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15 results for “Vegetation water content”
Soil salinity and water content at GCE-LTER vegetation monitoring plots in October 2010
Soil samples were collected in conjunction with Fall 2010 plant monitoring at half of the permanent vegetation monitoring plots in the creekbank and midmarsh zones at 10 GCE study sites. Pore-water salinity was determined by analysis of supernatant salinity in dried soil samples hydrated with a measured volume of deionized water.
Satellite soil and vegetation water content data capturing main terrestrial ecosystem changes
<p>I)SUMMARY</p> <p>This repository contains a harmonized database for the study of terrestrial ecosystem changes published in [Bueso et al., 2021]. It covers the period June 2010 - July 2020 and includes the following variables, which were harmonized to a common spatial scale of 25km and monthly temporal resolution and clustered as detailed in [Bueso et al., 2021]:</p> <p>- SM: soil moisture from SMOS-IC v2<br> - VOD: vegetation optical depth from SMOS-IC v2<br> - NDVI: Normalized Vegetation Difference Index from MODIS, product MOD13Q1 v6<br> - PREC: Rainfall from PERSIANN-CDR v2.2.</p> <p>Additionally, land cover information from MODIS MCD12Q1 collection 6 for years 2011 and 2019 is provided for each cluster.</p> <p>II) CONTACT</p> <p>For questions, please e-mail Diego Bueso at diego.bueso@uv.es</p> <p>III) DATABASE</p> <p>We provide the maps of identified clusters by quantile of SM and VOD and the code to generate Figs 1 and 2 of supplementary material in [Bueso et al., 2023]. We then provide for each identified cluster .mat files containing the variables described above. Further details are in the readme.txt file</p> <p>IV) CITE</p> <p>To properly acknowledge the dataset we kindly encourage users to (1) cite the DOI as an in-text citation and/or in the data acknowledgements in any publication and (2) reference the following publication: </p> <p>D. Bueso, M. Piles, P. Ciais, J-P. Wigneron, Á. Moreno-Martínez, G. Camps-Valls, "Soil and vegetation water content identify the main terrestrial ecosystem changes", National Science Review, 2023, <a href="https://doi.org/10.1093/nsr/nwad026">https://doi.org/10.1093/nsr/nwad026</a> </p>
Asynchronous changes in precipitation and soil water content decelerate alpine vegetation greening
<p>data for "Asynchronous changes in precipitation and soil water content decelerate alpine vegetation greening".</p>
Theory of Maximum Entropy Production (MEP) and Its Application to Microwave Remote Sensing - Simultaneous Retrieval of Soil Moisture and Vegetation Water Content
<p>A theory of maximum entropy production (MEP) for electromagnetic wave propagation in dielectric materials is proposed and applied to simultaneously retrieving soil moisture (SM) and vegetation water content (VWC) from L-band microwave brightness temperature (TB). One representation of the MEP principle states that a non-equilibrium system corresponds to such a configuration of energy fluxes that minimizes a dissipation function under the constraint of energy conservation. The dissipation function for radiative transfer is formulated as an analogy of that for heat transfer. A new physical parameter, radiative inertia as an analogy of thermal inertia, is introduced to characterize radiative attenuation in dielectric media. The radiative inertia is parameterized in terms of the penetration depth of electromagnetic waves as a function of the complex dielectric constant. The MEP based retrieval algorithm predicts SM and VWC by minimizing the dissipation function under the constraint of the conservation of radiative energy. The retrievals of SM and VWC based on the MEP theory were validated against field observations in tropical and temperate forested regions of the Amazon and North America. The proof-of-concept analysis demonstrates the capability of the MEP algorithm for simultaneous retrievals of SM and VWC even for dense canopy (e.g. VWC > 5 kg m-2). The MEP method is a new theoretical framework for developing innovative remote sensing algorithms of the Earth system not limited to just microwave observations.</p><p>Note: We would appreciate if users contact us for the use of the data.</p>
SMEX03 Vegetation Water Content: Oklahoma, Version 1
Notice to Data Users: The documentation for this data set was provided solely by the Principal Investigator(s) and was not further developed, thoroughly reviewed, or edited by NSIDC. Thus, support for this data set may be limited.This data set consists of Vegetation Water Content (VWC) data for two Soil Moisture Experiment 2003 (SMEX03) regional study areas, Oklahoma North and Oklahoma South. VWC was derived from the Normalized Difference Water Index (NDWI) which was obtained from Landsat 5 Thematic Mapper (TM) and Landsat 7 Enhanced Thematic Mapper (ETM+) imagery.
