Wen Li

Quality water plays a huge role in human life. Chlorophyll-a (Chl-a) in water bodies is a direct reflection of the population size of the primary productivity of various phytoplankton species in the water body and can provide critical information on the health of water ecosystems and the pollution status of water quality. Case 2 Regional CoastColour (C2RCC) is a networked atmospheric correction processor introduced by the Sentinel Application Platform for various remote sensing products. Among them, the Extreme Case-2 Waters (C2X) process has demonstrated advantages in inland complex waters, enabling the generation of band data, conc_chl product for Chl-a, and kd_z90max product for Secchi Depth (SD). Accurate in situ data are essential for the development of reliable Chl-a models, while in situ data measurement is limited by many factors. To explore and improve the uncertainties involved, we combined the C2X method with Sentinel-2 imagery and water quality data, taking lakes in Wuhan from 2018 to 2021 as a case. A Chl-a model was developed and validated using an empirical SD model and a neural network incorporating Trophic Level Index (TLI) to derive the predicted correction result, Chl-a_t. The results indicated that (1) the conc_chl product measured by C2X and in situ Chl-a exhibited consistent overall trends, with the highest correlation observed in the range of 2–10 μg/L. (2) The corrected Chl-a_t using the conc_chl product had a mean absolute error of approximately 10–15 μg/L and a root-mean-square error of approximately 8–10 μg/L, while using in situ Chl-a had a root-mean-square error (RMSE) of approximately 15 μg/L and a mean absolute error (MAE) of approximately 20 μg/L; both errors decreased by double after correction. (3) The correlation coefficient (R) between Chl-a_t and each data point in the Chl-a model results was lower than that of SD-a_t with each data point in the SD model results. Additionally, the difference in R-value between Chl-a_t and each data point (0.45–0.60) was larger than that of SD-a_t with each data point (0.35–0.5). (4) When using corrected Chl-a_t data to calculate the TLI estimation model, both RMSE and MAE decreased, which were 1μg/L lower than those derived from uncorrected data, while R increased, indicating an improvement in accuracy and reliability. These findings demonstrated the presence of in situ errors in Chl-a measurements, which must be acknowledged during research. This study holds practical significance as some of these errors can be effectively corrected through the use of C2X atmospheric correction on spectral bands.

Background: Jixi is a typical mining city in China that has undergone dramatic changes in its land-use pattern of mining areas over the development of its coal resources. The impacts of coal mining activities have greatly affected the regional land surface temperature and ecological system. Methods: The Landsat 8 Operational Land Imager (OLI) data from 2015 and 2019 were used from the Jiguan, Didao, and Chengzihe District of Jixi in Heilongjiang, China as the study area. The calculations to determine the land-use classification, vegetation coverage, and land surface temperature (LST) were performed using ArcGIS10.5 and ENVI 5.3 software packages. A correlation analysis revealed the impact of land-use type, vegetation coverage, and coal mining activities on LSTs. Results: The results show significant spatial differentiation in the LSTs of Jixi City. The LSTs for various land-use types were ranked from high to low as follows: mining land > construction land > grassland > cultivated land > forest land > water area. The LST was lower in areas with high vegetation coverage than in other areas. For every 0.1 increase in vegetation coverage, the LST is expected to drop by approximately 0.75 °C. An analysis of mining land patches indicates that the patch area of mining lands has a significant positive correlation with both the average and maximum patch temperatures. The average patch temperature shows a logarithmic increase with the growth of the patch area, and within 200,000 m², the average patch temperature increases significantly. The maximum patch temperature shows a linear increase with the patch area growth, and for every 100,000 m² increase in the patch area of mining lands, the maximum patch temperature increases by approximately 0.81 °C. The higher the average patch temperature of mining land, the higher the temperature in its buffer zone, and the greater its influence scope. This study provides a useful reference for exploring the warming effects caused by coal mining activities and the definition of its influence scope.