Remote sensing estimation of dissolved organic carbon concentrations in Chinese lakes based on Landsat images

Abstract

Long-term estimation of dissolved organic carbon (DOC) dynamics based on satellite data provides important information to comprehensively understand the lake carbon cycle, climate change, and sustainable water resource management. Here, we proposed a novel DOC concentration estimation model based on water classification using a large field dataset (n = 1929) collected from 123 Chinese lakes. We divided water into terrestrial-DOM-dominated and endogenous-DOM-dominated water bodies using the optimal spectral slope ratio (SR) threshold determined by setting the classification scenario. Then, we calibrated the optimal DOC estimation model using stepwise quadratic polynomial regression for the different types of water and determined the final DOC estimation model. The final DOC estimation model performed well in both the calibration and validation datasets, with R2 ≥ 0.78, RMSD ≤ 0.78 mg/L, and MAPD ≤ 16.51 %. Subsequently, we mapped the spatiotemporal dynamics of DOC concentrations in Chinese lakes with surface area greater than 1 km2 (n = 2621) between 1986 and 2021 using the Landsat data. Spatially, we observed obvious spatial heterogeneity in the DOC concentrations in the Chinese lakes, with the highest DOC concentration occurring in the Northeast Region (NER) (4.39 ± 0.21 mg/L) and the lowest DOC concentration occurring in the Qinghai-Tibet Plateau Region (QTR) (3.76 ± 1.12 mg/L). Temporally, the average DOC concentration in Chinese lakes generally decreased at a rate of − 0.09 mg/L decade−1 from 1986 to 2021, during which 56.5 % of the lakes experienced a decrease, 29.6 % of the lakes experienced no obvious change, and 13.9 % of the lakes experienced an increase. We also revealed that human activities are the main drivers of DOC increases in lakes, while vegetation and climate change are the main drivers of DOC decreases in lakes. Our results contribute to improving the accuracy of organic carbon estimations in lakes worldwide and provides meaningful insights into the lake carbon cycle..