Improving LST Downscaling Quality on Regional and Field-Scale by Parameterizing the DisTrad Method

Abstract

Many scientists have been investigating Land Surface Temperature (LST) because of its relevance in water management science due to its direct influence on the hydrological water cycle. This effect stems from being one of the most significant variables influencing evapotranspiration. One of the most important reasons for the evapotranspiration retrieved from MODIS data’s limited suitability for scheduling and planning irrigation schemes is the lack of spatial resolution. As a result, high-resolution LST is required for estimating evapotranspiration. The goal of this study is to improve the resolution of the available LST data, to improve evapotranspiration (ETa) estimation using statistical downscaling with Normalized Difference Vegetation Index (NDVI) as a predictor. The DisTrad (Disaggregation of Radiometric Surface Temperature) method was used for the LST downscaling procedure, which is based on aggregating the NDVI map to the LST map resolution and then calculating the coefficient of variation of the native NDVI map within the aggregated pixel and classifying the aggregated map into three classes: NDVI < 0.2 for the bare soil, 0.2 ≤ NDVI ≤ 0.5 for the partial vegetation, and NDVI > 0.5 for the full vegetation. DisTrad uses 25% of the pixels with the lowest coefficient of variation from each class to calculate the regression coefficients. In this work, adjustments to the DisTrad method were implemented to enhance downscaling LST and to examine the impacts of that alteration on the evapotranspiration estimation. The linear regression model was tested as an alternative to the original second-order polynomial. In using 10% of the pixels instead of the originally proposed 25% with the lowest coefficient of variation values, it is assumed that a group of pixels with a lower coefficient of variation represents a more homogeneous area, thus it gives more accurate values. The downscaled LST map retrieval was validated using Landsat 8 thermal maps (100 m). Applying the modified DisTrad approach to disaggregate Landsat LST to 30 m (NDVI resolution) yielded an R2 of 0.72 for the 10%, 0.74 for the 25% and 0.61 for the second-order polynomial lowest coefficient of variation compared to native LST Landsat, which means that 10% can be used as an alternative. Applying the downscaled LST map to estimate ETa yielded R2 0.84 in both cases, compared to ETa yielded from the native Landsat LST. These results prove that using the robust linear regression provided better results than using polynomial regression. With the downscaled Land Surface Temperature data, it was possible to create detailed ETa maps of the small agricultural fields in the test area.

A New Spatial Downscaling Method for Long-Term AVHRR NDVI by Multiscale Residual Convolutional Neural Network

“…The exploration and extraction of nonlinear attributes have seen significant progress, with researchers developing a variety of feature combinations. A landmark development in this area is the downscaling Convolutional Neural Network (CNN) which employs bicubic interpolation alongside a three-layer convolutional network to create high-resolution images, demonstrating the robust capabilities of deep learning in the realm of image downscaling reconstruction [27,28]. This method, however, extracts only a limited set of features by relying on information from a singular image.…”

Introducing Variations of Predictors as Optional Predictors Offers the Potential to Improve the Downscaling Performance of Geographically Weighted Regression Model

“…Therefore, satellite products with a high temporal resolution are often required to be spatially downscaled (disaggregation of a coarse cell into many finer cells) for various environmental applications. In the past 10 years, numerous spatial downscaling studies have been conducted on many satellite-derived products, such as precipitation [4], [5], [6], [7], [8], [9], [10], [11], [12], [13], [14], soil moisture [15], [16], [17], [18], [19], [20], [21], [22], [23], land surface temperature [2], [24], [25], [26], [27], [28], [29], [30], night-time light [31], solar radiation [32], evapotranspiration [33], [34], [35], chlorophyll [36], and wind speed [37]. The primary goal of spatial downscaling research is to improve the downscaling performance of satellite-derived products which is generally performed from two main aspects [38]: the introduction of new auxiliary variables [5], [8], [39], [40] and the development of new downscaling models [6], [13], [22],…”