Soil Moisture Estimation Using Hyperspectral SWIR Imagery Poster Number IN43B-1184 D. Lewis, Institute for Technology Development, Building 1103, Suite 118, Stennis Space Center, MS; M. Finn, USGS National Geospatial Technical Operations Center, Rolla, MO, INTRODUCTION ANALYSIS / RESULTS CONCLUSIONS The U.S. Geological Survey (USGS) is engaged with the U.S. Department of Agriculture’s (USDA) Agricultural Research Service (ARS) and the University of Georgia’s National Environmentally Sound Production Agriculture Laboratory (NESPAL) both in Tifton, Georgia, USA, to develop transformations for medium and high resolution remotely sensed images to generate moisture indicators for soil. The Institute for Technology Development (ITD) is located at the Stennis Space Center in southern Mississippi. ITD has developed hyperspectral sensor systems that, when mounted in aircraft, collect electromagnetic reflectance data of the terrain. The sensor suite consists of sensors for three different sections of the electromagnetic spectrum; the Ultra-Violet (UV), Visible/Near InfraRed (VNIR) and Short Wave InfraRed (SWIR). The USDA/ARS’ Southeast Watershed Research Laboratory has probes that measure and record soil moisture. Data taken from the ITD SWIR sensor and the USDA/ARS soil moisture meters were analyzed to study relationships between SWIR data and measured soil moisture. METHODOLOGY The ITD sensors collect two dimensional spatial (image) data sets at a variety of different wavelengths. When stacked together, these image data sets create hyperspectral data cubes with two dimensions containing spatial information and the third dimension containing spectral information. These HyperSpectral Imaging (HSI) sensors record hyperspectral data cubes for targets of interest. On March 6, 2007 ITD collected data with the SWIR HSI sensor over 29 soil moisture meters managed by USDA/ARS near Tifton, Georgia. Hyperspectral data collected by the HSI sensor was linked to the soil moisture meter data recorded by the soil moisture meter station to explore relationships between the data sets. Data is measured and recorded by the soil moisture meter stations every half hour continuously throughout the year. Figure 1 shows images of soil moisture meters with labels 32 and 52. Regions of Interest (ROI) were drawn using the ENVI image processing application over the image data set corresponding with the location of the soil moisture meters. Scripts written in the Interactive Data Language (IDL) were developed to use the ROIs to extract the spectra from each of the processed hyperspectral image data over each soil moisture meter. Each ROI covered a 3 by 3 pixel mask around the soil moisture meter. The spatial resolution of the image data was just over 6 meters. So the ROI for each soil moisture meter was approximately 20 x 20 meters. The resulting spectral information was recorded in a comma delimited text file. Figure 5 shows the image surrounding soil moisture meter labeled 26, with the location of the soil moisture meter station indicated by a red box. Tn Analytical Spectral Devices (ASD) SWIR radiometer, provided by the USGS, was used in collaboration with the University of Missouri – Rolla’s Department of Civil, Architectural & Environmental Engineering. During the time period that the aircraft was collecting imagery over the calibration tarps, radiometer data was also being recorded by the ASD radiometer. Regression analysis was later performed using the radiometer data and hyperspectral image data to perform atmospheric correction. A section of the flightline containing the calibration tarps and soil moisture meter 66 is shown in Figure 3 with the red, green, blue display colors driven by wavelengths 1227, 1031, and 1559 nanometer bands respectively. Figure 1: Soil Moisture Meters 32 (left) and 52 (right) The conclusion of this study is that there is a relationship between SWIR data and soil moisture. The results for the 2 inch model, shown if Table 1, are highly significant with an R 2 value of The wavelengths that contribute to these models are in the 1300 to 1550 range (Table 2). This data set showed capability for detecting soil moisture at the 8 and 12 inch depths also. In fact, the 8 inch depth model had an R 2 value of 0.75, which was slightly better than the 2 inch depth model. The 12 inch depth model had a slightly lower R 2 value of All were significant at the.05 level, with the 2 and 8 inch models being significant at the.01 level. During the course of the study several objects were identified that interfered with the reflection of light from the area around the soil moisture meter station. The fact that these stations were not designed to be used in remote sensing projects, testifies to the resilience of the models. However, it might be advantageous to design future experiments with more control over the items in the 20 by 20 meter area around the soil moisture meters. In summary, this project used SWIR airborne hyperspectral imagery to model soil moisture with R 2 values in the.62 through.75 R 2 range for the 3 soil depths in this