Bayesian atmospheric correction over land: Sentinel-2/MSI and Landsat 8/OLI

Mitigating the impact of atmospheric effects on optical remote sensing data is critical for monitoring intrinsic land processes and developing Analysis Ready Data (ARD). This work develops an approach to this for the NERC NCEO medium resolution ARD Landsat 8 (L8) and Sentinel 2 (S2) products, called Sensor Invariant Atmospheric Correction (SIAC). The contribution of the work is to phrase and solve that problem within a probabilistic (Bayesian) framework for medium resolution multispectral sensors S2/MSI and L8/OLI and to provide per-pixel uncertainty estimates traceable from assumed top-of-atmosphere (TOA) measurement uncertainty, making progress towards an important aspect of CEOS ARD target requirements. A set of observational and a priori constraints are developed in SIAC to constrain an estimate of coarse resolution (500 m) aerosol optical thickness (AOT) and total column water vapour (TCWV), along with associated uncertainty. This is then used to estimate the medium resolution (10–60 m) surface reflectance and uncertainty, given an assumed uncertainty of 5 % in TOA reflectance. The coarse resolution a priori constraints used are the MODIS MCD43 BRDF/Albedo product, giving a constraint on 500 m surface reflectance, and the Copernicus Atmosphere Monitoring Service (CAMS) operational forecasts of AOT and TCWV, providing estimates of atmospheric state at core 40 km spatial resolution, with an associated 500 m resolution spatial correlation model. The mapping in spatial scale between medium resolution observations and the coarser resolution constraints is achieved using a calibrated effective point spread function for MCD43. Efficient approximations (emulators) to the outputs of the 6S atmospheric radiative transfer code are used to estimate the state parameters in the atmospheric correction stage. SIAC is demonstrated for a set of global S2 and L8 images covering AERONET and RadCalNet sites. AOT retrievals show a very high correlation to AERONET estimates (correlation coefficient around 0.86, RMSE of 0.07 for both sensors), although with a small bias in AOT. TCWV is accurately retrieved from both sensors (correlation coefficient over 0.96, RMSE <0.32 g cm−2). Comparisons with in situ surface reflectance measurements from the RadCalNet network show that SIAC provides accurate estimates of surface reflectance across the entire spectrum, with RMSE mismatches with the reference data between 0.01 and 0.02 in units of reflectance for both S2 and L8. For near-simultaneous S2 and L8 acquisitions, there is a very tight relationship (correlation coefficient over 0.95 for all common bands) between surface reflectance from both sensors, with negligible biases. Uncertainty estimates are assessed through discrepancy analysis and are found to provide viable estimates for AOT and TCWV. For surface reflectance, they give conservative estimates of uncertainty, suggesting that a lower estimate of TOA reflectance uncertainty might be appropriate

Linking Remote Sensing with APSIM through Emulation and Bayesian Optimization to Improve Yield Prediction

The enormous increase in the volume of Earth Observations (EOs) has provided the scientific community with unprecedented temporal, spatial, and spectral information. However, this increase in the volume of EOs has not yet resulted in proportional progress with our ability to forecast agricultural systems. This study examines the applicability of EOs obtained from Sentinel-2 and Landsat-8 for constraining the APSIM-Maize model parameters. We leveraged leaf area index (LAI) retrieved from Sentinel-2 and Landsat-8 NDVI (Normalized Difference Vegetation Index) to constrain a series of APSIM-Maize model parameters in three different Bayesian multi-criteria optimization frameworks across 13 different calibration sites in the U.S. Midwest. The novelty of the current study lies in its approach in providing a mathematical framework to directly integrate EOs into process-based models for improved parameter estimation and system representation. Thus, a time variant sensitivity analysis was performed to identify the most influential parameters driving the LAI (Leaf Area Index) estimates in APSIM-Maize model. Then surrogate models were developed using random samples taken from the parameter space using Latin hypercube sampling to emulate APSIM’s behavior in simulating NDVI and LAI at all sites. Site-level, global and hierarchical Bayesian optimization models were then developed using the site-level emulators to simultaneously constrain all parameters and estimate the site to site variability in crop parameters. For within sample predictions, site-level optimization showed the largest predictive uncertainty around LAI and crop yield, whereas the global optimization showed the most constraint predictions for these variables. The lowest RMSE within sample yield prediction was found for hierarchical optimization scheme (1423 Kg ha−1) while the largest RMSE was found for site-level (1494 Kg ha−1). In out-of-sample predictions for within the spatio-temporal extent of the training sites, global optimization showed lower RMSE (1627 Kg ha−1) compared to the hierarchical approach (1822 Kg ha−1) across 90 independent sites in the U.S. Midwest. On comparison between these two optimization schemes across another 242 independent sites outside the spatio-temporal extent of the training sites, global optimization also showed substantially lower RMSE (1554 Kg ha−1) as compared to the hierarchical approach (2532 Kg ha−1). Overall, EOs demonstrated their real use case for constraining process-based crop models and showed comparable results to model calibration exercises using only field measurements

Quantifying Vegetation Biophysical Variables from Imaging Spectroscopy Data: A Review on Retrieval Methods

