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

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.

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

“…This indicates an impact of clouds on amount of incident shortwave radiation, which regulates the land heating process. In this case findings from the present study are not in agreement with spatio-temporal gapfilling proposed by Weiss et al [44], Sun et al [109], Li et al [111] and Sarafanov et al [43] who predicted overcast surface temperatures from adjacent cloud-free pixels.…”

Tocap: a web tool for ad-hoc campaign planning in terrestrial hydrology

“…Advanced numerical simulations (de Graaf et al 2017;Zaherpour et al 2018) and satellite-based remote sensing (Butler 2014;Simmons et al 2016;Chen & Wang 2018) in conjunction with sophisticated algorithms such as machine learning tools (e.g. Mao et al 2019;Sarafanov et al 2020) can provide 4D environmental datasets with unprecedented resolution, coverage, and accuracy.…”

Gap-Filling of 8-Day Terra MODIS Daytime Land Surface Temperature in High-Latitude Cold Region with Generalized Additive Models (GAM)

“…In recent years, machine learning and deep learning techniques obtain a complex network by training a rich set of the known data to identify the relationships between the independent and dependent parameters. These algorithms reduce the inter-collinearity of adjacent pixels in the above-mentioned models in both spatial and temporal domains, and have great advantages in solving nonlinear problems [24][25][26][27][28]. However, the need of an intensive training dataset and high computational capacity may hinder their widespread application.…”