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

Тип документаJournal Article
Дата публикации2020-12-01
Название журналаRemote Sensing
ИздательMultidisciplinary Digital Publishing Institute (MDPI)
Квартиль по SCImagoQ1
Квартиль по Web of ScienceQ1
Импакт-фактор 20215.35
ISSN20724292
General Earth and Planetary Sciences
Краткое описание
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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ГОСТ |
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1. Sarafanov M. и др. 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. Т. 12. № 23. С. 3865.
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TY - JOUR

DO - 10.3390/rs12233865

UR - http://dx.doi.org/10.3390/rs12233865

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 - Sarafanov, Mikhail

AU - Kazakov, Eduard

AU - Nikitin, Nikolay O.

AU - Kalyuzhnaya, Anna V.

PY - 2020

DA - 2020/11/25

PB - MDPI AG

SP - 3865

IS - 23

VL - 12

SN - 2072-4292

ER -

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@article{Sarafanov_2020,

doi = {10.3390/rs12233865},

url = {https://doi.org/10.3390%2Frs12233865},

year = 2020,

month = {nov},

publisher = {{MDPI} {AG}},

volume = {12},

number = {23},

pages = {3865},

author = {Mikhail Sarafanov and Eduard Kazakov and Nikolay O. Nikitin and Anna V. Kalyuzhnaya},

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}

}

MLA
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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, p. 3865. Crossref, https://doi.org/10.3390/rs12233865.