Machine Learning Algorithms for Chromophoric Dissolved Organic Matter (CDOM) Estimation Based on Landsat 8 Images

Xiao Sun,Yunlin Zhang,Yibo Zhang,Kun Shi,Na Li,Yongqiang Zhou

doi:10.3390/rs13183560

Abstract

Chromophoric dissolved organic matter (CDOM) is crucial in the biogeochemical cycle and carbon cycle of aquatic environments. However, in inland waters, remotely sensed estimates of CDOM remain challenging due to the low optical signal of CDOM and complex optical conditions. Therefore, developing efficient, practical and robust models to estimate CDOM absorption coefficient in inland waters is essential for successful water environment monitoring and management. We examined and improved different machine learning algorithms using extensive CDOM measurements and Landsat 8 images covering different trophic states to develop the robust CDOM estimation model. The algorithms were evaluated via 111 Landsat 8 images and 1708 field measurements covering CDOM light absorption coefficient a(254) from 2.64 to 34.04 m−1. Overall, the four machine learning algorithms achieved more than 70% accuracy for CDOM absorption coefficient estimation. Based on model training, validation and the application on Landsat 8 OLI images, we found that the Gaussian process regression (GPR) had higher stability and estimation accuracy (R2 = 0.74, mean relative error (MRE) = 22.2%) than the other models. The estimation accuracy and MRE were R2 = 0.75 and MRE = 22.5% for backpropagation (BP) neural network, R2 = 0.71 and MRE = 24.4% for random forest regression (RFR) and R2 = 0.71 and MRE = 24.4% for support vector regression (SVR). In contrast, the best three empirical models had estimation accuracies of R2 less than 0.56. The model accuracies applied to Landsat images of Lake Qiandaohu (oligo-mesotrophic state) were better than those of Lake Taihu (eutrophic state) because of the more complex optical conditions in eutrophic lakes. Therefore, machine learning algorithms have great potential for CDOM monitoring in inland waters based on large datasets. Our study demonstrates that machine learning algorithms are available to map CDOM spatial-temporal patterns in inland waters.

Highlights

Introduction published maps and institutional affilChromophoric dissolved organic matter (CDOM), which is referred to as gelbstoff, gilvin or yellow matter, is widely found in natural water bodies and is a soluble and complicated mixture of organic substances consisting mainly of humic acids, fulvic acids and aromatic polymers [1]
Machine learning algorithms have great potential for CDOM monitoring in inland waters based on large datasets
Our study demonstrates that machine learning algorithms are available to map CDOM spatial-temporal patterns in inland waters

Summary

Introduction

Introduction published maps and institutional affilChromophoric dissolved organic matter (CDOM), which is referred to as gelbstoff, gilvin or yellow matter, is widely found in natural water bodies and is a soluble and complicated mixture of organic substances consisting mainly of humic acids, fulvic acids and aromatic polymers [1]. CDOM can affect the underwater light field, and its generation, transport and transformation processes influence the biogeochemical recycling of carbon, nitrogen and phosphorus in the water column [2,3,4,5,6]. Current research suggests that CDOM in water comes from multiple sources, including (a) allochthonous sources, which mainly include degraded organic matter from the surrounding terrestrial environment as input from terrestrial runoff, precipitation and groundwater recharge, and resuspension of sediments [7], and (b) autochthonous sources, which include the chemical degradation products of organisms from phytoplankton, macrophyte and bacteria [8]. The degradation process of CDOM mainly includes photochemical iations.

Methods

Discussion

Conclusion