Live fuel moisture content across the Western United States
Live fuel moisture is defined as the mass of water per unit mass of live biomass (expressed as a percentage quantity). Here, live fuel moisture was estimated from a deep learning model trained using Sentinel-1 C-band backscatter, optical reflectance, and various other land surface characteristics like canopy height, soil texture, etc (training data can be downloaded at the Radiant MLHub Training Data Registry). The dataset is available at 15-day temporal resolution and 250 m spatial resolution and spans April 2016 to December 2021, after which Sentinel-1 B failures unfortunately limit the availability.
Rao, K., Williams, A.P., Flefil, J.F. & Konings, A.G. (2020). SAR-enhanced mapping of live fuel moisture content. Remote Sensing of Environment, 245, 111797. DOI: 10.1016/j.rse.2020.111797
Global plant hydraulic traits
We used a model-data fusion approach to determine several xylem and stomatal (maximum conductance, P50, etc) traits at 0.25 degree resolution based on remotely sensed ET (ALEXI) and by using AMSR-E VOD as a proxy of leaf water potential combined with a hydraulically-enabled hydrologic model. A Markov Chain Monte Carlo was used to estimate the traits. The data can be downloaded from Figshare.
Liu, Y., N.M. Holtzman, and A.G. Konings (2021). Global ecosystem-scale plant hydraulic traits retrieved using model-data fusion. Hydrology and Earth System Science, 25:2399-2417. DOI: 10.5194/hess-25-2399-2021
Isohydricity at global and CONUS scale from AMSR-E VOD
Ecosystem-scale isohydricity estimates derived from AMSR-E VOD at 1:30 AM and 1:30 PM can be found on github. Different versions of this dataset exist depending on the rain filtering used – see the README in the Github repo.
Konings, A.G and, P. Gentine (2017). Global Variations in Ecosystem-Scale Isohydricity. Global Change Biology, 23(2): 891-905. DOI: 10.1111/gcb.13389
Konings, A.G., A.P. Williams, and P. Gentine (2017). Sensitivity of grassland productivity to aridity controlled by stomatal and xylem regulation. Nature Geoscience, 10: 2290-2299. DOI: 10.1038/ngeo2903
Li, Y., K. Guan, P. Gentine, A.G. Konings, F.C. Meinzer, J.S. Kimball, X. Xu, W.R.L. Anderegg, N.G. McDowell, J. Martinez-Vilalta, D.G. Long, and S.P. Good (2017). Estimating global ecosystem iso/anisohydry using active and passive microwave satellite data. Journal of Geophysical Research – Biogeosciences: 122:3306-3321.
Drainage canals in Southeast Asian peatlands
A map of drainage canals was derived at 5 m resolution from 2017 Planet Basemaps imagery using a convolutional neural network. This map spans Sumatra, Borneo, and Peninsular Malaysia. Data can be visualized and downloaded here.
Dadap, N. C., A. M. Hoyt, A. R. Cobb, D. Oner, M. Kozinski, P. V Fua, K. Rao, C. F. Harvey, and A. G. Konings (2021). Drainage Canals in Southeast Asian Peatlands Increase Carbon Emissions. AGU Advances, 2(1): 1–14, doi:10.1029/2020AV000321.
Oner, D., M. Koziński, L. Citraro, N. C. Dadap, A. G. Konings, and P. Fua. Promoting Connectivity of Network-Like Structures by Enforcing Region Separation. IEEE Transactions on Pattern Analysis and Machine Intelligence. 44(9): 5401-5413. doi:10.1109/TPAMI.2021.3074366.
SMAP simultaneous retrievals of soil moisture and VOD from MT-DCA
SMAP Multi-Temporal Dual-Channel Algorithm (MT-DCA) retrievals at 9 km can be downloaded by anonymous ftp at ftp://pangea.stanford.edu/konings/MT-DCA. Make sure to use FTP, not SFTP. It is easiest to first connect to the base hostname and then navigate to the MT-DCA directory. Note that this is a fairly large directory (~100 GB). A detailed README is included on the server.
The above datasets only span until 2017. For the latest and up-to-date retrievals of SMAP MT-DCA VOD and soil moisture (as of August 2021), please see the SMAP MT-DCA Zenodo repository.
Dadap, N.C., A.R. Cobb, A.M. Hoyt, C.F. Harvey, and A.G. Konings (2019): Satellite soil moisture observations predict fire vulnerability in Southeast Asian peatlands, Environmental Research Letters, 14, 094014. DOI:10.1088/1748-9326/ab3891
Konings, A.G., M. Piles, N. Das, and D. Entekhabi (2017). L-band vegetation optical depth and effective scattering albedo estimation from SMAP. Remote Sensing of Environment, 198:460-470. DOI: 10.1016/j.rse.2017.06.037
Konings, A.G.*, M. Piles*, K. Rötzer, K.A. McColl, S. Chan, and D. Entekhabi (2016). Vegetation optical depth and scattering albedo retrieval using time-series of dual-polarized L-band radiometer observations. Remote Sensing of Environment. 172, 178-189. DOI: 10.1016/j.rse.2015.11.009
Heterotrophic respiration from carbon balance inversion
We derived estimates of spatio-temporal variability of heterotrophic respiration at monthly resolution and 4x5 degree spatial resolution spanning the period 2010-2012. These are created by combining data from CMS-Flux, GOME-2 Solar-induced Fluorescence, and CARDAMOM derived vegetation carbon use efficiency. These data can be found on Dryad.
Konings, A.G., A.A. Bloom, J. Liu, N.C. Parazoo, D.S. Schimel, and K.W. Bowman (2019): Global, satellite-driven estimates of heterotrophic respiration, Biogeosciences, 16 (11), 2269-2284. DOI: 10.5194/bg-16-2269-2019.