Improving remote sensing estimation of Secchi disk depth for global lakes and reservoirs using machine learning methods

TY - JOUR

T1 - Improving remote sensing estimation of Secchi disk depth for global lakes and reservoirs using machine learning methods

AU - Zhang, Yibo

AU - Shi, Kun

AU - Sun, Xiao

AU - Zhang, Yunlin

AU - Li, Na

AU - Wang, Weijia

AU - Zhou, Yongqiang

AU - Wei, Zhi

AU - Liu, Mingliang

AU - Li, Yuan

AU - Zhu, Guangwei

AU - Qin, Boqiang

AU - Jeppesen, Erik

AU - Zhou, Jian

AU - Li, Huiyun

PY - 2022/12

Y1 - 2022/12

N2 - Secchi disk depth (SDD) is a simple but particularly important indicator for characterizing the overall water quality status and assessing the long-term dynamics of water quality for diverse global waters. For this reason, countless efforts have been made to collect SDD data from the field and through remote sensing systems. Many empirical and semianalytical algorithms have been proposed to estimate SDD from different satellite images for a specific or regional water. However, the construction of a robust global SDD estimation model is still challenging due to the nonlinear response of SDD to optical properties and the complex physical and biogeochemical processes of different waters. Therefore, machine learning methods to better interpret nonlinear processes were used to improve remote sensing estimations of SDD for global lakes and reservoirs based on a global matchup dataset from Landsat TM (N = 4099), ETM+ (N = 2420), and OLI (N = 1249) covering in situ SDD from 0.01 m to over 18 m. Overall, extreme gradient boosting (XGBoost) and random forest (RF) had better SDD retrievals than back propagation neural network, support vector regression, empirical and quasi-analytical models showing high precision with mean relative error of approximately 30% and good agreements with the long-term in situ SDD in different waters with various optical properties. Our results can support long-term global-level water quality evaluation and thus making informed decisions about development policy.

AB - Secchi disk depth (SDD) is a simple but particularly important indicator for characterizing the overall water quality status and assessing the long-term dynamics of water quality for diverse global waters. For this reason, countless efforts have been made to collect SDD data from the field and through remote sensing systems. Many empirical and semianalytical algorithms have been proposed to estimate SDD from different satellite images for a specific or regional water. However, the construction of a robust global SDD estimation model is still challenging due to the nonlinear response of SDD to optical properties and the complex physical and biogeochemical processes of different waters. Therefore, machine learning methods to better interpret nonlinear processes were used to improve remote sensing estimations of SDD for global lakes and reservoirs based on a global matchup dataset from Landsat TM (N = 4099), ETM+ (N = 2420), and OLI (N = 1249) covering in situ SDD from 0.01 m to over 18 m. Overall, extreme gradient boosting (XGBoost) and random forest (RF) had better SDD retrievals than back propagation neural network, support vector regression, empirical and quasi-analytical models showing high precision with mean relative error of approximately 30% and good agreements with the long-term in situ SDD in different waters with various optical properties. Our results can support long-term global-level water quality evaluation and thus making informed decisions about development policy.

KW - Global lakes

KW - XGBoost

KW - random forest

KW - reservoirs

KW - water clarity

U2 - 10.1080/15481603.2022.2116102

DO - 10.1080/15481603.2022.2116102

M3 - Journal article

SN - 1548-1603

VL - 59

SP - 1367

EP - 1383

JO - GIScience & Remote Sensing

JF - GIScience & Remote Sensing

IS - 1

ER -