An analysis of atmospheric water vapor variations over a complex agricultural region using airborne imaging spectrometry


Autoři: Sarah W. Shivers aff001;  Dar A. Roberts aff001;  Joseph P. McFadden aff001;  Christina Tague aff002
Působiště autorů: Department of Geography, University of California, Santa Barbara, California, United States of America aff001;  Bren School of Environmental Science & Management, University of California, Santa Barbara, California, United States of America aff002
Vyšlo v časopise: PLoS ONE 14(12)
Kategorie: Research Article
doi: 10.1371/journal.pone.0226014

Souhrn

Understanding atmospheric water vapor patterns can inform regional understanding of water use, climate patterns and hydrologic processes. This research uses Airborne Visible Infrared Imaging Spectrometer (AVIRIS) reflectance and water vapor imagery to investigate spatial patterns of water vapor in California’s Central Valley on a June date in 2013, and 2015, and relates these patterns to surface characteristics and atmospheric properties. We analyze water vapor imagery at two scales: regional and agricultural field, to examine how the slope, intercept, and trajectory of water vapor interact with the landscape in a highly diverse and complex agricultural setting. At the field scale, we found significant quadratic relationships between water vapor slope and wind magnitude in both years (p<0.001). Results showed a positive correlation between crop water use and the frequency with which crops showed directional agreement between wind and water vapor (r = 0.23). At the regional scale, we found patterns of water vapor that indicate advection of moisture across the scene. Water vapor slope was inversely correlated to field size with correlations of -0.37, and -0.28 for 2013 and 2015. No correlation was found between green vegetation fraction and vapor slope (r = 0.001 in 2013, r = 0.02 in 2015), but a weak correlation was found for the intercept (r = 0.11 in 2013, r = 0.26 in 2015). These results lead us to conclude that accumulation of water vapor above fields in these scenes is observable with AVIRIS-derived water vapor imagery whereas advection at the field level was inconsistent. Based on these results, we identify new opportunities to use and apply water vapor imagery to advance our understanding of hydro-climatic patterns and applied agricultural water use.

Klíčová slova:

Agricultural irrigation – California – Crops – Valleys – Vapors – Wind – Advection – Water analysis


Zdroje

1. Trenberth KE, Fasullo J, Smith L. Trends and variability in column-integrated atmospheric water vapor. Clim Dyn. 2005; doi: 10.1007/s00382-005-0017-4

2. Ross RJ, Elliot WP. Radiosonde-based Northern Hemisphere tropospheric water vapor trends. J Clim. 2001; doi: 10.1175/1520-0442(2001)014<1602:RBNHTW>2.0.CO;2

3. Gaffen DJ, Elliott WP, Robock A. Relationships between tropospheric water vapor and surface temperature as observed by radiosondes. Geophys Res Lett. 1992.

4. Ogunjemiyo S, Roberts DA, Keightley K, Ustin SL, Hinckley T, Lamb B. Evaluating the relationship between AVIRIS water vapor and poplar plantation evapotranspiration. Journal of Geophysical Research Atmospheres. 2002. doi: 10.1029/2001JD001194

5. Gao B-C, Goetz AFH. Column atmospheric water vapor and vegetation liquid water retrievals from Airborne Imaging Spectrometer data. J Geophys Res. 1990; doi: 10.1029/JD095iD04p03549

6. Green RO, Eastwood ML, Sarture CM, Chrien TG, Aronsson M, Chippendale BJ, et al. Imaging spectroscopy and the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS). Remote Sens Environ. 1998; doi: 10.1016/S0034-4257(98)00064-9

7. Thompson DR, Gao BC, Green RO, Roberts DA, Dennison PE, Lundeen SR. Atmospheric correction for global mapping spectroscopy: ATREM advances for the HyspIRI preparatory campaign. Remote Sens Environ. 2015.

