This study provided a comprehensive evaluation of eight machine learning regression algorithms for forest aboveground biomass (AGB) estimation from satellite data based on leaf area index, canopy height, net primary production, and tree cover data, as well as climatic and topographical data. Some of these algorithms have not been commonly used for forest AGB estimation such as the extremely randomized trees, stochastic gradient boosting, and categorical boosting (CatBoost) regression. For each algorithm, its hyperparameters were optimized using grid search with cross-validation, and the optimal AGB model was developed using the training dataset (80%) and AGB was predicted on the test dataset (20%). Performance metrics, feature importance as well as overestimation and underestimation were considered as indicators for evaluating the performance of an algorithm. To reduce the impacts of the random training-test data split and sampling method on the performance, the above procedures were repeated 50 times for each algorithm under the random sampling, the stratified sampling, and separate modeling scenarios. The results showed that five tree-based ensemble algorithms performed better than the three nonensemble algorithms (multivariate adaptive regression splines, support vector regression, and multilayer perceptron), and the CatBoost algorithm outperformed the other algorithms for AGB estimation. Compared with the random sampling scenario, the stratified sampling scenario and separate modeling did not significantly improve the AGB estimates, but modeling AGB for each forest type separately provided stable results in terms of the contributions of the predictor variables to the AGB estimates. All the algorithms showed forest AGB were underestimated when the AGB values were larger than 210 Mg/ha and overestimated when the AGB values were less than 120 Mg/ha. This study highlighted the capability of ensemble algorithms to improve AGB estimates and the necessity of improving AGB estimates for high and low AGB levels in future studies.
Surface longwave downward radiation (LWDR) plays a key role in determining the Arctic surface energy budget, especially in insolation-absent boreal winter. A reliable LWDR product is essential for understanding the intrinsic physical mechanisms of the rapid changes in the Arctic climate. The Medium-Resolution Spectral Imager (MERSI-2), a major payload of the Chinese second-generation polar-orbiting meteorological satellite, FengYun-3D (FY-3D), was designed similar to the NASA Moderate-Resolution Imaging Spectroradiometer (MODIS) in terms of the spectral bands. Although significant progress has been made in estimating clear-sky LWDR from MODIS observations using a variety of methods, few studies have focused on the retrieval of clear-sky LWDR from FY-3D MERSI-2 observations. In this study, we propose an advanced method to directly estimate the clear-sky LWDR in the Arctic from the FY-3D MERSI-2 thermal infrared (TIR) top-of-atmosphere (TOA) radiances and auxiliary information using the extremely randomized trees (ERT) machine learning algorithm. The retrieval accuracy of RMSE and bias, validated with the Baseline Surface Radiation Network (BSRN) in situ measurements, are 14.14 W/m2 and 4.36 W/m2, respectively, which is comparable and even better than previous studies. The scale effect in retrieval accuracy evaluation was further analyzed and showed that the validating window size could significantly influence the retrieval accuracy of the MERSI-2 clear-sky LWDR dataset. After aggregating to a spatial resolution of 9 km, the RMSE and bias of MERSI-2 retrievals can be reduced to 9.43 W/m2 and −0.14 W/m2, respectively. The retrieval accuracy of MERSI-2 clear-sky LWDR at the CERES SSF FOV spatial scale (approximately 20 km) can be further reduced to 8.64 W/m2, which is much higher than the reported accuracy of the CERES SSF products. This study demonstrates the feasibility of producing LWDR datasets from Chinese FY-3D MERSI-2 observations using machine learning methods.
Aim: To date, PM2.5-associated vascular damage in metabolic abnormalities has remained controversial. We knew little about the vascular damage of PM2.5 constituents. Thus, this study aimed to investigate the relationship between long-term exposure to PM2.5 and its constituents and vascular damage in metabolic abnormalities. Methods: A total of 124,387 participants with metabolic abnormalities (defined as at least one metabolic disorder, such as obesity, elevated blood pressure, elevated triglyceride level, elevated fasting glucose level, or low HDL cholesterol level) were recruited in this study from 11 representative centers in China between January 2011 and December 2017. PM2.5 and its constituents (black carbon [BC], organic matter [OM], sulfate [SO4 2− ], nitrate [NO3 − ], and ammonium salts [NH4 + ]) were extracted. Elevated brachial-ankle pulse wave velocity (baPWV) (≥ 1,400 cm/s) and declined ankle-brachial index (ABI) (<0.9) indicated vascular damage. Multivariable logistic regression and Quantile g-Computation models were utilized to explore the impact on outcomes.Results: Of the 124,387 participants (median age, 49 years), 87,870 (70.64%) were men. One-year lag exposure to PM2.5 and its constituents was significantly associated with vascular damage in single pollutant models. The adjusted odds ratios (OR) for each 1-μg/m 3 increase in PM2.5 was 1.013 (95% CI, 1.012-1.015) and 1.031 (95% CI, 1.025-1.037) for elevated baPWV and decreased ABI, respectively. PM2.5 constituents were also associated with vascular damage in multi-pollutant models. Among the PM2.5 constituents, BC (47.17%), SO4 2− (33.59%), and NH4 + (19.23%) have the highest contribution to elevated baPWV and NO3 − (47.89%) and BC (23.50%) to declined ABI. Conclusion:Chronic exposure to PM2.5 and PM2.5 constituents was related to vascular damage in the abnormal metabolic population in China. The heterogeneous contribution of different PM2.5 constituents to vessel bed damage is worthy of attention when developing targeted strategies.
Landsat has provided the longest fine resolution data archive of Earth’s environment since 1972; however, one of the challenges in using Landsat data for various applications is its frequent large data gaps and heavy cloud contaminations. One pressing research topic is to generate the regular time series by integrating coarse-resolution satellite data through data fusion techniques. This study presents a novel spatiotemporal fusion (STF) method based on a depthwise separable convolutional neural network (DSC), namely, STFDSC, to generate Landsat-surface reflectance time series at 8-day intervals by fusing Landsat 30 m with high-quality Moderate Resolution Imaging Spectroradiometer (MODIS) 500 m surface reflectance data. The STFDSC method consists of three main stages: feature extraction, feature fusion and prediction. Features were first extracted from Landsat and MODIS surface reflectance changes, and the extracted multilevel features were then stacked and fused. Both low-level and middle-level features that were generally ignored in convolutional neural network (CNN)-based fusion models were included in STFDSC to avoid key information loss and thus ensure high prediction accuracy. The prediction stage generated a Landsat residual image and is combined with original Landsat data to obtain predictions of Landsat imagery at the target date. The performance of STFDSC was evaluated in the Greater Khingan Mountains (GKM) in Northeast China and the Ziwuling (ZWL) forest region in Northwest China. A comparison of STFDSC with four published fusion methods, including two classic fusion methods (FSDAF, ESTARFM) and two machine learning methods (EDCSTFN and STFNET), was also carried out. The results showed that STFDSC made stable and more accurate predictions of Landsat surface reflectance than other methods in both the GKM and ZWL regions. The root-mean-square-errors (RMSEs) of TM bands 2, 3, 4, and 7 were 0.0046, 0.0038, 0.0143, and 0.0055 in GKM, respectively, and 0.0246, 0.0176, 0.0280, and 0.0141 in ZWL, respectively; it can be potentially used for generating the global surface reflectance and other high-level land products.
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