Relationships between GPP, Satellite Measures of Greenness and Canopy Water Content with Soil Moisture in Mediterranean-Climate Grassland and Oak Savanna
We investigated the impact of soil moisture on gross primary production (GPP), chlorophyll content, and canopy water content represented by remotely sensed vegetation indices (VIs) in an open grassland and an oak savanna in California. We found for the annual grassland that GPP late in the growing season was controlled by the declining soil moisture, but there was a 10–20-day lag in the response of GPP to soil moisture. However, during the early and middle part of the growing season, solar radiation accounted for most of the variation in GPP. In the oak savanna, the grass understory exhibited a similar response, but oak trees were not sensitive to soil moisture in the upper 50？cm of the soil profile. Furthermore, while we found most VIs to be more or less related to soil moisture, the Visible Atmospherically Resistance Index (VARI) was the most sensitive to the change of soil moisture. 1. Introduction Plant-available soil moisture is a key element in ecosystem functioning, since it links energy balance and hydrological cycles, contributes to vegetation composition and richness, and impacts productivity. California’s Mediterranean climate is characterized by highly variable winter precipitation and prolonged summer drought, and its vegetation communities are strongly affected by the availability of water, resulting in pronounced annual cycles of growth and senescence [1]. For example, the growing season of annual grasslands typically begins in the wet and cool winter after major rain events and extends to the late spring supported by a declining supply of soil moisture. Given the nature of the hydroclimate regime in California, understanding the response of different plant functional types to soil water availability should be a primary objective of any advanced natural resource management system. Semiarid savanna in California, which is composed of widely spaced trees, understory grasses, and forbs, is a common land cover type in California. It has been hypothesized that trees and grasses are able to coexist because of either their differences in resource-acquisition potentials or differences in demographic mechanisms, under such disturbances as fires and grazing [2–4]. In general, spatial niche separation in root distributions appears to be more prevalent in arid systems [5], and plant-available moisture, rather than nutrients, may be the main resource limiting plant growth in savannas [6, 7]. Regardless of the ultimate controls on savanna structure, trees and grasses compete for available soil water, and although grasses are typically superior
References
[1]
J. Major, “California Climate in relation to vegetation,” in ‘Terrestrial Vegetation of California’, M. G. Barbour and J. Major, Eds., pp. 11–74, California Native Plant Society Special Publication 9, 1988.
[2]
H. Walter, Ecology of Tropical and Subtropical Vegetation, Oliver and Boyd, Edinburgh, UK, 1971.
[3]
R. J. Scholes and S. R. Archer, “Tree-grass interactions in Savannas,” Annual Review of Ecology and Systematics, vol. 28, pp. 517–544, 1997.
[4]
A. Kulmatiski, R. J. T. Verweij, K. H. Beard, and E. C. February, “A depth-controlled tracer technique measures vertical, horizontal and temporal patterns of water use by trees and grasses in a subtropical savanna,” New Phytologist, vol. 188, no. 1, pp. 199–209, 2010.
[5]
H. J. Schenk and R. B. Jackson, “Rooting depths, lateral root spreads and below-ground/above-ground allometries of plants in water-limited ecosystems,” Journal of Ecology, vol. 90, no. 3, pp. 480–494, 2002.
[6]
B. H. Walker, D. Ludwig, and S. Holling, “Stability of semi-arid savanna grazing systems,” Journal of Ecology, vol. 69, no. 2, pp. 473–498, 1981.
[7]
F. Van Langevelde, C. A. D. M. Van De Vijver, and L. Kumar, “Effects of fire and herbivory on the stability of savanna ecosystems,” Ecology, vol. 84, no. 2, pp. 337–350, 2003.
[8]
N. B. Thomas, “The control of stomata by water balance,” New Phytologist, vol. 168, pp. 275–292, 2005.
[9]
T. C. Hsiao, “Plant responses to water stress,” Annual Review of Plant Physiology, vol. 24, pp. 519–570, 1973.
[10]
H. K. Lichtenthaler, “Vegetation stress: an introduction to the stress concept in plants,” Plant Physiology, vol. 148, pp. 4–14, 1996.
