scholarly journals Evaluation of Machine Learning Algorithms in Spatial Downscaling of MODIS Land Surface Temperature

2019 ◽  
Vol 12 (7) ◽  
pp. 2299-2307 ◽  
Author(s):  
Wan Li ◽  
Li Ni ◽  
Zhao-Liang Li ◽  
Si-Bo Duan ◽  
Hua Wu
Keyword(s):  
Machine Learning ◽  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Modis Land Surface Temperature ◽  
Spatial Downscaling

scholarly journals A Machine Learning Approach for Remote Sensing Data Gap-Filling with Open-Source Implementation: An Example Regarding Land Surface Temperature, Surface Albedo and NDVI

2020 ◽  
Vol 12 (23) ◽  
pp. 3865
Author(s):  
Mikhail Sarafanov ◽  
Eduard Kazakov ◽  
Nikolay O. Nikitin ◽  
Anna V. Kalyuzhnaya
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Time Series ◽  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Temporal Dynamics ◽  
Machine Learning Algorithms ◽  
Series Data ◽  
Terra Modis

Satellite remote sensing has now become a unique tool for continuous and predictable monitoring of geosystems at various scales, observing the dynamics of different geophysical parameters of the environment. One of the essential problems with most satellite environmental monitoring methods is their sensitivity to atmospheric conditions, in particular cloud cover, which leads to the loss of a significant part of data, especially at high latitudes, potentially reducing the quality of observation time series until it is useless. In this paper, we present a toolbox for filling gaps in remote sensing time-series data based on machine learning algorithms and spatio-temporal statistics. The first implemented procedure allows us to fill gaps based on spatial relationships between pixels, obtained from historical time-series. Then, the second procedure is dedicated to filling the remaining gaps based on the temporal dynamics of each pixel value. The algorithm was tested and verified on Sentinel-3 SLSTR and Terra MODIS land surface temperature data and under different geographical and seasonal conditions. As a result of validation, it was found that in most cases the error did not exceed 1 °C. The algorithm was also verified for gaps restoration in Terra MODIS derived normalized difference vegetation index and land surface broadband albedo datasets. The software implementation is Python-based and distributed under conditions of GNU GPL 3 license via public repository.

scholarly journals Spatial Downscaling of SMAP Soil Moisture Using MODIS Land Surface Temperature and NDVI During SMAPVEX15

2017 ◽  
Vol 14 (11) ◽  
pp. 2107-2111 ◽  
Author(s):  
Andreas Colliander ◽  
Joshua B. Fisher ◽  
Gregory Halverson ◽  
Olivier Merlin ◽  
Sidharth Misra ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Modis Land Surface Temperature ◽  
Spatial Downscaling
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