Integrating Optical and Microwave Satellite Observations for High Resolution Soil Moisture Estimate and Applications in CONUS Drought Analyses
Abstract
In this study, optical and microwave satellite observations are integrated to estimate soil moisture at the same spatial resolution as the optical sensors (5km here) and applied for drought analysis in the continental United States. A new refined model is proposed to include auxiliary data like soil texture, topography, surface types, accumulated pre_x005fcipitation, in addition to Normalized Difference Vegetation Index (NDVI) and Land Surface Temperature (LST) used in the traditional universal triangle method. It is found the new proposed soil moisture model using accumulated precipitation demonstrated close agreements with the U.S. Drought Monitor (USDM) spatial patterns. Currently, the USDM is providing a weekly map. Recently, “flash†drought concept appears. To obtain drought map on daily basis, LST is derived from microwave observations and downscaled to the same resolution as the thermal infrared LST product and used to fill the gaps due to clouds in optical LST data. With the integrated daily LST available under nearly all weather conditions, daily soil moisture can be estimated at relatively higher spatial resolution than those traditionally derived from passive microwave sensors, thus drought maps based on soil moisture anomalies can be obtained on daily basis and made the flash drought analysis and monitoring become possible.References
1. Kousky C. Informing climate adaptation: A review
of the economic costs of natural disasters. Energy
Economics 2014; 46: 576-592.
2. Wilhite DA, Glantz MH. Understanding: the drought
phenomenon: the role of definitions. Water interna_x005ftional 1985; 10 (3): 111-120.
3. Svoboda M, LeComte D, Hayes M, Heim R,
Gleason K, Angel J, Rippey B, Tinker R, Palecki M,
Stooksbury D, Miskus D. The drought monitor.
Bulletin American Meteorological Society 2002; 83
(8): 1181-1190.
4. Mote PW. Climate-driven variability and trends in
mountain snowpack in Western North America.
Journal of Climate 2006; 19 (23): 6209-6220.
5. Xia YL, Ek MB, Peters-Lidard CD, Mocko D, Svoboda M, Sheffield J, Wood EF. Application of
USDM statistics in NLDAS-2: Optimal blended
NLDAS drought index over the continental United
States. Journal of Geophysical Research-Atmosphere 2014; 119 (6): 2947-2965.
6. Bolten JD, Crow WT, Zhan XW, Jackson TJ, Reynolds C. Evaluating the utility of remotely sensed soil
moisture retrievals for operational agricultural
drought monitoring. IEEE Journal of Selected Topics in Applied Earth Observation and Remote Sensing 2010; 3 (1): 57-66.
7. Leese J, Jackson T, Pitman A, Dirmeyer P. Meeting
summary: GEWEX/BAHC international workshop
on soil moisture monitoring, analysis, and prediction for hydrometeorological and hydroclimatological applications. Bulletin of American Meteorological Society 2001; 82(7):1423-1430.
8. Zhan X, Miller S, Chauhan N, Di L, Ardanuy P. Soil
moisture visible/infrared radiometer suite algorithm
theoretical basis document 2002. Raytheon Syst.
Company, Lanham, MD.
9. Chauhan NS, Miller S, Ardanuy P. Spaceborne soil
moisture estimation at high resolution: a microwave-optical/IR synergistic approach. International
Journal of Remote Sensing 2003; 24 (22):
4599-4622.
10. Merlin O, Walker JP, Chehbouni A, Kerr Y. Towards deterministic downscaling of SMOS soil
moisture using MODIS derived soil evaporative efficiency. Remote Sensing Environment; 112(10):
3935-3946.
11. Wan ZM, Dozier J. A generalized split-window
algorithm for retrieving land-surface temperature
from space. IEEE Transactions on Geoscience and
Remote Sensing 1996; 34 (4): 892-905.
12. Huete A, Justice C, Van Leeuwen W. MODIS vegetation index (MOD13): Algorithm theoretical basis
document 1999.
13. Huffman G J, Bolvin DT, Nelkin EJ, Wolff DB,
Adler RF, Gu GJ, Hong Y, Bowman KP,
Stocker EF. 2007. The TRMM multisatellite precipitation
analysis (TMPA): Quasi-global, multiyear, combined-sensor precipitation estimates at fine scales.
Journal of Hydrometeorology 2007; 8 (1): 38-55.
14. Gesch D, Oimoen M, Greenlee S, Nelson C, Steuck
M, Tyler D. The national elevation
dataset. Photogrammetric Engineering and Remote
Sensing 2002; 68 (1): 5-32.
15. Henkel M. 2015. 21st Century Homestead: Sustainable Agriculture I, ISBN, 1312939532,
Lulu. com, ch. 2, sec. 1.4, pp. 98- 103.
16. Batjes NH. A world dataset of derived soil properties by FAO–UNESCO soil unit for global
modelling. Soil use and management 1997; 13 (1): 9-16.
17. Jackson TJ. III. Measuring surface soil moisture
using passive microwave remote sensing. Hydrological processes 1993; 7 (2), 139-152.
