Transcript Spatial and Temporal Analysis of Soil Moisture using MODIS NDVI and LST Products J.M.

Spatial and Temporal Analysis of Soil Moisture using
MODIS NDVI and LST Products
J.M. Shawn
1Department
1
Hutchinson ,
of Geography and
Thomas J.
1
Vought ,
2Department
of Biological and Agricultural Engineering, Kansas State University, Manhattan, Kansas 66506
and Stacy L.
Impact of Maneuver Training on NPS Pollution
Military readiness depends upon high quality training. Effective maneuver training
requires large areas of land and creates intense stress on this land. Environmental
protection requirements place additional restrictions on land use and availability.
Because military training schedules are set well in advance to make the best use of
installation training facilities and National Training Centers, there is little flexibility to
modify training events and maintain readiness. In order to avoid maneuver restrictions,
proactive management plans must be developed giving commanders the information they
need to assess the environmental cost of training and management practices that reduce
the environmental impact.
Non-point source (NPS) pollution has been called the nation’s largest water quality
problem, and its reduction is a major challenge facing our society today. As of 1998 over
290,000 miles of river, almost 7,900,000 acres of lake and 12,500 square miles of
estuaries failed to meet water quality standards. Military training maneuvers have the
potential to significantly alter land surfaces in a manner that promotes NPS pollution,
resulting in the inability of military installations to meet water quality standards and the
decline of training lands.
The overall objective of the parent project of this research, funded through CP1339
(Characterizing and Monitoring Non-point Source Runoff from Military Ranges and
Identifying their Impacts to Receiving Water Bodies) is to identify sources of NPS
pollution resulting from military activities, assess the impact of this pollution on surface
water quality, and provide information for commanders to lessen the impact of training on
water quality (Figure 1). Investigators are assessing the impact of two major sources of
NPS pollution on surface water quality at Fort Riley, Kansas: (1) erosion from upland
training areas and (2) channel erosion at stream crossing sites.
Researchers are using watershed water quality models in conjunction with remotely
sensed information and geographic information systems (GIS) to assess the impact of
training on water quality, in particular on the amount of soil erosion. A pair of decision
matrices, the first addressing the generation of NPS pollution and the second the
potential to exceed TMDL regulations for NPS pollutants (i.e., an Environmental Decision
Support Tool), will be created for assessing the environmental cost of training maneuvers
(Figure 2). In addition, researchers are collecting surface runoff at three buffer sites to
determine the effect of vegetated buffers for controlling NPS pollution and using new realtime data collection systems to assess the impact of vehicle crossings on stream water
quality and erosion dynamics at Low Water Stream Crossings (LWSCs).
2
Hutchinson
Overall Technical Approach
DATA COLLECTION
Assess/Identify
NPS Pollution
MODELING
Quantify
Vegetation
Impacts
ASSESSMENT
Characterize
Stream
Sediment
Buffer
Field Study
DESIGN
Real-Time
Sediment Load
Sensor
Buffer Model
Development
NPS Pollution
Modeling
Stream
Crossing
Evaluations
Environmental
Decision Support
Tool
DELIVERABLE
Figure 1. Technical approach of the project, “Assessing the Impact
of Maneuver Training on NPS Pollution and Water Quality.”
VWC = 19.204 + 0.091 (NDVI) – 0.039 (LST)
Figure 4. Landuse and landcover of Fort Riley, Kansas.
Potential NPS Pollution
Generation
Specifically, the relationship between land surface temperature (LST) and NDVI will be
investigated (see Figure 3) and, if valid, each satellite image product will be used as
independent variables in a linear regression model to predict volumetric water content
(VWC) of near surface soils.
The study site is Fort Riley, Kansas (Figure 4). Located in the northeastern portion of the
state, Fort Riley is an Army training installation, approximately 39,800 hectares in area,
for multiple brigades of armored and mechanized infantry units.
Near real time MODIS satellite data products were obtained from the EOS Data Gateway.
Land surface temperature (Figure 5) and NDVI data (Figure 6) is in the form of 8-day and
16-day maximum value composites, respectively, at a spatial resolution of 1 kilometer.
Soil moisture was measured weekly at 80 control points using a portable time-domain
reflectometer (TDR). If multiple points were located within the same 1 km x 1 km image
grid cell, TDR measurements were averaged to define soil moisture at that location. In a
geographic information system (GIS), LST, NDVI, and TDR points were overlayed to
create a table of values that could be analyzed statistically.
Graphs
Field data collection will continue and more accurate and precise measurement
techniques will be incorporated (e.g., gravimetric sampling). Concurrent research is
comparing MODIS enhanced vegetation index (EVI) with NDVI by composite period,
and over time, to assess the suitability of EVI as a replacement vegetation index. In
addition to testing various data scaling techniques to “standardize” LST and VI values,
nonlinear regression models will be explored to improve variable significance and the
accuracy of predicted VWC values.
References
Carlson, T.N., R.R. Gillies, and T.J. Schmugge. 1995. An interpretation of
methodologies for indirect measurement of soil water content. Agricultural and Forest
Meteorology 77(3-4):191-205.
Figure 2. Decision support tools designed to assist installation
officials better evaluate the potential environmental impact of
scheduled training activities.
