Prediction of Land Surface Temperature under Cloudy Conditions using Microwave Remote Sensing and ANN

Shwetha, HR and Kumar, Nagesh D (2015) Prediction of Land Surface Temperature under Cloudy Conditions using Microwave Remote Sensing and ANN. In: INTERNATIONAL CONFERENCE ON WATER RESOURCES, COASTAL AND OCEAN ENGINEERING (ICWRCOE'15), MAR 11-14, 2015, Natl Inst Technol Karnataka, Mangaluru, INDIA, pp. 1381-1388.

PDF Aqu_Pro_4-1381-2015.pdf - Published Version Restricted to Registered users only Download (977kB) | Request a copy

Abstract

Land surface temperature (LST) is an important variable in climate, hydrologic, ecological, biophysical and biochemical studies (Mildrexler et al., 2011). The most effective way to obtain LST measurements is through satellites. Presently, LST from moderate resolution imaging spectroradiometer (MODIS) sensor is applied in various fields due to its high spatial and temporal availability over the globe, but quite difficult to provide observations in cloudy conditions. This study evolves of prediction of LST under clear and cloudy conditions using microwave vegetation indices (MVIs), elevation, latitude, longitude and Julian day as inputs employing an artificial neural network (ANN) model. MVIs can be obtained even under cloudy condition, since microwave radiation has an ability to penetrate through clouds. In this study LST and MVIs data of the year 2010 for the Cauvery basin on a daily basis were obtained from MODIS and advanced microwave scanning radiometer (AMSR-E) sensors of aqua satellite respectively. Separate ANN models were trained and tested for the grid cells for which both LST and MVI were available. The performance of the models was evaluated based on standard evaluation measures. The best performing model was used to predict LST where MVIs were available. Results revealed that predictions of LST using ANN are in good agreement with the observed values. The ANN approach presented in this study promises to be useful for predicting LST using satellite observations even in cloudy conditions. (C) 2015 The Authors. Published by Elsevier B.V.

Item Type:	Conference Paper
Series.:	Aquatic Procedia
Publisher:	ELSEVIER SCIENCE BV
Additional Information:	Copy right for this article belongs to the ELSEVIER SCIENCE BV, SARA BURGERHARTSTRAAT 25, PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS
Keywords:	LST; MVI; MODIS; AMSRE; ANN
Department/Centre:	Division of Mechanical Sciences > Civil Engineering
Date Deposited:	15 Jun 2015 09:27
Last Modified:	15 Jun 2015 09:27
URI:	http://eprints.iisc.ac.in/id/eprint/51667

Actions (login required)

View Item