18. Zhan XW, Liu JC, Zhao LM, Jensen K. Soil Moisture Operational Product System (SMOPS): Algo_x0002_rithm Theoretical Basis Document 2011.
19. Ek MB, Mitchell KE, Lin Y, Rogers E, Grunmann P,
Koren V, Gayno G, Tarpley JD.
Implementation of Noah land surface model advances in
operational mesoscale Eta model. Journal of Geophysical Research: Atmospheres 2003; 108(D22).
20. Koster RD, Suarez MJ. The influence of land surface moisture retention on precipitation
statistics. Journal of Climate 1996; 9(10), 2551-2567.
21. Liang X, Wood EF, Lettenmaier DP. Surface soil
moisture parameterization of the VIC-2L
model: Evaluation and modification. Global and Planetary Change 1996; 13(1-4), 195-206.
22. Xia Y, Mocko D, Huang M, Li B, Rodell M,
Mitchelle KE, Cai X, Ekg MB. Comparison
and assessment of three advanced LSMs in simulating
terrestrial water storage components over the U.S.,
Journal of Hydrometeorology 2017; 18, 625-649.
23. Keys RG. Cubic convolution interpolation for digital image processing. IEEE Trans. Acoust.
Speech Signal Process.1981; 29(6): 1153-1160.
24. Carlson TN, Gillies RR, Perry EM. A method to
make use of thermal infrared temperature and NDVI
measurements to infer surface soil water content
and fractional vegetation cover. Remote Sensing
Reviews 1994; 9 (1-2):161-173.
25. Sun DL, Kafatos M. Note on the NDVI-LST relationship and the use of temperature †related
drought indices over North America. Geophysical
Research Letters 2007; 34 (24).
26. Cleveland WS. Robust locally weighted regression
and smoothing scatterplots. Journal of the American
Statistical Association 1979; 74 (368): 829-836.
27. Reichle RH, Koster RD. Global assimilation of
satellite surface soil moisture retrievals into the
NASA Catchment land surface model. Geophysical
Research Letters 2005; 32 (2).
28. Anderson MC, Hain C, Otkin J, Zhan X, Mo K,
Svoboda M, Wardlow B, Pimstein A (2013). An
intercomparison of drought indicators based on
thermal remote sensing and NLDAS-2 simulations
with US Drought Monitor classifications. Journal of
Hydrometeorology 2013; 14(4): 1035-1056.
29. Anderson M C, Hain C, Wardlow B, Pimstein A,
Mecikalski J R, Kustas W P. Evaluation of
drought indices based on thermal remote sensing of
evapotranspiration over the continental United
States. Journal of Climate 2011; 24(8): 2025-2044.
30. Anderson MC, Norman J M, Mecikalski J R, Otkin
J A, Kustas WP. A climatological study of evapotranspiration and moisture stress across the continental United States based on thermal remote sensing: 1. Model formulation. Journal of Geophysical
Research: Atmospheres 2007; 112(D10).
31. Anderson MC, Norman JM, Mecikalski JR, Otkin
JA, Kustas W P. A climatological study of evapotranspiration and moisture stress across the continental United States based on thermal remote sensing: 2. Surface moisture climatology. Journal of
Geophysical Research: Atmospheres 2007;
112(D11).
32. Norman JM, Kustas WP, Humes KS. Source approach for estimating soil and vegetation energy
fluxes in observations of directional radiometric
surface temperature. Agricultural and Forest Mete-
orology 1995; 77(3), 263-293.
33. Kogan FN. Global drought watch from space. Bulletin American Meteorological Society 1997; 78(4):
621-636.
34. Kogan FN. Application of vegetation index
and brightness temperature for drought detection.
Advanced Space Research 1995; 11: 91–100.
35. Wang PX, Li XW, Gong JY, Song C. Vegetation
temperature condition index and its application for
drought monitoring. In Geoscience and Remote
Sensing Symposium, 2001; 1: 141-143.
36. Lorenz DJ, Otkin JA, Svoboda M, Hain CR, An_x005fderson MC, Zhong Y. Predicting the US Drought
Monitor (USDM) using Precipitation, Soil Moisture,
and Evapotranspiration Anomalies, Part II: Intraseasonal Drought Intensification Forecasts. Journal of Hydrometeorology 2017; 18: 1963-1982.
37. Grigg NS. The 2011–2012 drought in the United
States: new lessons from a record event. International journal of water resources development 2014;
30(2), 183-199.
38. Otkin JA, Anderson MC, Hain C, Svoboda M,
Johnson D, Mueller R, Brown J. Assessing the evolution of soil moisture and vegetation conditions
during the 2012 United States flash drought. Agricultural and Forest Meteorology 2016; 218:
230-242.
39. Hoerling M, Eischeid J, Kumar A, Leung R, Mariotti A, Mo K, Seager R. Causes and predictability
of the 2012 Great Plains drought. Bulletin of the
American Meteorological Society 2014; 95(2):
269-282.
40. Sun, D., Y. Li, X. Zhan, and C. Yang, 2018: Remote
Sensing Land Surface Temperature under nearly all
Weather Conditions through Integrating GOES and
AMSR-2 Observations. International Journal of
Remote Sensing, under review.