study. In order to prepare the data set for analysis, standard preprocessing steps were performed. These standard processes included sensor calibration, spectral subsetting, and atmospheric calibration. Figure 3 shows range of light pixels to dark pixels over the calibration tarps in the image data. This range covers 6 horizontal pixels and since there were not 8 pure pixels across the calibration tarp, the regression used an averaged value of the radiometer data from the three highest tarp sections paired with the brightest 2 pixels from the image data for the high bright end of the atmospheric correction regression line. The radiometer data averaged across the three high calibration tarps is shown in Figure 4. The wavelength region for the SWIR sensor is roughly between 900 and 1700 nanometers. Except for a few dips, the shape of the radiometer data in this region can be seen to stay around 55% reflectance. The low point on the atmospheric correction regression line used values generated by the sensor’s dark current image and paired them with 0% reflectance. These data pairings were used to generate a linear calibration regression equation for each spectral band. These regression equations were used to transform all the raw image data to percent reflectance. All the image files were calibrated using the regression equations. The calibration of the image file containing the calibration tarps is of especial interest. The regression equation should transform the pixels over the calibration tarp to be similar to the radiometer data. Figure 4 shows the spectral curve of the bright calibration tarps, which corresponds closely with the radiometer data of the bright calibration tarps. The analysis proceeded with the expectation that the regression equations would adequately perform atmospheric correction. The Statistical Analysis Software (SAS) was used for analysis. Discriminant Analysis was performed to identify key discriminating wavelengths. All 29 soil moisture meter data and spectra pairings were used in a General Linear Model (GLM) for the 2 inch, 8 inch and 12 inch soil moisture meter data with bands 29, 30, 32, 33, 34,36, 38, 39, and 43 of the calibrated data. The results, shown in Table 1, reveal the 2 inch and 8 inch models to be significant at the.01 level. The 12 inch model is significant at the 0.05 level. The R 2 values for the 2 inch, 8 inch and 12 inch models are.71,.75 and.62 respectively. A GLM was also generated for the raw data with similar significance and R2 values, but different model coefficients. The wavelengths associated with these bands are shown in Table 2. In order to perform atmospheric correction, calibration tarps were used. The tarps have 8 reflectance sections and are manufactured to have gradually greater reflectivity from one section to the next. They were factory measured to have 2%, 4%, 8%, 16%, 32%, 48%, 64% and 88% reflectance. However, in practice they deviate slightly from these manufactured settings. In order to get a true measurement of reflectance across each of the tarp sections, radiometer readings were taken on the ground during the time of airborne data acquisition. Calibration tarps that were placed near soil moisture meter station 66 and can be seen in Figure 2. Figure 2: Calibration Tarps Deployed Near Soil Moisture Meter Station 66 Figure 3: Calibration Tarps and Soil Moisture Meter 66 in HSI Image Figure 4: Radiometer (left) and Calibrated Image Data (right) Averaged over Three Brightest Calibration Tarps The 3x3 window of pixels within each ROI were averaged to create one spectra for each of the soil moisture meters. The raw and calibrated HSI sensor spectra collected from soil moisture meter station 26 is shown in Figure 9. The soil moisture data for each soil moisture meter was provided by USDA/ARS. The data for March 6, 2007 was extracted from the larger data set. The soil moisture entry from the half hour recording time that most closely matched the time of the HSI sensor data collection was extracted to use in the analysis. The soil moisture values for the 2 inch, 8 inch and 12 inch probes were recorded. This information was merged with the bands of the recorded HSI data for the respective soil moisture meter stations and merged into a comma delimited text file to be used in statistical analysis. Analysis for both the raw and atmospherically calibrated data sets were performed. Figure 6: Raw (left) and Calibrated (right) Spectra from Station 26 Figure 5: SWIR Image of Area Surrounding Station 26 (left in red) and photo of the Soil Moisture Meter Station 26 ModelR2R2 Pr > F inch-2 = ( * b29) + ( * b30) + (1.344 * b32) + (4.001 * b33) + ( * b34) + ( * b36) + ( * b38) + ( * b39) + ( * b43) inch-8 = ( * b29) + ( * b30) + ( * b32) + ( * b33) + ( * b34) + ( * b36) + ( * b38) + ( * b39) * b43) inch-12 = ( * b29) + ( * b30) + ( * b32) + ( * b33) + ( * b34) + ( * b36) + ( * b38) + ( * b39) + ( * b43) BandWavelength (nm) Table 1. Model coefficients and statistical significance for calibrated data set Table 2. Wavelength for significant spectral bands for soil moisture