Amyotrophic lateral sclerosis (ALS) is a progressive, fatal neurodegenerative disease with a lifetime risk of developing as 1 in 700. Despite many recent discoveries about the genetics of ALS, the etiology of sporadic ALS remains largely unknown with gene-environment interaction suspected as a driver. Water quality and the toxin beta methyl-amino-alanine produced by cyanobacteria are suspected environmental triggers. Our objective was to develop an eco-epidemiological modeling approach to characterize the spatial relationships between areas of higher than expected ALS incidence and lake water quality risk factors derived from satellite remote sensing as a surrogate marker of exposure.

Synthetic Aperture Radar (SAR), as an active sensor transmitting long wavelengths, has the advantages of working day and night and without rain or cloud disturbance. It is further able to sense the geometric structure of forests more than passive optical sensors, making it a valuable tool for mapping forest Above Ground Biomass (AGB). This paper studies the ability of the single- and multi-temporal C-band Sentinel-1 and polarimetric L-band PALSAR-2 data to estimate live AGB based on ground truth data collected in New England, USA in 2017. Comparisons of results using the Simple Water Cloud Model (SWCM) on both VH and VV polarizations show that C-band reaches saturation much faster than the L-band due to its limited forest canopy penetration. The exhaustive search multiple linear regression model over the many polarimetric parameters from PALSAR-2 data shows that the combination of polarimetric parameters could slightly improve the AGB estimation, with an adjusted R² as high as 0.43 and RMSE of around 70 Mg/ha when decomposed Pv component and Alpha angle are used. Additionally, the single- and multi-temporal C-band Sentinel-1 data are compared, which demonstrates that the multi-temporal Sentinel-1 significantly improves the AGB estimation, but still has a much lower adjusted R² due to the limitations of the short wavelength. Finally, a site-level comparison between paired control and treatment sites shows that the L-band aligns better with the ground truth than the C-band, showing the high potential of the models to be applied to relative biomass change detection.

Reoccurring seasonal cyanobacterial harmful algal blooms (CHABs) persist in many waters, and recent work has shown links between CHAB and elevated risk of amyotrophic lateral sclerosis (ALS). Quantifying the exposure levels of CHAB as a potential risk factor for ALS is complicated by human mobility, potential pathways, and data availability. In this work, we develop phycocyanin concentration (i.e., CHAB exposure) maps using satellite remote sensing across northern New England to assess relationships with ALS cases using a spatial epidemiological approach. Strategic semi-analytical regression models integrated Landsat and in situ observations to map phycocyanin concentration (PC) for all lakes greater than 8 ha (n = 4117) across the region. Then, systematic versions of a Bayesian Poisson Log-linear model were fit to assess the mapped PC as a risk factor for ALS while accounting for model uncertainty and modifiable area unit problems. The satellite remote sensing of PC had strong overall ability to map conditions (adj. R2, 0.86; RMSE, 11.92) and spatial variability across the region. PC tended to be positively associated with ALS risk with the level of significance depending on fixed model components. Meta-analysis shows that when average PC exposure is 100 μg/L, an all model average odds ratio is 1.48, meaning there is about a 48% increase in average ALS risk. This research generated the first regionally comprehensive map of PC for thousands of lakes and integrated robust spatial uncertainty. The outcomes support the hypothesis that cyanotoxins increase the risk of ALS, which helps our understanding of the etiology of ALS.

Lakes have been suggested as an indicator of climate change; however, long-term, systematic records of lake temperature are limited. Satellite remote sensing is capable of supporting lake temperature mapping with the advantage of large-area and systematic observations. The goal of this research application was to assess spatiotemporal trends in lake skin temperature for all lakes over 8 ha across northern New England for the past three decades. Nearly 10 000 Landsat scenes for July, August, and September from 1984 to 2014 were processed using MODTRAN and MERRA parameterizations to generate atmospherically corrected lake skin temperature records. Results show, on average, lakes warmed at a rate of 0.8°C decade−1, with smaller lakes warming at a faster rate. Complementing regression and space–time analyses showed similar results (R2 = 0.63) for lake temperature trends and found lakes, on average, are warming faster than daily maximum or minimum air temperature. No major hot spots were found as lake temperature changes were heterogeneous on a local scale and evenly distributed across the region. Maximum and minimum daily temperature, lake size, and elevation were found as significant drivers of lake temperature. This effort provides the first regionally focused and comprehensive spatiotemporal assessment of thousands (n = 3955) of lakes concentrated in one geographic region. The approach is scalable and adaptable to any region for assessing lake temperature trends and potential drivers.

Crop area mapping is important for tracking agricultural production and supporting food security. Spaceborne approaches using synthetic aperture radar (SAR) now allow for mapping crop area at moderate spatial and temporal resolutions. Multi-frequency SAR data is highly useful for crop monitoring because backscatter response from vegetation canopies is wavelength dependent. This study evaluates the utility of C-band Sentinel-1B (Sentinel-1) and L-band ALOS-2 (PALSAR) data, collected during the 2019 growing season, for generating accurate active crop extent (crop vs. non-crop) classifications over an agricultural region in western Canada. Evaluations were performed against the Agriculture and Agri-Food Canada satellite-based Annual Cropland Inventory (ACI), an open data product that maps land cover across the extent of Canada’s agricultural land. Classifications were performed using the temporal coefficient of variation (CV) approach, where an optimal crop/non-crop delineating CV threshold (CV_thr) is selected according to Youden’s J-statistic. Results show that crop area mapping agreed better with the ACI when using Sentinel-1 data (83.5%) compared to PALSAR (73.2%). Analysis of performance by crop reveals that PALSAR’s poorer performance can be attributed to soybean, urban, grassland, and pasture ACI classes. This study also compared CV values to in situ wet biomass data for canola and soybeans, showing that crops with lower biomass (soybean) had correspondingly lower CV values.

Irrigated rice requires intense water management under typical agronomic practices. Cost effective tools to improve the efficiency and assessment of water use is a key need for industry and resource managers to scale ecosystem services. In this research we advance model‐based decomposition and machine learning to map inundated rice using time‐series polarimetric, L‐band Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) observations. Simultaneous ground truth observations recorded water depth inundation during the 2019 crop season using instrumented fields across the study site in Arkansas, USA. A three‐component model‐based decomposition generated metrics representing surface‐, double bounce‐, and volume‐scattering along with a shape factor, randomness factor, and the Radar Vegetation Index (RVI). These physically meaningful metrics characterized crop inundation status independent of growth stage including under dense canopy cover. Machine learning (ML) comparisons employed Random Forest (RF) using the UAVSAR derived parameters to identify cropland inundation status across the region. Outcomes show that RVI, proportion of the double‐bounce within total scattering, and the relative comparison between the double‐bounce and the volume scattering have moderate to strong mechanistic ability to identify rice inundation status with Overall Accuracy (OA) achieving 75%. The use of relative ratios further helped mitigate the impacts of far range incidence angles. The RF approach, which requires training data, achieved a higher OA and Kappa of 88% and 71%, respectively, when leveraging multiple SAR parameters. Thus, the combination of physical characterization and ML provides a powerful approach to retrieving cropland inundation under the canopy. The growth of polarimetric L‐band availability should enhance cropland inundation metrics beyond open water that are required for tracking water quantity at field scale over large areas.