biophysical variables derived from prefire Sentinel 2 MSI image; and burning conditions (weather conditions, aligned winds and topography to the fire-front direction and fire progression map).

This dataset includes all variables used to assess differences in freeze/thaw products across global climate, land cover, and elevation. This includes modified versions of Koppen-Geiger climate and MODIS IGBP land cover classifications used in this study. Files containing the cropped Global Multi-resolution Terrain Elevation Data 2010 elevation data and "freezing days" derived using the Global Land Data Assimilation Data are also included. Files containing/defining the overlapping classification regions for the Soil Moisture Active Passive freeze/thaw and the Freeze/Thaw Earth System Data Record domains. Additionally, the spatial map of agreement for the entire study period (April 1, 2015 - December 31, 2017) is included. All files have an accompanied text file which include necessary data descriptions. SMAP and FT-ESDR data are hosted on the NSIDC website and can be accessed there. Necessary data citations are provided in the manuscript.

PRI, environmental, pigments and photosynthesis data

This dataset depicts Belize's national mangrove cover for the period November 1980 through February 2017, based on the ~1990 baseline established by Simon Zisman. The dataset was developed by using satellite imagery to detect where how mangrove cover has changed between 1980 and 2017. The source data for this effort include Landsat-3 Multispectral Scanner (MSS) imagery, Landsat-5 Thematic Mapper (Landsat TM) imagery, Landsat-7 Enhanced Thematic Mapper Plus (Landsat ETM+) imagery, and Landsat-8 Operational Land Imager (OLI) imagery. A full description of the process by which the data were generated is provided in “Use of public Earth Observation data for tracking progress in sustainable management of coastal forest ecosystems in Belize, Central America,” by Cherrington et al.