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High spatial resolution thermal infrared imagery is needed in routine environmental assessment in both urban and suburban areas. Due to the tradeoff between temporal and spatial resolution in currently available thermal infrared data, thermal sharpening technique is desirable. The Needs for Thermal Infrared Data Land Surface Temperature (LST) and Vegetation Abundance This project compared the prediction of LST using NDVI and fractional vegetation cover as the indicators in urban and suburban areas in Indianapolis, IN. Objective Step 2: Calculate the residual at coarser resolution Table 2. Mean and standard deviation of difference between observed LST and estimated LST from NDVI and vegetation fraction. Step 3: perform the regression between LST and vegetation cover at finer resolution, and the residual were added back. Table 3. RMSE between observed LST and estimated LST from NDVI and vegetation fraction. RMSE was computed Where is modeled, and is observed. Results Conclusions and Discussions This study uses NDVI and fractional vegetation cover as indicators to estimate the land surface temperature. Vegetation fraction method did reduce RMSE in the urban area in both linear (from 0.937 to 0.880) and polynomial (from 1.960 to 1.152) regression, which proved that the subpixel technique is more suitable for observing and extracting urban surface features. Vegetation fraction application in the suburban area did not improve much the result over the NDVI method. Considering the size of individual agricultural fields in this region, 30m resolution is good enough for LST estimation in the suburban area. Generally, linear regression worked better than polynomial regression, for it is less sensitive to outliers. Future Work We Assume that vegetation cover amount is the primary driver of temperature variation and heat exchange. Both Normalized Difference Vegetation Index (NDVI) and fractional vegetation cover can be used as indicators for estimating LST. NDVI could provides general vegetation growing condition. It can be calculated from red and NIR band with higher spatial resolution than thermal infrared band. Considering the characteristics of urban surface feature, sub-pixel techniques, may further improve the accuracy of the temperature estimation in urban areas. Center for Urban and Environmental Change and Department of Earth and Environmental Systems Indiana State University Yitong Jiang and Qihao Weng Estimating Land Surface Temperature Using a Thermal Sharpening Technique Table 1. Resolutions of thermal bands (Agam et al., 2007) SensorSpatial resolutionTemporal resolution TM120m16 days ETM+60m16 days ASTER90mon demand MODIS1,000m1-2 per day AVHRR1,000m2 per day GOES5,000m15 minutes Method Data and Study Area Kustas (2003) three-step LST estimation method was adopted. Step 1: (1) Step 2: (2) Step 3: (3) Where means vegetation cover. Both NDVI and fractional vegetation cover were tested. In equation (1), both linear and polynomial regression were tested. In equation (3), means the estimated temperature of each th 30m pixel in 120m pixel. The sample points for generating both LST-NDVI regression and LST- vegetation fraction regression are the same, including impervious surface, urban and suburb residential, agricultural fields with or without vegetation, and forestry. Interstate Highway 465 were used to divide urban and suburban areas. Urban and suburban areas were processed separately. Due to the assumption that vegetation cover is the primary driver of temperature variations, water bodies were masked out. Landsat TM image (path 21, row 32) that was taken on July 6th, 2002. IndicatorRegressionAreaMeanStd.Dev f(NDVI) Linear urban-0.1121.146 suburban-0.6112.143 polynomial urban-0.0971.775 suburban-0.4772.064 f(fraction) Linear urban-0.0961.166 suburban-0.4992.046 polynomial urban0.0311.270 suburban-0.5092.064 IndicatorRegressionAreaRMSE (°C) f(NDVI) Linear urban0.937 suburban1.468 polynomial urban1.960 suburban1.380 f(fraction) Linear urban0.880 suburban1.441 polynomial urban1.152 suburban1.459 The accuracy of the fraction image needs to be improved. Further investigation under different vegetation and climate conditions is needed. More resolution variation is expected to be tested. Step 1: perform the regression between LST and vegetation cover at coarser resolution (1) Regression LST vs. NDVI – Urban Linear: y = -14.565x + 34.419 (R² = 0.56) Polynomial: y = -30.9x 2 + 3.6116x + 32.557 (R² = 0.68) – Suburb Linear: y = -19.661x + 37.071 (R² = 0.75) Polynomial: y = -28.836x 2 + 5.3342x + 32.22 (R² = 0.81) (2) Regression LST vs. vegetation fraction – Urban Linear: y = -12.079x + 33.937 (R² = 0.51) Polynomial: y = -19.003x 2 + 0.7568x + 32.363(R² = 0.59) – Suburb y = -11.495x + 34.048 (R² = 0.70) y = -10.219x 2 - 2.3244x + 32.57(R² = 0.77)