Open Access
Open access
Remote Sensing, volume 12, issue 23, pages 1-21

A machine learning approach for remote sensing data gap-filling with open-source implementation: An example regarding land surface temperature, surface albedo and ndvi

Publication typeJournal Article
Publication date2020-11-25
Journal: Remote Sensing
Quartile SCImago
Q1
Quartile WOS
Q1
Impact factor5
ISSN20724292
General Earth and Planetary Sciences
Abstract

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.

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GOST |
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GOST Copy
Sarafanov M. et al. A machine learning approach for remote sensing data gap-filling with open-source implementation: An example regarding land surface temperature, surface albedo and ndvi // Remote Sensing. 2020. Vol. 12. No. 23. pp. 1-21.
GOST all authors (up to 50) Copy
Sarafanov M., Kazakov E., Nikitin N. O., Kalyuzhnaya A. V. A machine learning approach for remote sensing data gap-filling with open-source implementation: An example regarding land surface temperature, surface albedo and ndvi // Remote Sensing. 2020. Vol. 12. No. 23. pp. 1-21.
RIS |
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RIS Copy
TY - JOUR
DO - 10.3390/rs12233865
UR - https://doi.org/10.3390%2Frs12233865
TI - A machine learning approach for remote sensing data gap-filling with open-source implementation: An example regarding land surface temperature, surface albedo and ndvi
T2 - Remote Sensing
AU - Nikitin, Nikolay O
AU - Kazakov, Eduard
AU - Kalyuzhnaya, Anna V
AU - Sarafanov, Mikhail
PY - 2020
DA - 2020/11/25 00:00:00
PB - Multidisciplinary Digital Publishing Institute (MDPI)
SP - 1-21
IS - 23
VL - 12
SN - 2072-4292
ER -
BibTex |
Cite this
BibTex Copy
@article{2020_Sarafanov
author = {Nikolay O Nikitin and Eduard Kazakov and Anna V Kalyuzhnaya and Mikhail Sarafanov},
title = {A machine learning approach for remote sensing data gap-filling with open-source implementation: An example regarding land surface temperature, surface albedo and ndvi},
journal = {Remote Sensing},
year = {2020},
volume = {12},
publisher = {Multidisciplinary Digital Publishing Institute (MDPI)},
month = {nov},
url = {https://doi.org/10.3390%2Frs12233865},
number = {23},
pages = {1--21},
doi = {10.3390/rs12233865}
}
MLA
Cite this
MLA Copy
Sarafanov, Mikhail, et al. “A machine learning approach for remote sensing data gap-filling with open-source implementation: An example regarding land surface temperature, surface albedo and ndvi.” Remote Sensing, vol. 12, no. 23, Nov. 2020, pp. 1-21. https://doi.org/10.3390%2Frs12233865.
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