Land Surface Temperature Reconstruction Under Long-Term Cloudy-Sky Conditions at 250 m Spatial Resolution: Case Study of Vinschgau/Venosta Valley in the European Alps

Abstract

In this paper, we present a new concept for predicting satellite-derived land surface temperature (LST) under cloudy skies over vegetated areas in the Alps. Although many different reconstruction methods have been developed, they require rarely available inputs, or they restore missing pixels from clear-sky observations with low spatial resolution (1-5 km), which makes them unreliable in heterogenous ecosystems. Given these limitations, we propose a station-based procedure to predict cloudcovered grids from 1-km Terra MODIS LST at 250 m spatial resolution. First, we explored correlations between groundmeasured LST and air temperature in conjunction with other geobiophysical variables under cloudy-sky conditions derived from ESRA clear-sky radiation model. Considering a high site dependency driven by different landcovers, in-situ data were aggregated into three groups (forest, permanent crops, grassland) and then, models were established. Next, the regressions were applied to 250-m gridded predictors to estimate cloud-covered LST pixels for six Terra MODIS LST images in 2014. While for permanent crops and forest group linear modelling was the most efficient, neural networks achieved the best performance for grasslands. The reconstructions showed reasonable LST distribution considering landscape heterogeneity of the region. The results were validated against timeseries of ground-measured LST in 2014. The models achieved reliable performance with an average R 2 of 0.84 and RMSE of 2.12°C. Despite some limitations, mainly due to diversified character of cloudy-sky conditions and high heterogeneity of gridded predictors, the method can effectively reconstruct overcast MODIS data at subpixel level, which shows great potential for producing cloud-free LSTs in complex ecosystems.

Two-source energy balance modeling of evapotranspiration with thermal remote sensing at different spatial resolutions: a case study of the European Alps

“…where LSTFR denotes final downscaled LST data with residual correction applied, LSTfR is the indirect LST output after applying coarse resolution (CR) thermal model to the FR explanatory variables, and the term of (LSTCR -LSTfR CR ) FR indicates residual correction between coarse resolution surface temperatures and their corresponding upscaled fine resolution predictions resampled to spatial resolution of the kernels applied. As shown in previous studies (Bartkowiak et al, 2022;Bertoldi et al, 2010;He et al, 2019), driving forces of LST distribution in mountain regions may be related to many factors, including topography, vegetation content and climate forcing. To this end, selection of representative 250-m kernels was based on "the process-guided design" as an attempt to better explain LST variability in complex ecosystems (Mao et al, 2021).…”

“…Additionally, the sharpening models were forced with 250-m incoming solar radiation granules that were obtained by means of geostatistical downscaling applied to daily DSSF product acquired by MSG/SEVIRI instrument (https://landsaf.ipma.pt). On average, root mean square error (RMSE) was equal to 2.64 MJ m -2 day -1 when compared to in-situ measurements over the Alpine region (Bartkowiak et al, 2022).…”

Evaluating the Reconstructed All-Weather Land Surface Temperature for Urban Heat Island Analysis

“…The reconstructed LST in this paper exhibited a high degree of accuracy, with an average RMSE of 2.20 K, average MAE of 1.51 K, and ρ greater than 0.9. Reconstructing the all-weather LST has consistently remained a topic of interest [5,48], and several studies have been conducted. For instance, Zhou, et al [49] proposed the data interpolating empirical orthogonal functions (DINEOF) method for LST reconstruction, with an RMSE of 4.57 K. Duan, Li and Leng [7] fused TIR and passive microwave data to reconstruct the LST, with an RMSE ranging from 3.50 to 4.40 K. Li, et al [50] introduced a three-step mixed gap-filling method, with an RMSE of 4.78 K. Long, Yan, Bai, Zhang and Shi [9] utilized a data fusion approach combined with bias correction to reconstruct the LST, with an RMSE of 2.72 to 4.00 K. Compared with previous results [41], the reconstructed LST in this study exhibits a higher precision and hence can provide a valuable reconstruction approach for future research and serve as a fundamental data source for studies related to the thermal environment.…”

“…The conventional method to obtain the LST involves meteorological station and remote sensing (RS) observation data. However, the meteorological station distribution is relatively sparse, and the LST can exhibit significant variations within short distances [5]. Obtaining the LST through RS observation techniques offers high spatial coverage and easy accessibility, demonstrating a tremendous potential [6].…”

Harnessing deep learning to forecast local microclimate using global climate data

“…Neural networks, in particular, present a promising solution for obtaining a comprehensive description of temperature and humidity variations. By integrating localized sensor data with global climate information, neural networks can effectively capture the intricate relationship between these variables 22 .…”