Location: Soil and Water Management ResearchTitle: A review of downscaling methods for remote sensing-based irrigation management: Part I) Author
Submitted to: Irrigation Science
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 3/9/2012
Publication Date: 6/6/2013
Citation: Ha, W., Gowda, P., Howell, T.A. 2013. A review of downscaling methods for remote sensing-based irrigation management: Part I. Irrigation Science. 31(4):831-850. Interpretive Summary: Estimating an accurate evapotranspiration (ET) image has been considered important in agricultural research for water management to improve the estimation of crop water requirements and to promote more precise irrigation scheduling. Evapotranspiration data from remote sensing satellite sensors provide a wide coverage of data to estimate crop water use from field to regional scale. However, retrieving finer spatial resolution data has often been difficult due to the design limits of satellite sensors. In order to obtain finer spatial resolution satellite data using coarser spatial resolution data from satellite sensors, image sharpening methods have been considered. Based on a literature review, there are two broad categories of image sharpening methods which are downscaling and image fusion. The objective of this paper is to provide a comprehensive review of existing downscaling methods.
Technical Abstract: High resolution daily evapotranspiration (ET) maps would greatly improve irrigation management. Numerous ET mapping algorithms have been developed to make use of thermal remote sensing data acquired by satellite sensors. However, adoption of remote sensing-based ET maps for irrigation management has not been feasible due to inadequate spatial and temporal resolution of ET maps. This is either due to sparse overpass frequency of high spatial resolution sensors such as Landsat Thematic Mapper (TM) or design limits of high temporal resolution satellite sensors such as MODerate resolution Imaging Spectroradiometer (MODIS). Data from a coarse spatial resolution image in agricultural fields often cause inaccuracy in estimation of ET because of a high level of spatial heterogeneity in land use. Image downscaling methods have been utilized to overcome spatial and temporal scaling issues in numerous remote sensing applications. In the field of hydrology, the image sharpening method has been used to improve spatial resolution of remote sensing-based ET maps for irrigation scheduling purposes and thus improves estimation of crop water requirements. Based on input requirements, methodologies, and purpose of applications, we have classified downscaling methods into two broad categories; (1) scale-based traditional downscaling and (2) image fusion. This paper (Part I) reviews downscaling methods to improve spatial resolution of land surface characteristics such as land surface temperature (LST) or ET. Each downscaling method was assessed and compared with respect to their capability of downscaling spatial resolutions of images. Scaling effects for a general circulation model (GCM) were added to represent the application of downscaling concept in climate modeling. Scaling of geophysical parameters was also addressed to introduce the concept of scaling in the field of soil physical property-related remote sensing. The companion paper (Part II) presents a review of image fusion methods which are also designed to increase spatial resolutions of images by integrating multi-spectral and panchromatic images.