INTRODUCTION
The logic behind the surface moisture
index has been extensively described elsewhere (see Nemani and Running, 1989).
Briefly, it relies on calculating the relationship between NDVI and Ts.
Generally, for a given landscape, as NDVI increases, Ts will decrease.
This is due to vegetation’s ability to regulate Ts by partitioning absorbed
radiation to latent heat flux (via evapotranspiration) rather than sensible
heat flux. Absorbed radiation and water availability are the two primary
controls on Ts for a given surface. As water becomes limited at that surface,
whether vegetated or not, the absorbed energy will be partitioned to sensible
heat flux and the radiant temperature of that surface will increase. The
core of the SMI logic relies on these biophysical principals for monitoring
surface moisture status. If a surface is wet, Ts will be low. However,
as that surface dries, the Ts will increase accordingly. The relative
increase in Ts is more significant in low NDVI areas, corresponding to bare
soil or sparse vegetation. In high NDVI areas the relative change in Ts
is not as noticeable due to the aforementioned ability of vegetation to regulate
water relations. This is particularly true of forested areas that have access
to sub-surface water. The result is a negative relationship between NDVI
and Ts. As a given area dries, we would expect the relationship between
NDVI and Ts, as measured by the slope of a line fit to the Ts/NDVI scatterplot,
to become increasingly negative due to an increased Ts for the low NDVI areas.
It is this relationship that is the logical basis for the fire potential and
drought indices.
ONGOING ACTIVITIES
Inclusion of landscape surface meteorological variables derived from gridded DAYMET surfaces will allow a more robust assesment of the remotely sensed relationship between near surface air temperature and a spectral vegetation index for assessing the surface moisture status. This improved Surface Moisture Index (SMI) will allow conversion to more timely and rigorous analysis of drought condition and fuel moisture. Historic analysis of SMI variability will provide context for current condition in relation to the long term changes in surface moisture. Knowing where current condition lies relative to known historic condition will allow us to better assess drought severity and potential for fire growth.
REFERENCES
Nemani, R., L. Pierce, S. Running,
and S. Goward. 1993. "Developing Satellite-derived Estimates of Surface
Moisture Status. Journal of Applied
Meteorology. 32(3):548-557.
Nemani, R. and S. Running.
1989. "Estimation of Regional Surface Resistance to Evapotranspiration from
NDVI and Thermal-IR AVHRR
Data." Journal of Applied Meteorology. 28(4):276-284.
Riddering, James P., Seielstad,
Carl A., Queen, LLoyd P. May,1999. Developing a Computationally Efficient
Fire Potential Index from Satellite Derived Estimates of Surface Moisture Status.
Proceedings of the American Society of Photogrammetry and Remote Sensing Annual
Conference. Portland, Oregon.