A Spatio-temporal disaggregation method to derive time series of Normalized Difference Vegetation Index and Land Surface Temperature at fine spatial resolution

• Published in: International archives of the photogrammetry, remote sensing and spatial information sciences. Vol. XL-8; no. 8; pp. 1397 - 1401
• Main Authors: Bindhu, V. M., Narasimhan, B.
• Format: Journal Article Conference Proceeding
• Language: English
• Published: Gottingen Copernicus GmbH 01.01.2014
• ISSN: 2194-9034; 1682-1750; 2194-9034
• DOI: 10.5194/isprsarchives-XL-8-1397-2014

Estimation of evapotranspiration (ET) from remote sensing based energy balance models have evolved as a promising tool in the field of water resources management. Performance of energy balance models and reliability of ET estimates is decided by the availability of remote sensing data at high spatial and temporal resolutions. However huge tradeoff in the spatial and temporal resolution of satellite images act as major constraints in deriving ET at fine spatial and temporal resolution using remote sensing based energy balance models. Hence a need exists to derive finer resolution data from the available coarse resolution imagery, which could be applied to deliver ET estimates at scales to the range of individual fields. The current study employed a spatio-temporal disaggregation method to derive fine spatial resolution (60 m) images of NDVI by integrating the information in terms of crop phenology derived from time series of MODIS NDVI composites with fine resolution NDVI derived from a single AWiFS data acquired during the season. The disaggregated images of NDVI at fine resolution were used to disaggregate MODIS LST data at 960 m resolution to the scale of Landsat LST data at 60 m resolution. The robustness of the algorithm was verified by comparison of the disaggregated NDVI and LST with concurrent NDVI and LST images derived from Landsat ETM+. The results showed that disaggregated NDVI and LST images compared well with the concurrent NDVI and LST derived from ETM+ at fine resolution with a high Nash Sutcliffe Efficiency and low Root Mean Square Error. The proposed disaggregation method proves promising in generating time series of ET at fine resolution for effective water management.