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 October 2020.
- 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
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 at github: http://github.com/agkonings/isohydricity. 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.DOI: 10.1002/2017JG003958
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. One key implementation feature is highlighted here: the MT-DCA optimizes, alongside VOD and soil moisture, the single-scattering albedo for each pixel. The albedo is assumed constant across the entire period. This means that all retrievals change every time the record is extended, which is a somewhat undesirable situation from a software stability situation (though it has the advantage of continually refining estimates of optimal average albedo). In this dataset, the albedo was optimized over the first year of the record (April 1st 2015 to March 31st, 2016), so that data during this period exactly match those described in the Konings et al, RSE 2017 paper. Then, starting on April 1st, 2016 and beyond, the pixel-specific value of albedo is set as fixed based on the results from the retrieval in Year 1. The soil moisture and VOD are then retrieved based on a moving window as in the rest of the MT-DCA retrievals, but without the additional albedo optimization loop. Note that these datasets only span until 2017. For up-to-date retrievals of SMAP MT-DCA VOD and soil moisture (as of August 2020), please take a look at Andrew Feldman (MIT)'s page.
- 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: https://datadryad.org/stash/dataset/doi:10.5061/dryad.298rn73.
- 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.