Spatial and Temporal Analysis of Soil Moisture usingSpatial and Temporal Analysis of Soil Moisture usingMODIS NDVI and LST ProductsMODIS NDVI and LST ProductsJ.M. Shawn Hutchinson1, Thomas J. Vought1, and Stacy L. Hutchinson2

1Department of Geography and 2Department of Biological and Agricultural Engineering, Kansas State University, Manhattan, Kansas 66506

Impact of Maneuver Training on NPS Pollution 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 real-time data collection systems to assess the impact of vehicle crossings on stream water quality and erosion dynamics at Low Water Stream Crossings (LWSCs).

Soil Moisture – A Critical VariableSoil Moisture – A Critical VariableSoil 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.

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).

Estimating Soil Moisture via Remote SensingEstimating Soil Moisture via Remote SensingThe 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.

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.

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Preliminary FindingsPreliminary FindingsLST 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.

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.

ReferencesReferencesCarlson, 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.

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):3145-3166.

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.

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.

Nemani, R.R., L. Pierce, S.W. Running, and S. Goward. 1993. Developing satellite-derived 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.

AcknowledgementsAcknowledgementsThis 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). Co-investigators 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).

Figure 1. Technical approach of the project, “Assessing the Impact of Maneuver Training on NPS Pollution and Water Quality.”

Figure 2. Decision support tools designed to assist installation officials better evaluate the potential environmental impact of scheduled training activities.

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.

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Figure 6. LST values for Fort Riley from the composite period including June 9, 2004. Field sampling sites shown as point features.

Figure 5. NDVI values for Fort Riley from the composite period including June 9, 2004 Field sampling sites shown as point features.

Potential NPS Pollution Potential NPS Pollution GenerationGeneration

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VWC = 19.204 + 0.091 (NDVI) – 0.039 (LST) R2 = 0.11 SE = 9.3

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 4. Landuse and landcover of Fort Riley, Kansas.

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