8. Siebert S, Döll P. The Global Map of Irrigation Areas. 2007.

9. Mount J, Hanak E. Water Use in California. PPIC Water Policy Cent. 2016.

10. California Department of Water Resources (CA DWR). Agricultural Water Use Efficiency. State of California; c2019 [cited 2019 March 7]. https://water.ca.gov/Programs/Water-Use-And-Efficiency/Agricultural-Water-Use-Efficiency.

11. Glenn EP, Huete AR, Nagler PL, Hirschboeck KK, Brown P. Integrating remote sensing and ground methods to estimate evapotranspiration. Critical Reviews in Plant Sciences. 2007.

12. Faunt CC. Groundwater Availability of the Central Valley Aquifer, California. US Geol Surv. 2009.

13. Lo MH, Famiglietti JS. Irrigation in California’s Central Valley strengthens the southwestern U.S. water cycle. Geophys Res Lett. 2013; doi: 10.1002/grl.50108

14. Gordon LJ, Steffen W, Jönsson BF, Folke C, Falkenmark M, Johannessen Å. Human modification of global water vapor flows from the land surface. Proc Natl Acad Sci. National Academy of Sciences; 2005;102: 7612–7617. doi: 10.1073/PNAS.0500208102 15890780

15. Carrère V, Conel JE. Recovery of atmospheric water vapor total column abundance from imaging spectrometer data around 940 nm—sensitivity analysis and application to Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) data. Remote Sens Environ. 1993; doi: 10.1016/0034-4257(93)90015-P

16. Roberts DA, Green RO, Adams JB. Temporal and spatial patterns in vegetation and atmospheric properties from AVIRIS. Remote Sens Environ. 1997; doi: 10.1016/S0034-4257(97)00092-8

17. Gao BC, Goetz AFH. Retrieval of equivalent water thickness and information related to biochemical components of vegetation canopies from AVIRIS data. Remote Sens Environ. 1995; doi: 10.1016/0034-4257(95)00039-4

18. Rocken C, Ware R, Van Hove T, Solheim R, Alber C, Johnson J, Bevis M, Businger S, Sensing atmospheric water vapor with the Global Positioning System, Geophys. Res. Lett. 20, 2631, 1993

19. Shivers S, Roberts DA, McFadden JP, Using paired thermal and hyperspectral aerial imagery to quantify land surface temperature variability and assess crop stress within California orchards, Remote Sens. Environ., 2019, 222, 215–231. doi: 10.1016/j.rse.2018.12.030

20. Jackson RD, Idso SB, Reginato RJ, Pinter PJ. Canopy temperature as a crop water stress indicator. Water Resour Res. 1981; doi: 10.1029/WR017i004p01133

21. Tanner CB. Plant Temperatures1. Agron J. American Society of Agronomy; 1963;55: 210. doi: 10.2134/agronj1963.00021962005500020043x

22. Jarvis PG, Mcnaughton KG. Stomatal Control of Transpiration: Scaling Up from Leaf to Region. Adv Ecol Res. 1986; doi: 10.1016/S0065-2504(08)60119-1

23. Asbjornsen H, Goldsmith GR, Alvarado-Barrientos MS, Rebel K, Van Osch FP, Rietkerk M, et al. Ecohydrological advances and applications in plant-water relations research: A review. Journal of Plant Ecology. 2011. doi: 10.1093/jpe/rtr005

24. California Department of Food & Agriculture (CDFA) [Internet]. California Agricultural Statistics Review 2015–16. State of California; c2016 [cited 2019 March 7]. https://www.cdfa.ca.gov/statistics/PDFs/2016Report.pdf.

25. California Department of Water Resources (CA DWR) [Internet]. California Water Plan Update 2013. Volume 2, Regional Reports. c2013 [cited 2019 March 7]. https://water.ca.gov/LegacyFiles/waterplan/docs/cwpu2013/Final/Vol2_TulareLakeRR.pdf

26. Carle D. Introduction to water in California. No. 76. Univ of California Press; 2004.

27. National Academies of Sciences, Engineering, and Medicine. Thriving on Our Changing Planet: A Decadal Strategy for Earth Observation from Space. National Academies Press. 2018. https://doi.org.10.17226/24938.

