Estimation of Chlorophyll-A Concentration with Remotely Sensed Data for the Nine Plateau Lakes in Yunnan Province

Abstract

The quantitative retrieval of the chlorophyll-a concentration is an important remote sensing method that is used to monitor the nutritional status of water bodies. The high spatial resolution of the Sentinel-2 MSI and its subdivision in the red-edge band highlight the characteristics of water chlorophyll-a, which is an important detection tool for assessing water quality parameters in plateau lakes. In this study, the Nine Plateau Lakes in the Yunnan-Kweichow Plateau of China were selected as the study area. Using Sentinel-2 MSI transit images and in situ measured chlorophyll-a concentration as the data source, the chlorophyll-a concentrations of plateau lakes (CCAPLs) were investigated, and the surface temperatures of plateau lakes (STPLs) were retrieved to verify the hypothesis that the lake surface temperature could increase the chlorophyll-a concentration. By comparing feature importance using a random forest (RF), the Sentinel-2 MSI surface reflectance and in situ data were linearly fitted using four retrieval spectral indices with high feature importance, and the accuracy of the estimated concentration of chlorophyll-a was evaluated by monitoring station data in the same period. Then, Landsat-8 TIRS Band 10 data were used to retrieve the STPL with a single-channel temperature retrieval algorithm and to verify the correlation between the STPL and the CCAPL. The results showed that the retrievals of the CCAPL and the STPL were consistent with the actual situation. The root-mean-square error (RMSE) of the fifteenth normalized difference chlorophyll-a index (NDCI15) was 0.0249. When the CCAPL was greater than 0.05 mg/L and the STPL was within 28–34 °C, there was a positive linear correlation between the CCAPL and the STPL. These results will provide support for the remote sensing monitoring of eutrophication in plateau lakes and will contribute to the scientific and effective management of plateau lakes.

Retrieval of Plateau Lake Water Surface Temperature from UAV Thermal Infrared Data

“…Lake water surface temperature (LWST) plays a pivotal role in shaping local climate and hydrological processes, standing as a fundamental parameter within lake ecosystems [1]. The investigation of LWST holds significant importance in elucidating the operational dynamics of lake ecosystems and addressing the challenges presented by climate change and extreme weather events [2]. Lakes, as indispensable freshwater reservoirs on Earth, support diverse ecosystems.…”

Seasonal changes of Chlorophyll-A concentration in Jiujiang city based on remote sensing

“…At the same time, frequent sampling is restricted by time and labor costs, making it difficult to comprehensively monitor seasonal changes in Chla concentration. With the development of satellite remote sensing technology, especially the widespread application of high-resolution satellite data has been made in water quality monitoring at large scale * Corresponding author (Jiang et al, 2020;Markogianni et al, 2020;Wang et al, 2022). Satellite remote sensing has the advantages of globality, regularity and large-scale, and can provide comprehensive Chla concentration information in lakes and rivers (Tong et al, 2022;Zhang et al, 2020).…”

Environmental Monitoring on the Surface of the Andaman Sea Over the Southwestern Coast of Thailand: A Case Study of Spatial and Temporal Variability of Chlorophyll-A

“…It can record data in a wide range and repeat it in the same area in each orbit of the satellite, so environmental changes can be monitored in different periods (Laosuwan & Uttaruk, 2017;Rotjanakusol & Laosuwan, 2019a;Rotjanakusol & Laosuwan, 2019b;Auntarin et al, 2021;Jomsrekrayom et al, 2021;Celik et al, 2022;Kanjanasiranont et al, 2022;Turton et al, 2022;Chen et al, 2023;Phoophiwfa et al, 2023). For this reason, this technology has been used for studying chlorophyll-a at the surface of the sea, such as in the studies by Lins et al (2017), Silveira et al (2020, Aranha et al (2022), and Wang et al (2022).…”