Evaluation of Disaggregation Methods for Downscaling MODIS Land Surface Temperature to Landsat Spatial Resolution in Barrax Test Site

Abstract

Thermal infrared (TIR) data are usually acquired at 1 a coarser spatial resolution (CR) than visible and near infrared 2 (VNIR). Several disaggregation methods have been recently devel-3 oped to enhance the TIR spatial resolution using VNIR data. These 4 approaches are based on the retrieval of a relation between TIR 5 and VNIR data at CR, or training of a neural network, to be 6 applied at the fine resolution afterward. In this work, different 7 disaggregation methods are applied to the combination of two 8 different sensors in the experimental test site of Barrax, Spain. 9 The main objective is to test the feasibility of these techniques 10 when applied to satellites provided with no TIR bands. Landsat 11 and moderate imaging spectroradiometer (MODIS) images were 12 used for this work. Land surface temperature (LST) from MODIS 13 images was disaggregated to the Landsat spatial resolution using 14 Landsat VNIR data. Landsat LST was used for the validation 15 and comparison of the different techniques. Best results were 16 obtained by the method based on a linear regression between 17 normalized difference vegetation index (NDVI) and LST. An aver-18 age RMSE = ±1.9 K was observed between disaggregated and 19 Landsat LST from four different dates in a study area of 120 km 2. 20 Index Terms-Image enhancement, image resolution, remote 21 sensing, temperature. 22 I. INTRODUCTION 23 T IME series of fine spatial and temporal resolution images 24 are key inputs in numerous studies, e.g., water resources 25 management [1], [2]. However, there is a limitation in the exist-26 ing satellites since revisit time for fine spatial resolution sensors 27 is typically poor, while those with a high revisit frequency are 28 characterized by a coarse spatial resolution. This is especially 29 true when focusing on the thermal infrared (TIR) since spa-30 tial resolution for the TIR bands is always coarser than that for 31 the visible and near infrared (VNIR) bands onboard the same 32 sensor [2]. 33 Disaggregation methods allow downscaling the TIR coarse 34 resolution (CR) to finer resolutions. In [3], a review of land 35 Manuscript

Improving the Downscaling of Diurnal Land Surface Temperatures Using the Annual Cycle Parameters as Disaggregation Kernels

“…The inconsistency in the performance of LST predictors, both in respect to time and location, is another important issue in the downscaling literature [41,57]. This is because it complicates or even prohibits the transfer of a downscaling scheme designed for a specific area to another area with different landscape and climatic characteristics [15,57].…”

“…The third operation of the employed method is the application of the retrieved regression model to the fine scale LST predictors to generate the DLST image data. The third operation is coupled with a DLST adjustment process (i.e., a residual correction) as in TsHARP [10] and also in [9,20,41]. This process aims to compensate the loss of variability due to the inflexibility of the linear regression tool and it is based on the difference of the observed and modeled coarse-scale LST data (Figure 3).…”

“…Prior to the application of Equation (4), the ΔLST are resampled to the 1 × 1 km DLST grid. A smoothing filter is also applied to the resampled ΔLST to prevent the occurrence of boxy effects on the DLST data, as suggested in [41]. The DLSTadj are the primary output of the employed downscaling method and the main input to Equation (1).…”

“…The selection of the LST predictors is based on the findings of [7,[19][20][21]40,41]. The SRTM DEM is a near-global, high quality DEM available free-of-charge at a spatial resolution of 30 and 90 m. The absolute geolocation and height errors of SRTM DEM for the Eurasia continental landmass are 8.8 and 6.2 m, respectively [42].…”

“…Hence, to make this study insusceptible to short-term weather effects, summer 2014 (DOYs: 152-243) was selected as the study period. The selection of this study period is based on the following four reasons: (i) it can provide a representative dataset for the purpose of this study; (ii) using LST data from the same year will ensure that the impact of phenological changes on the LST patterns will be reduced; (iii) the negative correlation of LST and NDVI, which is vital for the successful downscaling of LST Scheme 1, which is the control of this study's experiment, is in essence a modified version of the TsHARP (thermal sharpening) algorithm [10] that is widely used as a reference in the downscaling literature [20,22,41,45]. This LST downscaling algorithm exploits the strong correlation of NDVI with LST and uses linear regression to model it.…”