28. Hook SJ, Myers JJ, Thome KJ, Fitzgerald M, Kahle AB. The MODIS/ASTER airborne simulator (MASTER)—A new instrument for earth science studies. Remote Sens Environ. 2001; doi: 10.1016/S0034-4257(00)00195-4

29. ImSpec LLC. ACORN 6.0 User’s Guide. 2002.

30. Berk A, Conforti P, Kennett R, Perkins T, Hawes F, Van Den Bosch J. MODTRAN® 6: A major upgrade of the MODTRAN® radiative transfer code. Workshop on Hyperspectral Image and Signal Processing, Evolution in Remote Sensing. 2017.

31. Kruse FA. Comparison of atrem, acorn, and flaash atmospheric corrections using low-altitude aviris data of boulder, co. 13th JPL Airborne Geosci Work Jet Propuls Lab. 2004;

32. Roberts DA, Gardner M, Church R, Ustin S, Scheer G, Green RO. Mapping Chaparral in the Santa Monica Mountains Using Multiple Endmember Spectral Mixture Models. Remote Sens Environ. 1998;65: 267–279. doi: 10.1016/S0034-4257(98)00037-6

33. Roberts DA, Smith MO, Adams JB, Green vegetation, nonphotosynthetic vegetation and soils in AVIRIS data, Remote Sens. Environ. 1993; 44, 2–3, 255–269. doi: 10.1016/0034-4257(93)90020-X

34. Legates DR, Willmott CJ. Mean seasonal and spatial variability in gauge-corrected, global precipitation. Int J Climatol. John Wiley & Sons, Ltd; 1990;10: 111–127. doi: 10.1002/joc.3370100202

35. Dirks KN, Hay JE, Stow CD, Harris D. High-resolution studies of rainfall on Norfolk Island: Part II: Interpolation of rainfall data. J Hydrol. Elsevier; 1998;208: 187–193. doi: 10.1016/S0022-1694(98)00155-3

36. Luo W, Taylor MC, Parker SR. A comparison of spatial interpolation methods to estimate continuous wind speed surfaces using irregularly distributed data from England and Wales. Int J Climatol. John Wiley & Sons, Ltd; 2008;28: 947–959. doi: 10.1002/joc.1583

37. Shivers S, Roberts D, McFadden J, Tague C. Using Imaging Spectrometry to Study Changes in Crop Area in California’s Central Valley during Drought. Remote Sens. 2018; doi: 10.3390/rs10101556

38. Irrigation Training & Research Center. California Crop and Soil Evapotranspiration. Irrigation Training & Research Center, California Polytechnic State University, San Luis Obispo, California, USA. ITRC Report No. R 03–001. 65 pp; 2003.

39. McAneney KJ, Brunet Y, Itier B. Downwind evolution of transpiration by two irrigated crops under conditions of local advection. J Hydrol. Elsevier; 1994;161: 375–388. doi: 10.1016/0022-1694(94)90136-8

40. Zermeño-Gonzalez A, Hipps LE. Downwind evolution of surface fluxes over a vegetated surface during local advection of heat and saturation deficit. J Hydrol. Elsevier; 1997;192: 189–210. doi: 10.1016/S0022-1694(96)03108-3

41. Chapin FS III, Matson PA, Mooney HA. Principles of terrestrial ecosystem ecology. 2nd ed. New York: Springer; 2011.

42. Ben-Dor E, Kindel BC, Patkin K. A comparison between six model-based methods to retrieve surface reflectance and water vapor content from hyperspectral data: A case study using synthetic \ldots. Á Present Int \ldots. 2005;


Článek vyšel v časopise

PLOS One


2019 Číslo 12