Sub-pixel water temperature estimation from thermal-infrared imagery using vectorized lake features

Water skin temperature derived from thermal infrared satellite data are used in a wide variety of studies. Many of these studies would benefit from frequent, high spatial resolution (100 m pixels) thermal imagery but currently, at any given location, such data are only available every few weeks from spaceborne sensors such as ASTER. Lower spatial resolution (1 km pixels) thermal imagery is available multiple times per day at any given location, from several sensors such as MODIS on board both the AQUA and TERRA satellite platforms. In order to fully exploit lower spatial resolution imagery, a sub-pixel unmixing technique has been developed and tested at Quesnel Lake, British Columbia, Canada. This approach produces accurate, frequent high spatial resolution water skin temperature maps by exploiting a priori knowledge of water boundaries derived from vectorized water features. The pixel water-fraction maps are then input to a gradient descent algorithm to solve the mixed pixel ground leaving radiance equation for sub-pixel water temperature. Ground-leaving radiance is estimated from standard temperature and emissivity data products for pure pixels and a simple regression technique to estimate atmospheric effects. In this test case, MODIS 1 km thermal imagery was used along with 1:50,000 water features to create a high-resolution (100 m) water skin temperature map. This map is compared to a concurrent ASTER temperature image and found to be within 1 degrees C of the ASTER skin temperature 99% of the time. This is a considerable improvement over the 2.55 degrees C difference between the original MODIS product and ASTER image due to land temperature contamination. The algorithm is simple, effective, and unlocks a largely untapped resource for limnological and hydrological studies. (c) 2007 Elsevier Inc. All rights reserved.