Figure 5. NDVI values for Fort Riley from the composite period
including June 9, 2004 Field sampling sites shown as point
features.
Gillies, R.R. and T.N. Carlson. 1995. Thermal remote sensing of surface soil-water
content with partial vegetation cover for incorporation into climate models. Journal of
Applied Meteorology 34(4):745-756
Gillies, R.R. T.N. Carlson, J. Cui, W.P. Kustas, and K.S. Humes. 1997. A verification
of the ‘triangle’ method for obtainin surface soil water content and energy fluxes from
remote measurements of the normalized difference vegetation Index (NDVI) and
surface radiant temperature. International Journal of Remote Sensing 18(15):31453166.
Goward, S. N., C. J. Tucker, and D. G. Dye. 1985. North American vegetation
patterns observed with the NOAA-7 advanced very high resolution radiometer,
Vegetatio 64:3-14.
Normalized Difference Vegetation Index, NDVI
(Min = -1.0 to Max = +1.0)
The objective of this subtask of the SERDP-funded project, “Impact of Maneuver Training
on Water Quality and NPS Pollution”, is to develop spatially- and temporally-distributed
estimates of near surface soil moisture. These estimates will be used to evaluate
antecedent soil moisture conditions and used as input into a landscape-scale surface
water quality model to evaluate the effectiveness of riparian buffers in filtering sediments
transported from upland sites within mechanized military training areas.
Figure 7. Predicted VWC values for Fort Riley during the 16 day
composite period including June 9, 2004. Field sampling sites
shown as point features.
LST and NDVI values vary significantly among image dates within study area,
indicating a scaling technique may be necessary for a single regression-based model
to be applicable. The field sampling date/composite period of June 9 with the largest
variation in NDVI, LST, and soil wetness values produced the best linear regression
model (Figure 7), despite weakest negative correlation between LST and NDVI. Other
dates showed a significant negative relationship between LST and NDVI. However,
the more more homogeneous dry or wet conditions yielded poor model results.
Environmental Decision
Support Tool
Soil moisture is a critical variable that contributes to the physical processes,
biogeochemistry, and human systems that influence global change (Henderson-Sellers
1996). Increasingly, remotely sensed data are being used in land surface climatology
research and modeling efforts. In addition, antecedent soil moisture conditions affect the
hydrologic behavior of an area through the partitioning of precipitation into runoff and
storage terms. However, the value of soil moisture as an environmental descriptor or as
model input is lessened by our inability to measure it in a consistent and spatially
comprehensive manner. At the root of this problem is the natural spatial and temporal
variability of soil moisture conditions, caused by the heterogeneity of soil properties,
topography, land cover, and precipitation.
Estimating Soil Moisture via Remote Sensing
SE = 9.3
Preliminary Findings
Soil Moisture – A Critical Variable
In remote sensing, plant spectral reflectance characteristics permit ability to sense
variations in green biomass while the small thermal mass of plant leaves distinguishes
green vegetation from soil backgrounds (Tucker 1979, Goward et al. 1985, Carlson et al.
1995). For many years, surface radiant temperature measurements used to define
model parameters of soil moisture availability and thermal inertia (Gillies and Carlson
1995). Other research has shown that a strong negative relationship between surface
temperature (Ts) and normalized difference vegetation index (NDVI) over different
biomes, the slope of which can be used as landscape-level proxy for canopy resistance
(Rc) and “wetness” (Nemani and Running 1989; Nemani et al. 1993) (Figure 3). Others
have illustrated “inversion” methods for computing surface soil water content from
measurements of surface temperature and a vegetation index (Gillies et al. 1997).
R2 = 0.11
Henderson-Sellers, A. 1996. Soil moisture: A critical focus for global change studies.
Global and Planetary Change 13:3-9.
Nemani, R.R. and S.W. Running. 1989. Estimation of regional surface-resistance to
evapotranspiration from NDVI and thermal-IR AVHRR data. Journal of Applied
Meteorology 28(4):276-284.
NDVI Image
LST Image
Nemani, R.R., L. Pierce, S.W. Running, and S. Goward. 1993. Developing satellitederived estimates of surface moisture status. Journal of Applied Meteorology
32(3):548-557.
Tucker, C.J. 1979. Red and photographic infrared linear combinations for monitoring
vegetation. Remote Sensing of the Environment 8:127-150.
Acknowledgements
Land Surface Temperature, LST (oC)
Figure 3. Scatterplot of LST and NDVI from three image dates for
Fort Riley and surrounding counties showing typical relationship
between the two measurements.
Figure 6. LST values for Fort Riley from the composite period
including June 9, 2004. Field sampling sites shown as point
features.
This project is funded by the Strategic Environmental Research and Development
Program through CP1339 (Characterizing and Monitoring Non-point Source Runoff
from Military Ranges and Identifying their Impacts to Receiving Water Bodies). Coinvestigators of this project are (from Kansas State University) James M. Steichen,
Phillip L. Barnes, Naiqian Zhang, Charles G. Oviatt, Naiqian Zhang, and (from the Fort
Riley Integrated Training Area Management (ITAM) Program) Philip B. Woodford.
Field work during year one of this research effort was performed by graduate students
Scott Leis, Ben White, and Brooke Stansberry (Department of Geography, Kansas
State University).