Abstract
In this article, 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 cloud-covered grids from 1-km Terra MODIS LST at 250 m spatial resolution. First, we explored correlations between ground-measured LST and air temperature in conjunction with other geo-biophysical 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 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> of 0.84 and root-mean-square error 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
Highlights
EARTH’S skin temperature is a fundamental property regulating the exchange of water and energy between land and the atmosphere
LSTmean over grasslands tends to be higher than TAmean and the difference grows with temperature, especially at higher altitudes (Fig. 4e-f)
The main objective of this work was to develop a robust procedure for restoring invalid coarseresolution MODIS land surface temperature (LST) at 250 m spatial resolution by combining data-driven modelling from meteorological stations with physical-based approach to retrieve variables under longterm cloudy-sky conditions
Summary
EARTH’S skin temperature is a fundamental property regulating the exchange of water and energy between land and the atmosphere. It influences water and surface energy budget that is needed to estimate the impacts of climate change on water cycling, landcover, and to examine water anomalies in vegetation through evapotranspiration modelling [1]-[3]. It allows monitoring vegetation conditions and studying climate change and impacts of extreme events on vegetation. MODIS LST product has been applied in multiple research fields, including urban heat island assessment [12]-[15], drought detection [16][19], agricultural management [7],[20]-[21], and energy and water balance modelling [22]-[25]
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