A wavelet-artificial intelligence fusion approach (WAIFA) for blending Landsat and MODIS surface temperature

Land surface temperature (LST) is an important parameter that is highly responsive to surface energy fluxes and has become valuable to many disciplines. However, it is difficult to acquire satellite LSTs with both high spatial and temporal resolutions due to tradeoffs between them. Thus, various algorithms/models have been developed to enhance the spatial or the temporal resolution of thermal infrared (TIR) data or LST, but rarely both. The Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) is the widely-used data fusion algorithm for Landsat and MODIS imagery to produce Landsat-like surface reflectance. In order to extend the STARFM application over heterogeneous areas, an enhanced STARFM (ESTARFM) approach was proposed by introducing a conversion coefficient and the spectral unmixing theory. The aim of this study is to conduct a comprehensive evaluation of the ESTARFM algorithm for generating ASTER-like daily LST by three approaches: simulated data, ground measurements and remote sensing products, respectively. The datasets of LST ground measurements, MODIS, and ASTER images were collected in an arid region of Northwest China during the first thematic HiWATER-Multi-Scale Observation Experiment on Evapotranspiration (MUSOEXE) over heterogeneous land surfaces in 2012 from May to September. Firstly, the results of the simulation test indicated that ESTARFM could accurately predict background with temperature variations, even coordinating with small ground objects and linear ground objects. Secondly, four temporal ASTER and MODIS data fusion LSTs (i.e., predicted ASTER-like LST products) were highly consistent with ASTER LST products. Here, the four correlation coefficients were greater than 0.92, root mean square error (RMSE) reached about 2 K and mean absolute error (MAE) ranged from 1.32 K to 1.73 K. Finally, the results of the ground measurement validation indicated that the overall accuracy was high (R 2 = 0.92, RMSE = 0.77 K), and the ESTARFM algorithm is a highly recommended method to assemble time series images at ASTER spatial resolution and MODIS temporal resolution due to LST estimation error less than 1 K. However, the ESTARFM method is also limited in predicting LST changes that have not been recorded in MODIS and/or ASTER pixels.

Section: Introductionmentioning

confidence: 99%

Evaluation of ASTER-Like Daily Land Surface Temperature by Fusing ASTER and MODIS Data during the HiWATER-MUSOEXE

Yang

Weng

et al. 2016

“…2018, 10, 105 9 of 17 the disaggregated image and the reference image are different. The ERGAS is defined in Equation (25) as follows: (25) where β is the scale ratio between the pixel sizes of the SWIR image and the BT image, RMSE i is the RMSE between the ith fused band and the reference band, and μ i is the mean of the ith reference band. Thus, a small value of ERGAS means a small spectral distortion is present in the disaggregated image.…”

Section: Image Quality Assessmentmentioning

confidence: 99%

“…Previous work has focused on choosing other predictors such as albedo [16], percent impervious surface area [17], temperature vegetation dryness index [18], and normalized difference built-up index [19]. More recently, complex non-linear statistical algorithms with additional predictor variables have been proposed to improve performance, including least median square (LMS) regression [12], artificial neural network [20][21][22], thin plate spline interpolation [23], co-kriging method [24], wavelet transformation [25], and random forest regression [26,27]. Also, some studies have focused on spatio-temporal disaggregation that conducts data fusion between thermal imagery with low spatial and high temporal resolution and that with high spatial and low temporal resolution [28][29][30][31].…”

Section: Introductionmentioning

confidence: 99%

Disaggregation of Landsat-8 Thermal Data Using Guided SWIR Imagery on the Scene of a Wildfire

Cho

Kim

2018

Thermal data products derived from remotely sensed data play significant roles as key parameters for biophysical phenomena. However, a trade-off between spatial and spectral resolutions has existed in thermal infrared (TIR) remote sensing systems, with the end product being the limited resolution of the TIR sensor. In order to treat this problem, various disaggregation methods of TIR data, based on the indices from visible and near-infrared (VNIR), have been developed to sharpen the coarser spatial resolution of TIR data. Although these methods were reported to exhibit sufficient performance in each study, preservation of thermal variation in the original TIR data is still difficult, especially in fire areas due to the distortion of the VNIR reflectance by the impact of smoke. To solve this issue, this study proposes an efficient and improved disaggregation algorithm of TIR imagery on wildfire areas using guided shortwave infrared (SWIR) band imagery via a guided image filter (GF). Radiometric characteristics of SWIR wavelengths could preserve spatially high frequency temperature components in flaming combustion, and the GF preserved thermal variation of the original TIR data in the disaggregated result. The proposed algorithm was evaluated using Landsat-8 operational land imager (OLI) and thermal infrared sensor (TIRS) images on wildfire areas, and compared with other algorithms based on a vegetation index (VI) originating from VNIR. In quantitative analysis, the proposed disaggregation method yielded the best values of root mean square error (RMSE), mean absolute error (MAE), correlation coefficient (CC), erreur relative globale adimensionelle de synthèse (ERGAS), and universal image quality index (UIQI). Furthermore, unlike in other methods, the disaggregated temperature map in the proposed method reflected the thermal variation of wildfire in visual analysis. The experimental results showed that the proposed algorithm was successfully applied to the TIR data, especially to wildfire areas in terms of quantitative and visual assessments.

“…These methods can be categorized as weighted function methods, unmixing methods and dictionary-pair learning methods. Other relatively uncommon methods are also used and are based on Bayesian [9,10], neural network [11,12], pyramid [13] and semi-physical model [14] methods. Among the weighted function-based methods, the spatial and temporal adaptive reflectance fusion model (STARFM) method [15] was developed in 2006 to predict the daily Landsat surface reflectance by blending MODIS and Landsat surface reflectance data.…”

Section: Introductionmentioning

confidence: 99%

A Rigorously-Weighted Spatiotemporal Fusion Model with Uncertainty Analysis

Wang

Huang

2017

Abstract:Interest has been growing with regard to the use of remote sensing data characterized by a fine spatial resolution and frequent coverage for the monitoring of land surface dynamics. However, current satellite sensors are fundamentally limited by a trade-off between their spatial and temporal resolutions. Spatiotemporal fusion thus provides a feasible solution to overcome this limitation, and many blending algorithms have been developed. Among them, the popular spatial and temporal adaptive reflectance fusion model (STARFM) is based on a weighted function; however, it uses an ad hoc approach to estimate the weights of surrounding similar pixels. Additionally, an uncertainty analysis of the predicted result is not provided in the STARFM or any other fusion algorithm. This paper proposes a rigorously-weighted spatiotemporal fusion model (RWSTFM) based on geostatistics to blend the surface reflectances of Moderate Resolution Imaging Spectroradiometer (MODIS) and Landsat-5 Thematic Mapper (TM) imagery. The RWSTFM, which is based on ordinary kriging, derives the weights in terms of a fitted semivariance-distance relationship and calculates the estimation variance, which is a measure of the prediction uncertainty. The RWSTFM was tested using three datasets and compared with two commonly-used spatiotemporal reflectance fusion algorithms: the STARFM and the flexible spatiotemporal data fusion (FSDAF) method. The fusion results show that the proposed RWSTFM consistently outperformed the other algorithms both visually and quantitatively. Additionally, more than 70% of the squared error was accounted for by the estimation variance of the RWSTFM for all three of the datasets.