Publications

Xue, C; Cannizzaro, JP; Hu, CM; Barnes, BB; Xie, YY; Qi, L; Armstrong, C; Chen, ZQ; Jones, PR (2025). Long-Term Changes of Chlorophyll-a in Lake Okeechobee: Combining the Strengths of In Situ Observations, Multi-Sensor Remote Sensing, and Machine Learning. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 63, 4206415.

Abstract
Many lakes around the world have faced eutrophication and frequent cyanobacterial harmful algal blooms (cyanoHABs), where chlorophyll-a (Chla) concentration is often used to determine the trophic status of the lake and evaluate the effectiveness of nutrient reduction strategies. However, limited in situ sampling and the absence of robust remotely sensed data product often hinder the analysis of long-term Chla changes in such lakes. Here, using Lake Okeechobee (LO) as an example, we addressed this challenge by developing a novel method to harnesses the power of both multi-sensor remote sensing and machine learning (ML). The method first used in situ Chla to estimate Chla from OLCI imagery (Chla(OLCI)), which then served as a surrogate for in situ data toward obtaining the large matchup datasets for MODIS model training. Subsequently, two deep neural network (DNN) models, DNNS and DNNNS, were trained for the MODIS-saturated pixels with high algal biomass waters and the non-saturated pixels with lower Chla but more turbid waters, respectively. The daily and monthly Chla derived from MODIS (Chla(MODIS)) were validated using in situ Chla (R-2 similar to 0.77, Mean absolute percentage difference (MAPD) similar to 25%) and monthly Chla(OLCI)(R-2 similar to 0.90, MAPD similar to 11%), respectively. Monthly Chla(MODIS) derived from Terra in 2000-2023 was used to analyze the spatiotemporal changes in LO. We observed a significant long-term increasing trend of Chla (similar to 2 mg m(-3) decade(-1)) and a similar to 30% increase relative to the multiple-year average. To explore the potential factors driving long-term Chla changes, water temperature, precipitation, inflow, stage, and the multivariate ENSO index (MEI) were tested through multi-variative analysis. Although further testing is required, the approach of using one sensor as a bridge to bring limited in situ data and another sensor together to overcome the data scarcity and lack of spectral bands is believed to be extendable to other eutrophic lakes around the world.

DOI:
10.1109/TGRS.2025.3567318

ISSN:
1558-0644