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Abhishek Kumar

Publications and source records attributed to Abhishek Kumar.

4 recordsLinked to original sources
USGS2025

Phytoplankton biomass dynamics in wet (2019) and dry (2023) years in Lake Pontchartrain estuary, Louisiana from Sentinel 2-MSI and PACE-OCI observations

This study provides a comprehensive assessment of phytoplankton biomass dynamics in Lake Pontchartrain, Louisiana, by combining monthly water quality data with multispectral and hyperspectral satellite observations using a machine learning algorithm. A machine learning model based on Variational Autoencoder (VAE), globally applicable, was used to estimate phytoplankton biomass via chlorophyll- a (Chl- a ) from Sentinel 2-MSI and NASA's new hyperspectral mission, PACE-OCI, enabling the first direct comparison between the two sensors. The model performed well in this complex estuarine system, with higher accuracy from PACE-OCI (MAE: 1.48, RMSE: 10.40, slope: 0.87) than Sentinel 2-MSI (MAE: 1.57, RMSE: 11.08, slope: 0.83). This approach enabled continuous high-resolution monitoring of phytoplankton biomass across space and time. Comparative analysis of 2019, a wet year with Bonnet Carré Spillway (BCS) openings, and 2023, a dry year with extremely low riverine inputs, revealed distinct biomass dynamics. In 2019, BCS discharge initially suppressed Chl- a within turbid waters (<5 mg Chl- a m −3 ) but later acted as a nutrient and hydrodynamic driver, transporting nutrients toward the lake outlet and Mississippi coast, promoting high biomass (25–45 mg Chl- a m −3 ) near the entrance. In contrast, dry conditions in 2023 led to more frequent-than-expected high biomass (>35 mg Chl- a m −3 ), persisting in the lake center. Similar spatial patterns were observed again in 2024, revealed for the first time by PACE-OCI. This study demonstrates the value of satellite-derived observations for capturing transient phytoplankton biomass events and highlights the potential of PACE-OCI's hyperspectral capabilities to better distinguish phytoplankton communities and improve understanding of their responses to freshwater inflows and associated processes driving pulses into estuaries.

Louisiana↗
USGS2025

Modeling daily ice cover in northern hemisphere lakes with a long short‐term memory neural network

Quantifying lake ice loss is crucial for understanding the impact of climate change on lake ecosystems. In this study, we trained a deep learning model (Long-Short Term Memory with Landsat observations, 1984–2012) to simulate Northern Hemisphere lake ice changes at a fine spatial scale (> 0.1 km 2 ) from 1980 to 2022. The model achieved good performance overall during the test period (2013–2022), and the derived ice-on and ice-off matched well with two independent ice phenology data sets. Results reveal a 76.8% increase in intermittently ice-covered lakes from the 1980s to the 2010s, alongside a 10.7-day shorter ice duration and a 3.9 percentage-points reduction in annual mean ice cover fractions. The model can track daily partial ice cover changes, providing a novel contribution to understanding shifts in lake ice cover with climate change. These findings can provide valuable insights for future limnology studies, such as improving estimates of greenhouse gas emissions from lakes.

Geophysical Research Letters↗
USGS2024

A multi-sensor approach to characterize winter water-level drawdown patterns in lakes

Artificial manipulation of lake water levels through practices like winter water-level drawdown (WD) is prevalent across many regions, but the spatiotemporal patterns are not well documented due to limited in situ monitoring. Multi-sensor satellite remote sensing provides an opportunity to map and analyze drawdown frequency and metrics (timing, magnitude, duration) at broad scales. This study developed a cloud computing framework to process time series of synthetic aperture radar (Sentinel 1-SAR) and optical sensor (Landsat 8, Sentinel 2) data to characterize WD in 166 lakes across Massachusetts, USA, during 2016–2021. Comparisons with in situ logger data showed that the Sentinel 1-derived surface water area captured relative water-level fluctuations indicative of WD. A machine learning approach classified lakes as WD versus non-WD based on seasonal water-level fluctuations derived from Sentinel 1-SAR data. The framework mapped WD lakes statewide, revealing prevalence throughout Massachusetts with interannual variability. Results showed WDs occurred in over 75% of lakes during the study period, with high interannual variability in the number of lakes conducting WD. Mean WD magnitude was highest in the wettest year (2018) but % lake area exposure did not show any association with precipitation and varied between 8% to 12% over the 5-year period. WD start date was later and duration was longer in wet years, indicating climate mediation of WD implementation driven by management decisions. The data and tools developed provide an objective information resource to evaluate ecological impacts and guide management of this prevalent but understudied phenomenon. Overall, the results and interactive web tool developed as part of this study provide new hydrologic intelligence to inform water management and policies related to WD practices.

Massachusetts↗