[11]
O. E. Sala, W. J. Parton, L. A. Joyce, and W. K. Lauenroth, “Primary production of the central grassland region of the United States,” Ecology, vol. 69, no. 1, pp. 40–45, 1988.
[12]
C. K？rner, Alpine Plant Life. In Functional Plant Ecology of High Mountain Ecosystems, Springer, Berlin, Germany, 1999.
[13]
G. Maracci, G. Schmuck, B. Hosgood, and G. Andreoli, “Interpretation of reflectance spectra by plant physiological parameters,” in Proceedings of the International Geoscience and Remote Sensing Symposium (IGARSS '91) Remote Sensing: Global Monitoring for Earth Management, pp. 2303–2306, Helsinki University of Technology, Espoo, Finland, June 1991.
[14]
J. M. Briggs and A. K. Knapp, “Interannual variability in primary production in tallgrass prairie: climate, soil moisture, topographic position, and fire as determinants of aboveground biomass,” American Journal of Botany, vol. 82, no. 8, pp. 1024–1030, 1995.
[15]
A. K. Knapp and M. D. Smith, “Variation among biomes in temporal dynamics of aboveground primary production,” Science, vol. 291, no. 5503, pp. 481–484, 2001.
[16]
L. B. Flanagan, L. A. Wever, and P. J. Carlson, “Seasonal and interannual variation in carbon dioxide exchange and carbon balance in a northern temperate grassland,” Global Change Biology, vol. 8, no. 7, pp. 599–615, 2002.
[17]
L. Xu and D. D. Baldocchi, “Seasonal variation in carbon dioxide exchange over a Mediterranean annual grassland in California,” Agricultural and Forest Meteorology, vol. 123, no. 1-2, pp. 79–96, 2004.
[18]
A. C. Risch and D. A. Frank, “Carbon dioxide fluxes in a spatially and temporally heterogeneous temperate grassland,” Oecologia, vol. 147, no. 2, pp. 291–302, 2006.
[19]
D. D. Baldocchi, “Assessing the eddy covariance technique for evaluating carbon dioxide exchange rates of ecosystems: past, present and future,” Global Change Biology, vol. 9, no. 4, pp. 479–492, 2003.
[20]
A. K. Knapp, J. T. Fahnestock, S. P. Hamburg, L. B. Statland, T. R. Seastedt, and D. S. Schimel, “Landscape patterns in soil-plant water relations and primary production in tallgrass prairie,” Ecology, vol. 74, no. 2, pp. 549–560, 1993.
[21]
J. F. Reynolds, P. R. Kemp, K. Ogle, and R. J. Fernández, “Modifying the ‘pulse-reserve’ paradigm for deserts of North America: precipitation pulses, soil water, and plant responses,” Oecologia, vol. 141, no. 2, pp. 194–210, 2004.
[22]
A. Porporato, F. Laio, L. Ridolfi, and I. Rodriguez-Iturbe, “Plants in water-controlled ecosystems: active role in hydrologic processes and responce to water stress I. Scope and general outline,” Advances in Water Resources, vol. 24, no. 7, pp. 695–705, 2001.
[23]
M. M. Chaves, J. S. Pereira, J. Maroco, et al., “How plants cope with water stress in the field? Photosynthesis and growth,” Annals of Botany, vol. 89, pp. 907–916, 2002.
[24]
E. T. Engman and N. Chauhan, “Status of microwave soil moisture measurements with remote sensing,” Remote Sensing of Environment, vol. 51, no. 1, pp. 189–198, 1995.
[25]
M. C. Dobson and F. T. Ulaby, “Active microwave soil moisture research,” IEEE Transactions on Geoscience and Remote Sensing, vol. 24, no. 1, pp. 23–36, 2007.
[26]
T. Schmugge, P. E. O'Neill, and J. R. Wang, “Passive microwave soil moisture research,” IEEE Transactions on Geoscience and Remote Sensing, vol. 24, no. 1, pp. 12–22, 2007.
[27]
J. A. Gamon, B. Field, M. L. Goulden, et al., “Relationships between NDVI, canopy structure, and photosynthesis in three Californian vegetation types,” Ecological Applications, vol. 5, no. 1, pp. 28–41, 1995.
