An Effective Similar-Pixel Reconstruction of the High-Frequency Cloud-Covered Areas of Southwest China

Yu, Wei; Tan, Junlei; Ma, Mingguo; Li, Xiaolu; She, Xiaojun; Song, Zengjing

doi:10.3390/rs11030336

Cited by 32 publications

(16 citation statements)

References 41 publications

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“…can be obtained by the FY-4A SSI product, and the RF approach can be applied to the entire hemisphere's LSTs with a longitude centered at 104°42′ E, theoretically. Compared with other methods [34,36], this reconstruction method is much more practical and independent, with the auxiliary data obtained from the FY-4A SSI product to describe the change of LST impacted by the cloud-covered conditions. To further analyze the validation effects at different sites, the accuracy of the reconstructed LSTs was evaluated separately with in situ measurements at each site.…”

Section: Discussionmentioning

confidence: 99%

“…Due to the limited availability of ground-based observations, there is a lack of abundant sites and measurements at a complete time-series for validation in the complex area. Moreover, because of the special spatial position in this region, the LST products reconstructed by this method are not compared with other reconstructed LST products [34,37]. Additionally, it is difficult to validate the LST products in such a heterogeneous region.…”

Section: Discussionmentioning

confidence: 99%

“…Based on the thermal radiation transfer function, the in situ LST measurements can be estimated by the following function [34]:…”

Section: Validation Datamentioning

confidence: 99%

See 2 more Smart Citations

Gap-Free LST Generation for MODIS/Terra LST Product Using a Random Forest-Based Reconstruction Method

Xiao

Zhao

et al. 2021

Remote Sensing

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Land surface temperature (LST) is a crucial input parameter in the study of land surface water and energy budgets at local and global scales. Because of cloud obstruction, there are many gaps in thermal infrared remote sensing LST products. To fill these gaps, an improved LST reconstruction method for cloud-covered pixels was proposed by building a linking model for the moderate resolution imaging spectroradiometer (MODIS) LST with other surface variables with a random forest regression method. The accumulated solar radiation from sunrise to satellite overpass collected from the surface solar irradiance product of the Feng Yun-4A geostationary satellite was used to represent the impact of cloud cover on LST. With the proposed method, time-series gap-free LST products were generated for Chongqing City as an example. The visual assessment indicated that the reconstructed gap-free LST images can sufficiently capture the LST spatial pattern associated with surface topography and land cover conditions. Additionally, the validation with in situ observations revealed that the reconstructed cloud-covered LSTs have similar performance as the LSTs on clear-sky days, with the correlation coefficients of 0.92 and 0.89, respectively. The unbiased root mean squared error was 2.63 K. In general, the validation work confirmed the good performance of this approach and its good potential for regional application.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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Gap-Free LST Generation for MODIS/Terra LST Product Using a Random Forest-Based Reconstruction Method

Xiao

Zhao

et al. 2021

Remote Sensing

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“…Due to temporal limitations of the NP method, Lu et al [28] proposed an enhanced solution based on geostationary satellite data from the MSG SEVIRI radiometer with shorter repeat cycle with respect to polar-orbiting satellites. Later, Yu et al [29] adapted the NP by exploiting the spatio-temporal domain offered by MODIS LST product. Because of the physical complexity of this method and scarcity of the station-based inputs, such as wind speed and latent heat flux, some new approaches were proposed.…”

Section: Introductionmentioning

confidence: 99%

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

Bartkowiak

Castelli

Crespi

et al. 2022

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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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.

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“…Considering the LST data over heterogeneous landscapes, some studies attempted to take more factors into an interpolation algorithm, e.g., a multi-dimensional spline interpolation was applied to reconstruct MODIS LST and produced a full-layer result [11], but its performance is limited to the small and complex topography. For the purposes of improving the reconstruction accuracy over topographically complex regions, an effective method based on the land energy balance theory was developed to reconstruct LSTs and generate an accurate reconstruction in the southwest region of China [12]. These spatial reconstruction methods are generally easy to implement and perform.…”

Section: Introductionmentioning

confidence: 99%

Filling Gaps of Monthly Terra/MODIS Daytime Land Surface Temperature Using Discrete Cosine Transform Method

Liu

Jiang

et al. 2020

Remote Sensing

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Land surface temperature (LST) is a key parameter in geophysical fields. The Moderate Resolution Imaging Spectroradiometer (MODIS) onboard Terra provides an accurate LST dataset with global coverage and monthly series, but the monthly MODIS LST data are often obscured by clouds and other atmospheric disturbances and consequently exhibit significant data gaps at a global scale, resulting in a difficult interpretation of LST trends and climatological characteristics. In this paper, an effective and fast LST reconstruction method to fill data gaps in monthly MODIS LST is presented. The proposal combines the Discrete Cosine Transform (DCT) and the Penalized Least Square approach (PLS) together with the Generalized Cross-Validation (GCV) criterion. It depends only on the spatial high-frequency information from original LST estimates and allows a fast and automatic filling process without the help of any other ancillary data. To analyze its performance, the method is applied to fill data gaps on three continents with synthetic random missing values introduced as validation sets. The statistical evaluation shows that this method is capable of filling a large number of missing values in MODIS LST datasets with very high accuracy. In addition, the trend differences between the original LST and reconstructed LST have assessed the significance by computing 95% confidence intervals for a time series of trend differences is examined. Simulated experiments show that data gaps with large missing counts lead to significant differences in trend patterns and the patterns on validation sets are well estimated by this method, which confirms that the filling process of MODIS LST is necessary and favorable results can be produced for substantial data gaps by the DCT-PLS method.

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An Effective Similar-Pixel Reconstruction of the High-Frequency Cloud-Covered Areas of Southwest China

Cited by 32 publications

References 41 publications

Gap-Free LST Generation for MODIS/Terra LST Product Using a Random Forest-Based Reconstruction Method

Gap-Free LST Generation for MODIS/Terra LST Product Using a Random Forest-Based Reconstruction Method

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

Filling Gaps of Monthly Terra/MODIS Daytime Land Surface Temperature Using Discrete Cosine Transform Method

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