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Abstract
Phosphorus (P) is an important substance for the growth of phytoplankton and an efficient index to assess the water quality. However, estimation of the TP concentration in waters by remote sensing must be associated with optical substances such as the chlorophyll-a (Chla) and the suspended particulate matter (SPM). Based on the good correlation between the suspended inorganic matter (SPIM) and P in Lake Hongze, we used the direct and indirect derivation methods to develop algorithms for the total phosphorus (TP) estimation with the MODIS/Aqua data. Results demonstrate that the direct derivation algorithm based on 645 nm and 1240 nm of the MODIS/Aqua performs a satisfied accuracy (R2 = 0.75, RMSE = 0.029mg/L, MRE = 39% for the training dataset, R2 = 0.68, RMSE = 0.033mg/L, MRE = 47% for the validate dataset), which is better than that of the indirect derivation algorithm. The 645 nm and 1240 nm of MODIS are the main characteristic band of the SPM, so that algorithm can effectively reflect the P variations in Lake Hongze. Additionally, the ratio of the TP to the SPM is positively correlated with the accuracy of the algorithm as well. The proportion of the SPIM in the SPM has a complex effect on the accuracy of the algorithm. When the SPIM accounts for 78%, the algorithm achieves the highest accuracy. Furthermore, the performance of this direct derivation algorithm was examined in two inland lakes in China (Lake Nanyi and Lake Chaohu), it derived the expected P distribution in Lake Nanyi whereas the algorithm failed in Lake Chaohu. Different water properties influence significantly the accuracy of this direct derivation algorithm, while the TP, Chla, and suspended particular inorganic matter (SPOM) of Lake Chaohu are much higher than those of the other two lakes, thus it is difficult to estimate the TP concentration by a simple band combination in Lake Chaohu. Although the algorithm depends on the dataset used in the development, it usually presents a good estimation for those waters where the SPIM dominated, especially when the SPIM accounts for 60% to 80% of the SPM. This research proposed a direct derivation algorithm for the TP estimation for the turbid lake and will provide a theoretical and practical reference for extending the optical remote sensing application and the TP empirical algorithm of Lake Hongze’s help for the local government management water quality.
Highlights
In recent years, with the rapid development of the economy, the intensity of land development and human activities is increasing, P emissions from point sources and non-point sources are increasing year by year in China
(1) Is there an underlying mechanism that explains why the direct derivation algorithms perform better than the indirect derivation algorithms for Lake Hongze? What are the disadvantages of the indirect derivation algorithms? (2) Why can empirical algorithms estimate the total phosphorus (TP)? Can this algorithm be interpreted from the perspective of remote sensing? (3) How is the performance of the algorithm? Will other substances influence the algorithm? How to influence? (4) Can the TP algorithm of Lake Hongze be applied to other lakes? Why or why not?
The suspended inorganic matter (SPIM)/suspended particulate matter (SPM) has a complex effect on the accuracy of the algorithm, where when the SPIM accounts for 78%, the algorithm achieves the highest accuracy, which begins to decrease with increasing SPIM
Summary
With the rapid development of the economy, the intensity of land development and human activities is increasing, P emissions from point sources and non-point sources are increasing year by year in China. Lake pollution has been aggravating in the eastern plain lake zone of China [1,2,3]. In addition to some lakes that have been seriously polluted, some lakes are undergoing eutrophication, for instance, the water quality of Lake Hongze, the fourth largest freshwater lake in China, decreased drastically [4], with the lake becoming turbid and the water more eutrophic [5,6]. Traditional water quality monitoring relies on monitoring stations or lake tour gauging. It is critical to be able to estimate the water properties accurately and quickly. For this task, it is believed that remote sensing technologies are the most convenient way to provide such information
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