[28]
A. A. Gitelson and M. N. Merzlyak, “Signature analysis of leaf reflectance spectra: algorithm development for remote sensing of chlorophyll,” Journal of Plant Physiology, vol. 148, no. 3-4, pp. 494–500, 1996.
[29]
E. R. Hunt and B. N. Rock, “Detection of changes in leaf water content using Near- and Middle-Infrared reflectances,” Remote Sensing of Environment, vol. 30, no. 1, pp. 43–54, 1989.
[30]
T. J. Jackson, D. Chen, M. Cosh, et al., “Vegetation water content mapping using landsat data derived normalized difference water index for corn and soybeans,” Remote Sensing of Environment, vol. 92, no. 4, pp. 475–482, 2004.
[31]
T. J. Farrar, S. E. Nicholson, and A. R. Lare, “The influence of soil type on the relationships between NDVI, rainfall, and soil moisture in semiarid Botswana. II.NDVI response to soil moisture,” Remote Sensing of Environment, vol. 50, no. 2, pp. 121–133, 1994.
[32]
P. E. Dennison, D. A. Roberts, S. R. Thorgusen, J. C. Regelbrugge, D. Weise, and C. Lee, “Modeling seasonal changes in live fuel moisture and equivalent water thickness using a cumulative water balance index,” Remote Sensing of Environment, vol. 88, no. 4, pp. 442–452, 2003.
[33]
D. A. Roberts, S. L. Ustin, S. Ogunjemiyo et al., “Spectral and structural measures of northwest forest vegetation at leaf to landscape scales,” Ecosystems, vol. 7, no. 5, pp. 545–562, 2004.
[34]
J. O. Adegoke and A. M. Carleton, “Relations between soil moisture and satellite vegetation indices in the U.S. Corn Belt,” American Meteorology Society, vol. 3, no. 4, pp. 395–405, 2001.
[35]
Y. Gu, E. Hunt, B. Wardlow, J. B. Basara, J. F. Brown, and J. P. Verdin, “Evaluation of MODIS NDVI and NDWI for vegetation drought monitoring using Oklahoma Mesonet soil moisture data,” Geophysical Research Letters, vol. 35, no. 22, Article ID L22401, 2008.
[36]
R. R. Gillies, T. N. Carlson, J. Cui, W. P. Kustas, and K. S. Humes, “A verification of the ‘triangle’ method for obtaining surface soil water content and energy fluxes from remote measurements of NDVI and surface radiant temperature,” International Journal of Remote Sensing, vol. 18, no. 15, pp. 3145–3166, 1997.
[37]
I. Sandholt, K. Rasmussen, and J. Andersen, “A simple interpretation of the surface temperature/vegetation index space for assessment of surface moisture status,” Remote Sensing of Environment, vol. 79, no. 2-3, pp. 213–224, 2002.
[38]
K. Mallick, B. K. Bhattacharya, and N. K. Patel, “Estimating volumetric surface moisture content for cropped soils using a soil wetness index based on surface temperature and NDVI,” Agricultural and Forest Meteorology, vol. 149, no. 8, pp. 1327–1342, 2009.
[39]
I. Rodriguez-Iturbe, “Ecohydrology: a hydrologic perspective of climate-soil-vegetation dynamics,” Water Resources Research, vol. 36, no. 1, pp. 3–9, 2000.
[40]
S. Ma, D. D. Baldocchi, L. Xu, and T. Hehn, “Inter-annual variability in carbon dioxide exchange of an oak/grass savanna and open grassland in California,” Agricultural and Forest Meteorology, vol. 147, no. 3-4, pp. 157–171, 2007.
[41]
L. Xu, D. D. Baldocchi, and J. Tang, “How soil moisture, rain pulses, and growth alter the response of ecosystem respiration to temperature,” Global Biogeochemical Cycles, vol. 18, pp. 4002–4012, 2004.
[42]
D. D. Baldocchi, L. Xu, and N. Kiang, “How plant functional-type, weather, seasonal drought, and soil physical properties alter water and energy fluxes of an oak-grass savanna and an annual grassland,” Agricultural and Forest Meteorology, vol. 123, no. 1-2, pp. 13–39, 2004.
