Improving the Quality of Satellite Imagery Based on Ground-Truth Data from Rain Gauge Stations

Abstract

Multitemporal imagery is by and large geometrically and radiometrically accurate, but the residual noise arising from removal clouds and other atmospheric and electronic effects can produce outliers that must be mitigated to properly exploit the remote sensing information. In this study, we show how ground-truth data from rain gauge stations can improve the quality of satellite imagery. To this end, a simulation study is conducted wherein different sizes of outlier outbreaks are spread and randomly introduced in the normalized difference vegetation index (NDVI) and the day and night land surface temperature (LST) of composite images from Navarre (Spain) between 2011 and 2015. To remove outliers, a new method called thin-plate splines with covariates (TpsWc) is proposed. This method consists of smoothing the median anomalies with a thin-plate spline model, whereby transformed ground-truth data are the external covariates of the model. The performance of the proposed method is measured with the square root of the mean square error (RMSE), calculated as the root of the pixel-by-pixel mean square differences between the original data and the predicted data with the TpsWc model and with a state-space model with and without covariates. The study shows that the use of ground-truth data reduces the RMSE in both the TpsWc model and the state-space model used for comparison purposes. The new method successfully removes the abnormal data while preserving the phenology of the raw data. The RMSE reduction percentage varies according to the derived variables (NDVI or LST), but reductions of up to 20% are achieved with the new proposal.

RGISTools: Downloading, Customizing, and Processing Time Series of Remote Sensing Data in R

“…Many gap-filling and smoothing approaches have been developed to mitigate these issues (Shen, Li, Cheng, Zeng, Yang, Li, and Zhang 2015). Among them, there is the IMA procedure, which was developed by Militino et al (2018Militino et al ( , 2019.…”

The Methodology and Results from Ground Validation of Satellite Observations at Gas Flaring Sites in Nigeria

“…Multi-temporal imagery is generally geometrically and radiometrically accurate, but the residual noise arising from removal of clouds and other atmospheric and electronic effects can produce irregularity that must be mitigated to properly exploit the remote sensing information (Militino et al, 2018). Hence, causing distortion and missing of data in satellite imagery.…”

“…Other purposes of acquiring ground validation data include calibration of remote sensing sensors; and development of multi-satellite remote sensing interpretation (Morakinyo, 2015;Pressler and Walker, 1999). Some researchers on ground validation of satellite data include Militino et al (2018) who merged Moderate Resolution Imaging Spectroradiometer (MODIS) data with ground data for gap-filling and smoothing of satellite data for the purpose of eliminating irregularities. Yeboah et al (2017) and Pareta (2014) acquired Landsat 5 Thematic Mapper (TM) data, Landsat 7 Enhanced Thematic Mapper (ETM+) data, and field data for land…”

Filling missing data and smoothing altered data in satellite imagery with a spatial functional procedure

“…Recent examples are explained next. A hybrid generalised additive model and geo-statistical space-time model have been recently published by Poggio et al (2012) for fitting a smooth spatio-temporal trend, and a state-space model that accounts for spatio-temporal dependence (Militino et al, 2017), thinplate splines for modelling anomalies that use ground-truth data for improving the prediction quality (Militino et al, 2018), and a quantile regression method called Gapfill (Gerber et al, 2018(Gerber et al, , 2016 have also been proposed.…”