Utilizing neural networks for image downscaling and water quality monitoring

Remotely sensed images are becoming highly required for various applications, especially those related to natural resource management. The Moderate Resolution Imaging Spectroradiometer (MODIS) data has the advantages of its high spectral and temporal resolutions but remains inadequate in providing the required high spatial resolution. On the other hand, Sentinel-2 is more advantageous in spatial and temporal resolution but lacks a solid historical database. In this study, four MODIS bands in the visible and near-infrared spectral regions of the electromagnetic spectrum and their matching Sentinel-2 bands were used to monitor the turbidity in Lake Nasser, Egypt. The MODIS data were downscaled to Sentinel-2, which enhanced its spatial resolution from 250 and 500m to 10m.Furthermore, it provided a historical database that was used to monitor the changes in lake turbidity. Spatial approach based on neural networks was presented to downscale MODIS bands to the spatial resolution of the Sentinel-2 bands. The correlation coefficient between the predicted and actual images exceeded 0.70 for the four bands. Applying this approach, the downscaled MODIS images were developed and the neural networks were further employed to these images to develop a model for predicting the turbidity in the lake. The correlation coefficient between the predicted and actual measurements reached 0.83. The study suggests neural networks as a comparatively simplified and accurate method for image downscaling compared to other methods. It also demonstrated the possibility of utilizing neural networks to accurately predict lake water quality parameters such as turbidity from remote sensing data compared to statistical methods

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

International audienc

Enjeux de la réduction d'échelle dans l'estimation par télédétection des déterminants climatiques

Ce travail s'inscrit dans le cadre de recherche sur les maladies vectorielles de Lyme et Virus du Nil au sein de l'Agence de Santé Publique du Canada (ASPC) ayant pour finalité d'évaluer et de cartographier les risques sanitaires associés à ces maladies infectieuses liées au climat aux échelles municipales, provinciales et fédérale. Dans ce contexte, cette recherche vise à démontrer la faisabilité, la pertinence et les enjeux de recourir aux méthodes de réduction d'échelle pour obtenir à une haute résolution spatio-temporelle (100/30 m et 1 jour) avec au plus des marges d'erreur de 2 unités, des déterminants climatiques et microclimatiques (DCMC) en milieu hétérogène du Canada. Un cadre méthodologique d'application des méthodes de réduction d'échelle, Random Forest Regression (RFR), Thermal sharpening (TsHARP), Pixel block intensity modulation (PBIM), a été proposé pour estimer la température de surface (LST) de MODIS 1000 m à 100/30 m. Des expérimentations basées sur cette approche ont été effectuées sur trois sites au Québec à différentes époques. Les résultats, spatialement représentatifs, ont été validés avec les températures de l'air et celles prises par de Landsat 08 avec des marges d'erreur autour de 2°C. L'analyse des résultats démontre la capacité effective des méthodes de réduction d'échelle à discriminer la LST dans l'espace. Toutefois, dans le contexte du projet de l'ASPC, ces résultats sont non concluants à 100/30 m en l'absence d'une plus-value significative au plan spatial de LST. Cette analyse a conduit à discuter des enjeux temporels, spatiaux, méthodologiques et de gestion de gros volumes de données en lien avec la réduction d'échelle dans le contexte du projet.This research is part of the Public Health Agency of Canada's (PHAC) research on Lyme and West Nile Virus vector-borne diseases, which aims to assess and map the health risks associated with these climate-related infectious diseases at the municipal, provincial and federal levels. In this context, this research aims to demonstrate the feasibility, relevance and challenges of using downscaling methods to obtain high spatial and temporal resolution (100/30 m and 1 day), with margins of error of no more than 2 units, of climatic and microclimatic determinants (CMDs) in a heterogeneous Canadian environment. A methodological framework for the application of downscaling methods, Random Forest Regression (RFR), Thermal sharpening (TsHARP), Pixel block intensity modulation (PBIM), has been proposed to estimate the surface temperature (LST) from MODIS 1000 m to 100/30 m. Experiments with our approach were carried out at three sites in Quebec at different times. The spatially representative results were validated with air and Landsat 08 temperatures with error margins around 2°C. The analysis of our results demonstrates the effective capacity of downscaling methods to discriminate LST in space. However, in the context of the ASPC project, these results are inconclusive at 100/30 m in the absence of a significant, expected increase in the spatial accuracy of LST. This analysis led to a discussion of the temporal, spatial, methodological and large data volume management issues related to downscaling in the context of the project