[43]
P. E. Dennison, D. A. Roberts, and S. H. Peterson, “Spectral shape-based temporal compositing algorithms for MODIS surface reflectance data,” Remote Sensing of Environment, vol. 109, pp. 510–522, 2007.
[44]
J. W. Rouse, R. H. Haas, J. A. Schell, and D. W. Deering, “Monitoring vegetation system in the great plains with ERTS,” in Proceedings of the 3rd ERTS Symposium, vol. 1 of NASA SP-351, pp. 309–317, NASA, Washington, DC, USA, 1973.
[45]
A. A. Gitelson, Y. J. Kaufman, R. Stark, and D. Rundquist, “Novel algorithms for remote estimation of vegetation fraction,” Remote Sensing of Environment, vol. 80, no. 1, pp. 76–87, 2002.
[46]
B. C. Gao, “NDWI—A normalized difference water index for remote sensing of vegetation liquid water from space,” Remote Sensing of Environment, vol. 58, no. 3, pp. 257–266, 1996.
[47]
A. Palacios-Orueta, S. Khanna, J. Litago, M. L. Whiting, and S. L. Ustin, “Assessment of NDVI and NDWI spectral indices using MODIS time series analysis and development of a new spectral index based on MODIS shortwave infrared bands,” in Proceedings of the 1st International Conference of Remote Sensing and Geoinformation Processing, pp. 207–209, Trier, Germany, 2006.
[48]
J. A. Gamon, C. B. Field, M. L. Goulden, et al., “Relationships between NDVI, canopy structure, and photosynthesis in three Californian vegetation types,” Ecological Applications, vol. 5, no. 1, pp. 28–41, 1995.
[49]
J. M. Paruelo, H. E. Epstein, W. K. Lauenroth, and I. C. Burke, “ANPP estimates from NDVI for the central grassland region of the United States,” Ecology, vol. 78, no. 3, pp. 953–958, 1997.
[50]
M. L. Whiting, L. Li, and S. L. Ustin, “Predicting water content using Gaussian model on soil spectra,” Remote Sensing of Environment, vol. 89, no. 4, pp. 535–552, 2004.
[51]
S. Khanna, A. Palacios-Orueta, M. L. Whiting, S. L. Ustin, D. Ria？o, and J. Litago, “Development of angle indexes for soil moisture estimation, dry matter detection and land-cover discrimination,” Remote Sensing of Environment, vol. 109, no. 2, pp. 154–165, 2007.
[52]
M. M. Caldwell, T. E. Dawson, and J. H. Richards, “Hydraulic lift: consequences of water efflux from the roots of plants,” Oecologia, vol. 113, no. 2, pp. 151–161, 1998.
[53]
M. A. Davis, K. J. Wrage, and P. B. Reich, “Competition between tree seedlings and herbaceous vegetation: support for a theory of resource supply and demand,” Journal of Ecology, vol. 86, no. 4, pp. 652–661, 1998.
[54]
R. S. Oliveira, L. Bezerra, E. A. Davidson et al., “Deep root function in soil water dynamics in cerrado savannas of central Brazil,” Functional Ecology, vol. 19, no. 4, pp. 574–581, 2005.
[55]
S. Jacquemoud, “Inversion of the PROSPECT + SAIL canopy reflectance model from AVIRIS equivalent spectra: theoretical study,” Remote Sensing of Environment, vol. 44, no. 2-3, pp. 281–292, 1993.
[56]
H. Bach and W. Verhoef, “Sensitivity studies on the effect of surface soil moisture on canopy reflectance using the radiative transfer model GeoSAIL,” in Proceedings IEEE International Geoscience and Remote Sensing Symposium (IGARSS '03), pp. 1679–1681, Toulouse, France, July 2003.
[57]
L. Wang, J. J. Qu, X. Hao, and Q. Zhu, “Sensitivity studies of the moisture effects on MODIS SWIR reflectance and vegetation water indices,” International Journal of Remote Sensing, vol. 29, no. 24, pp. 7065–7075, 2008.
[58]
G. Rondeaux, M. Steven, and F. Baret, “Optimization of soil-adjusted vegetation indices,” Remote Sensing of Environment, vol. 55, no. 2, pp. 95–107, 1996.
