Publications

Guo, HW; Huang, JJ; Zhu, XT; Tian, S; Wang, BL (2024). Spatiotemporal variation reconstruction of total phosphorus in the Great Lakes since 2002 using remote sensing and deep neural network. WATER RESEARCH, 255, 121493.

Abstract
Total phosphorus (TP) is non-optically active, thus TP concentration (CTP) estimation using remote sensing still exists grand challenge. This study developed a deep neural network model (DNN) for CTP estimation with synchronous in-situ measurements and MODIS-derived remote sensing reflectance ( R rs ) ( N = 3916). Using DNN, the annual and intra-annual CTP spatial distributions of the Great Lakes since 2002 were reconstructed. Then, the reconstructions were correlated to nine potential factors, e.g., Chlorophyll-a, snowmelt, and cropland, to explain seasonal and long -term CTP variations. The results showed that DNN reliably estimated CTP from MODIS R rs , with R- 2 , mean absolute error (MAE), root mean squared error (RMSE), mean absolute percentage error (MAPE), and root mean squared logarithmic error (RMSLE) of 0.83, 1.05 mu g/L, 2.95 mu g/L, 9.92%, and 0.13 on the test set. The near-surface CTP in the Great Lakes decreased significantly ( p < 0.05) during 2002 - 2022, primarily attributed to cropland reduction, coupled with improvements in basin natural ecosystems. The sensitivity analysis verified the model robustness when confronted with input feature changes < 35%. This result along with the marginal difference between CTP derived from two sensors (R 2 = 0.76, MAE = 2.12 mu g/L, RMSE = 2.51 mu g/L, MAPE = 11.52%, RMSLE = 0.24) suggested the model transferability from MODIS to VIIRS. This transformation facilitated optimal usage of MODIS-related archive and enhanced the continuity of CTP estimation at moderate resolution. This study presents a practical method for spatiotemporal reconstruction of CTP using remote sensing, and contributes to better understandings of driving factors behind CTP variations in the Great Lakes.

DOI:
10.1016/j.watres.2024.121493

ISSN:
1879-2448