SMEX05 Vegetation Water Content Data: Iowa, Version 1
Notice to Data Users: The documentation for this data set was provided solely by the Principal Investigator(s) and was not further developed, thoroughly reviewed, or edited by NSIDC. Thus, support for this data set may be limited.This data set contains vegetation water content data of the Ames, Iowa, USA area. A vegetation sampling campaign was conducted in coordination with satellite calibration and correction to result in an estimated map of Vegetation Water Content (VWC).
SMEX04 Vegetation Water Content Data, Sonora, Version 1
Notice to Data Users: The documentation for this data set was provided solely by the Principal Investigator(s) and was not further developed, thoroughly reviewed, or edited by NSIDC. Thus, support for this data set may be limited.This data set consists of Vegetation Water Content (VWC) data for two Soil Moisture Experiment 2004 (SMEX04) regional study areas: Arizona, USA and Sonora, Mexico. VWC was derived from the Normalized Difference Infrared Index (NDII) which was obtained from Landsat 5 Thematic Mapper (TM) imagery.
SMEX04 Vegetation Water Content Data, Arizona, Version 1
Notice to Data Users: The documentation for this data set was provided solely by the Principal Investigator(s) and was not further developed, thoroughly reviewed, or edited by NSIDC. Thus, support for this data set may be limited.This data set consists of Vegetation Water Content (VWC) data for two Soil Moisture Experiment 2004 (SMEX04) regional study areas: Arizona, USA and Sonora, Mexico.
SMEX02 Vegetation Water Content, Iowa Regional and Walnut Creek Watershed, Version 1
This data set consists of Vegetation Water Content (VWC) data.
SMAPVEX12 Vegetation Water Content Map V001
The daily Vegetation Water Content (VWC) maps for the Soil Moisture Active Passive Validation Experiment 2012 (SMAPVEX12) were derived by calculating Normalized Difference Vegetation Index (NDVI) from SPOT and RapidEye satellite overpasses and then interpolating it for each day of the campaign. In addition, samples from a range of vegetation types were used to compare ground-based measurements to the satellite-based estimates.
CLASIC07 Vegetation Water Content Map V001
The Vegetation Water Content (VWC) map for the Cloud and Land Surface Interaction Campaign 2007 (CLASIC07) was derived by calculating Normalized Difference Water Index (NDWI) from ResourceSat-1 satellite imagery.
SMAPVEX08 Vegetation Water Content Map V001
The Vegetation Water Content (VWC) map for the Soil Moisture Active Passive Validation Experiment 2008 (SMAPVEX08) was derived by calculating Normalized Difference Water Index (NDWI) from Satellite Pour l'Observation de la Terre-4 (SPOT-4) overpasses on 11 October 2008. In addition, samples from a range of vegetation types were used to compare VWC and NDWI to the satellite imagery.
CLASIC07 Vegetation Water Content Map V001
The Vegetation Water Content (VWC) map for the Cloud and Land Surface Interaction Campaign 2007 (CLASIC07) was derived by calculating Normalized Difference Water Index (NDWI) from ResourceSat-1 satellite imagery.
SMAPVEX12 Vegetation Water Content Map V001
The daily Vegetation Water Content (VWC) maps for the Soil Moisture Active Passive Validation Experiment 2012 (SMAPVEX12) were derived by calculating Normalized Difference Vegetation Index (NDVI) from SPOT and RapidEye satellite overpasses and then interpolating it for each day of the campaign. In addition, samples from a range of vegetation types were used to compare ground-based measurements to the satellite-based estimates.
SMAPVEX08 Vegetation Water Content Map V001
The Vegetation Water Content (VWC) map for the Soil Moisture Active Passive Validation Experiment 2008 (SMAPVEX08) was derived by calculating Normalized Difference Water Index (NDWI) from Satellite Pour l'Observation de la Terre-4 (SPOT-4) overpasses on 11 October 2008. In addition, samples from a range of vegetation types were used to compare VWC and NDWI to the satellite imagery.
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International Brain Laboratory public data
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OpenNeuro
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