An unprecedented spectroscopic data stream will soon become available with forthcoming Earth-observing satellite missions equipped with imaging spectroradiometers. This data stream will open up a vast array of opportunities to quantify a diversity of biochemical and structural vegetation properties. The processing requirements for such large data streams require reliable retrieval techniques enabling the spatiotemporally explicit quantification of biophysical variables. With the aim of preparing for this new era of Earth observation, this review summarizes the state-of-the-art retrieval methods that have been applied in experimental imaging spectroscopy studies inferring all kinds of vegetation biophysical variables. Identified retrieval methods are categorized into: (1) parametric regression, including vegetation indices, shape indices and spectral transformations; (2) nonparametric regression, including linear and nonlinear machine learning regression algorithms; (3) physically based, including inversion of radiative transfer models (RTMs) using numerical optimization and look-up table approaches; and (4) hybrid regression methods, which combine RTM simulations with machine learning regression methods. For each of these categories, an overview of widely applied methods with application to mapping vegetation properties is given. In view of processing imaging spectroscopy data, a critical aspect involves the challenge of dealing with spectral multicollinearity. The ability to provide robust estimates, retrieval uncertainties and acceptable retrieval processing speed are other important aspects in view of operational processing. Recommendations towards new-generation spectroscopy-based processing chains for operational production of biophysical variables are given

Linking Remote Sensing with APSIM through Emulation and Bayesian Optimization to Improve Yield Prediction

The enormous increase in the volume of Earth Observations (EOs) has provided the scientific community with unprecedented temporal, spatial, and spectral information. However, this increase in the volume of EOs has not yet resulted in proportional progress with our ability to forecast agricultural systems. This study examines the applicability of EOs obtained from Sentinel-2 and Landsat-8 for constraining the APSIM-Maize model parameters. We leveraged leaf area index (LAI) retrieved from Sentinel-2 and Landsat-8 NDVI (Normalized Difference Vegetation Index) to constrain a series of APSIM-Maize model parameters in three different Bayesian multi-criteria optimization frameworks across 13 different calibration sites in the U.S. Midwest. The novelty of the current study lies in its approach in providing a mathematical framework to directly integrate EOs into process-based models for improved parameter estimation and system representation. Thus, a time variant sensitivity analysis was performed to identify the most influential parameters driving the LAI (Leaf Area Index) estimates in APSIM-Maize model. Then surrogate models were developed using random samples taken from the parameter space using Latin hypercube sampling to emulate APSIM’s behavior in simulating NDVI and LAI at all sites. Site-level, global and hierarchical Bayesian optimization models were then developed using the site-level emulators to simultaneously constrain all parameters and estimate the site to site variability in crop parameters. For within sample predictions, site-level optimization showed the largest predictive uncertainty around LAI and crop yield, whereas the global optimization showed the most constraint predictions for these variables. The lowest RMSE within sample yield prediction was found for hierarchical optimization scheme (1423 Kg ha−1) while the largest RMSE was found for site-level (1494 Kg ha−1). In out-of-sample predictions for within the spatio-temporal extent of the training sites, global optimization showed lower RMSE (1627 Kg ha−1) compared to the hierarchical approach (1822 Kg ha−1) across 90 independent sites in the U.S. Midwest. On comparison between these two optimization schemes across another 242 independent sites outside the spatio-temporal extent of the training sites, global optimization also showed substantially lower RMSE (1554 Kg ha−1) as compared to the hierarchical approach (2532 Kg ha−1). Overall, EOs demonstrated their real use case for constraining process-based crop models and showed comparable results to model calibration exercises using only field measurements

Global variations in heart failure etiology, management, and outcomes

Importance: Most epidemiological studies of heart failure (HF) have been conducted in high-income countries with limited comparable data from middle- or low-income countries. Objective: To examine differences in HF etiology, treatment, and outcomes between groups of countries at different levels of economic development. Design, Setting, and Participants: Multinational HF registry of 23 341 participants in 40 high-income, upper–middle-income, lower–middle-income, and low-income countries, followed up for a median period of 2.0 years. Main Outcomes and Measures: HF cause, HF medication use, hospitalization, and death. Results: Mean (SD) age of participants was 63.1 (14.9) years, and 9119 (39.1%) were female. The most common cause of HF was ischemic heart disease (38.1%) followed by hypertension (20.2%). The proportion of participants with HF with reduced ejection fraction taking the combination of a β-blocker, renin-angiotensin system inhibitor, and mineralocorticoid receptor antagonist was highest in upper–middle-income (61.9%) and high-income countries (51.1%), and it was lowest in low-income (45.7%) and lower–middle-income countries (39.5%) (P < .001). The age- and sex- standardized mortality rate per 100 person-years was lowest in high-income countries (7.8 [95% CI, 7.5-8.2]), 9.3 (95% CI, 8.8-9.9) in upper–middle-income countries, 15.7 (95% CI, 15.0-16.4) in lower–middle-income countries, and it was highest in low-income countries (19.1 [95% CI, 17.6-20.7]). Hospitalization rates were more frequent than death rates in high-income countries (ratio = 3.8) and in upper–middle-income countries (ratio = 2.4), similar in lower–middle-income countries (ratio = 1.1), and less frequent in low-income countries (ratio = 0.6). The 30-day case-fatality rate after first hospital admission was lowest in high-income countries (6.7%), followed by upper–middle-income countries (9.7%), then lower–middle-income countries (21.1%), and highest in low-income countries (31.6%). The proportional risk of death within 30 days of a first hospital admission was 3- to 5-fold higher in lower–middle-income countries and low-income countries compared with high-income countries after adjusting for patient characteristics and use of long-term HF therapies. Conclusions and Relevance: This study of HF patients from 40 different countries and derived from 4 different economic levels demonstrated differences in HF etiologies, management, and outcomes. These data may be useful in planning approaches to improve HF prevention and treatment globally