A Google Earth Engine Application to Retrieve Long-Term Surface Temperature for Small Lakes. Case: San Pedro Lagoons, Chile

Abstract

Lake surface water temperature (LSWT) is a crucial water quality parameter that modulates many lake and reservoir processes. Therefore, it is necessary to monitor it from a long-term perspective. Over the last decades, many methods to retrieve LSWT fields from satellite imagery have been developed. This work aims to test, implement and automate six methods. These are performed in the Google Earth Engine (GEE) platform, using 30 m spatial resolution images from Landsat 7 and 8 satellites for 2000–2020. Automated methods deliver long-term time series. Series are then calibrated with in situ data. Two-dimensional (2D) × time data fields are built on the lakes with the calibration, and a subsequent LSWT climatology is derived. Our study area is two urban lagoons with areas smaller than two (2) km2 of the city of San Pedro de la Paz, South-Central Chile. The six methods describe the seasonal variation of LSWT (Willmott’s index of agreement > 0.91, R2 > 0.67). The main difference between series is their bias. Thus, after a simple calibration, all series adequately describe the LSWT. We utilized the Pedro de la Paz lagoons to demonstrate the method’s utility. Our research demonstrates that these adjacent lagoons exhibit comparable LSWT spatial (15.5–17 ∘C) and temporal (7–25 ∘C) trends throughout the year. Differences in geographical pattern might result from the northern island’s heat impact and the existence of the Biobío river to the east. Our work represents an efficient alternative for obtaining LSWT in particular lakes and reservoirs, especially useful in medium and small-sized ones.

Spatiotemporal Variations in Biophysical Water Quality Parameters: An Integrated In-Situ and Remote Sensing Analysis for an Urban Lake in Chile

“…Our results clearly show a spatial pattern varying from higher Chl-a values in the northern zone compared to the southern zone. These results are in agreement with the research of Pedrero-Guarda et al, [23], which identified spatial variations in temperature in Laguna Grande, possibly attributed to a heat island effect. This effect causes temperature differences, with higher temperatures near urban areas (northern part) and lower temperatures in vegetated regions.…”

“…Since 2014, sampling is performed only at the Sector Centro 2 station, but the available information only includes three sampling missions by year between 2014 and 2016. Assuming four measurements per year, the percentage of missing data at stations Centro, Desagüe, Sur and Centro 2 between 1988 and 2016 is 28% [23]. These factors serve as a focal point to address the main problem that this study aims to solve.…”

“…This was achieved through a multitemporal analysis using satellite imagery. Recently, Pedreros-Guarda et al [23], implemented six automated methods for retrieving the surface water temperature of the lagoon using satellite thermal imagery.…”

Spatiotemporal Variations in Biophysical Water Quality Parameters: An Integrated In Situ and Remote Sensing Analysis of an Urban Lake in Chile

“…(iii) Assessment of the spatial-temporal variability Finally, results clearly showed a spatial pattern with higher Chl-a values in the northern zone compared to the southern zone. These results are in agreement with those of Pedrero-Guarda et al [25], who identified spatial dynamics of temperature in Laguna Grande, possibly attributed to a heat island effect. This effect causes temperature differences, with higher temperatures near urban areas (northern part) and lower temperatures in vegetated regions.…”

“…This was achieved through a multitemporal analysis using satellite imagery. Recently, Pedreros-Guarda et al [25], implemented six automated methods for retrieving the surface water temperature of the lagoon using satellite thermal imagery.…”