Remote Sensing Estimation of Lake Total Phosphorus Concentration Based on MODIS: A Case Study of Lake Hongze

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.

Combining Artificial Neural Networks with Causal Inference for Total Phosphorus Concentration Estimation and Sensitive Spectral Bands Exploration Using MODIS

“…Abundant characteristics make ANN approaches adaptive to different research areas and outperform traditional empirical methods. Because nonlinear components were shown to be useful for the estimation of water quality parameters [7,37], the nonlinear components, such as exponential, logarithmic, and power transformations of each visible and infrared band of MODIS were also chosen as input data for the ANN. For each reflectance ρ, ln(ρ), exp(ρ), ρ 2 , ρ 3 , ρ −1 , ρ −2 , ρ −3 were calculated and scaled to a 0-1 range in each dimension.…”

“…Total phosphorus (TP) concentration is a key water quality parameter for the monitoring and assessment of water supplies. Phosphorus is a major indicator of trophic states and an essential element for plants to grow [4][5][6][7]. TP is closely associated with optically active substances, such as chlorophyll-a (Chl-a), colored dissolved organic matter (CDOM), and total suspended matter (TSM) [7,8].…”

“…Because the inherent optical characteristics of lakes are often different, the semi-analytical methods lack generality [8,29]. Empirical approaches [7,30], such as indirect and direct methods [8], can be easily operated. These approaches are based on the connection between remote-sensing reflectance and optically active substances.…”

“…A time lag may exist between the change of the TP concentration and that of the optically active substance [5]. The unstable and inaccurate correlation function results in a low accuracy of the indirect methods [7]. Direct methods use spectral bands, band transformations, or band combinations to construct statistical models to directly estimate the TP concentration.…”

Retrieval of Non-Optically Active Parameters for Small Scale Urban Waterbodies by a Machine Learning-Based Strategy

“…Over the past several decades, scholars have carried out extensive research on water quality monitoring by remote sensing, and have achieved good results in estimating optically active parameters, such as Chlorophyll-a (Chl-a), suspended solids (SS), colored dissolved organic matter (CDOM), turbidity and transparency [11][12][13][14][15][16]. There are however few studies on non-optically active parameters, such as chemical oxygen demand (COD), total phosphorus (TP), and total nitrogen (TN) [17,18,[27][28][29][19][20][21][22][23][24][25][26]. Estimating non-optically active parameters directly from spectral characteristics is difficult.…”