2250 IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, VOL. 51, NO. 4, APRIL 2013 Fusion of MODIS Images Using Kriging With External Drift Marcio H. Ribeiro Sales, Carlos M. Souza, Jr., and Phaedon C. Kyriakidis Abstract—The Moderate Resolution Imaging Spectroradiome- ter (MODIS) has been used in several remote sensing studies, including land, ocean, and atmospheric applications. The ad- vantages of this sensor are its high spectral resolution, with 36 spectral bands; its high revisiting frequency; and its public domain availability. The first seven bands of MODIS are in the visible, near-infrared, and mid-infrared spectral regions of the electro- magnetic spectrum which are sensitive to spectral changes due to deforestation, burned areas, and vegetation regrowth, among other land-use changes, making near-real-time forest monitoring a suitable application. However, the different spatial resolution of the spectral bands placed in these spectral regions imposes challenges to combine them in forest monitoring applications. In this paper, we present an algorithm based on geostatistics to downscale five 500-m MODIS pixel bands to match two 250-m pixel bands. We also discuss the advantages and limitations of this method in relation to existing downscaling algorithms. Our proposed method merges the data to the best spatial resolution and better retains the spectral information of the original data. Index Terms—Forest monitoring, image fusion, kriging with an external drift, Moderate Resolution Imaging Spectroradiometer (MODIS), spatial resolution. I. I NTRODUCTION T HE Moderate Resolution Imaging Spectroradiometer (MODIS) measures reflected and emitted energy at 36 bands placed at a wavelength range of 0.405–14.385 μm, with spatial resolutions of 250–1000 m [1], acquired at a near daily basis. The spectral, spatial, and temporal resolutions of MODIS render it a powerful tool for global and large-scale applications of remote sensing. The first seven bands of MODIS cover the wavelength range most related to features of land, cloud, and aerosol properties [2], for example, chlorophyll absorption at Manuscript received December 21, 2011; revised May 18, 2012; accepted May 26, 2012. Date of publication September 6, 2012; date of current version March 21, 2013. This work was supported in part by The David and Lucile Packard Foundation and in part by the Climate Land Use Alliance. The work of M. H. Ribeiro Sales and P. C. Kyriakidis was supported in part by the U.S. National Geospatial Intelligence Agency. M. H. Ribeiro Sales is with the Instituto do Homem e Meio Ambiente da Amazônia—Imazon, Belém 66613-397, Brazil, and also with the Pro- duction Ecology and Resource Conservation Graduate School, Wageningen University, 6709 PB Wageningen, The Netherlands (e-mail: marcio@imazon. org.br). C. M. Souza, Jr., is with the Instituto do Homem e Meio Ambiente da Amazônia—Imazon, Belém 66613-397, Brazil (e-mail: souzajr@imazon. org.br). P. C. Kyriakidis is with the Department of Geography, University of Califor- nia at Santa Barbara, Santa Barbara, CA 93106-4060 USA, and also with the Department of Geography, University of the Aegean, 81100 Mytilene, Greece (e-mail: pckyriakidis@gmail.com). Color versions of one or more of the figures in this paper are available online at http://ieeexplore.ieee.org. Digital Object Identifier 10.1109/TGRS.2012.2208467 red band (i.e., band 1, 0.620–0.670 μm) and canopy backscat- tering at near infrared (band 2, 0.841–0.876 μm). These bands can also be combined to retrieve biophysical properties of forests using products such as vegetation indices available at the Land Process Distributed Active Archive Center (LP DAAC) Web site [3]. These features make the first seven MODIS bands the most important ones for land-use and land-cover change (LULCC) applications [1]. In Brazil, MODIS data have been used to detect deforestation at near real time for forest law enforcement and environmental policy verification [4], [5]. However, a limitation is imposed by the difference in the spatial resolutions of these bands: Bands 1 and 2 are the only ones available at the 250-m spatial resolution [6] while bands 3–7 are in the 500-m spatial resolution. This difference in spatial resolutions limits the generation of products based on MODIS data at the 250-m resolution using the full range of spectral information important for forest monitoring. The 250-m resolution bands 1 and 2 can, however, be spatially degraded to 500 m to create a product at this resolution with seven spectral bands, but the 250-m resolution is preferred because many changes in the land cover occur at this spatial scale [7]. Splitting a 500-m pixel into four 250-m pixels would result in a product with seven spectral bands with no necessarily direct spectral correspondence from the two spatial resolutions. Spectral mixing, for example, is a scale-dependent [8] phe- nomenon that could drastically affect this type of data-fused product. Alternatively, bands 1 and 2 alone have been used for land-cover mapping applications [9], [10]. However, the addition of information on the infrared bands can significantly improve the detection and analysis of LULCC [9]. Combining data from different spatial resolutions such as 250- and 500-m MODIS bands is a typical image fusion problem. The general objective of image fusion is to combine two (or more) images in order to obtain a better image, retaining the desired characteristic of each of the original images [11]. Image downscaling is a special case of image fusion, in which fine spatial coarser spectral resolution images are combined with coarser spatial higher spectral resolution images to yield a spectrally enhanced product at the finer spatial resolution [12]. This type of approach is more appropriate than pixel splitting the 500-m MODIS images because it retains the spatial infor- mation at the finer resolution and the spectral correspondence of the two spatial-resolution bands is guaranteed. Image downscaling has the potential to greatly improve the utility of MODIS data for several remote sensing applications. For ex- ample, improvements in snow cover mapping have been found after the application of a downscaling algorithm to MODIS images [13]. 0196-2892/$31.00 © 2012 IEEE