Search

Ginkgo biloba L. is a rare dioecious species that is valued for its diverse applications and is cultivated globally. This study aimed to develop a rapid and effective method for determining the sex of a Ginkgo biloba. Green and yellow leaves representing annual growth stages were scanned with a hyperspectral imager, and classification models for RGB images, spectral features, and a fusion of spectral and image features were established. Initially, a ResNet101 model classified the RGB dataset using the proportional scaling–background expansion preprocessing method, achieving an accuracy of 90.27%. Further, machine learning algorithms like support vector machine (SVM), linear discriminant analysis (LDA), and subspace discriminant analysis (SDA) were applied. Optimal results were achieved with SVM and SDA in the green leaf stage and LDA in the yellow leaf stage, with prediction accuracies of 87.35% and 98.85%, respectively. To fully utilize the optimal model, a two-stage Period-Predetermined (PP) method was proposed, and a fusion dataset was built using the spectral and image features. The overall accuracy for the prediction set was as high as 96.30%. This is the first study to establish a standard technique framework for Ginkgo sex classification using hyperspectral imaging, offering an efficient tool for industrial and ecological applications and the potential for classifying other dioecious plants. [ABSTRACT FROM AUTHOR]

Global climate change is a serious threat to food and energy security. Crop growth modelling is an important tool for simulating crop food production and assisting in decision making. Planting date is one of the important model parameters. Larger-scale spatial distribution with high accuracy for planting dates is essential for the widespread application of crop growth models. In this study, a planting date prediction method based on environmental similarity was developed in accordance with the third law of geography. Spring maize planting date observations from 124 agricultural meteorological experiment stations in China over the years 1992–2010 were used as the data source. Samples spanning from 1992 to 2009 were allocated as training data, while samples from 2010 constituted the independent validation set. The results indicated that the root mean square error (RMSE) for spring maize planting date based on environmental similarity was 10 days, which is better than that of multiple regression analysis (RMSE = 13 days) in 2010. Additionally, when applied at varying scales, the accuracy of national-scale prediction was better than that of regional-scale prediction in areas with large differences in planting dates. Consequently, the method based on environmental similarity can effectively and accurately estimate planting date parameters at multiple scales and provide reasonable parameter support for large-scale crop growth modelling. [ABSTRACT FROM AUTHOR]

Background: Long-term surface NO 2 data are essential for retrospective policy evaluation and chronic human exposure assessment. In the absence of NO 2 observations for Mainland China before 2013, training a model with 2013-2018 data to make predictions for 2005-2012 (back-extrapolation) could cause substantial estimation bias due to concept drift., Objective: This study aims to correct the estimation bias in order to reconstruct the spatiotemporal distribution of daily surface NO 2 levels across China during 2005-2018., Methods: On the basis of ground- and satellite-based data, we proposed the robust back-extrapolation with a random forest (RBE-RF) to simulate the surface NO 2 through intermediate modeling of the scaling factors. For comparison purposes, we also employed a random forest (Base-RF), as a representative of the commonly used approach, to directly model the surface NO 2 levels., Results: The validation against Taiwan's NO 2 observations during 2005-2012 showed that RBE-RF adequately corrected the substantial underestimation by Base-RF. The RMSE decreased from 10.1 to 8.2 µg/m 3 , 7.1 to 4.3 µg/m 3 , and 6.1 to 2.9 µg/m 3 in predicting daily, monthly, and annual levels, respectively. For North China with the most severe pollution, the population-weighted NO 2 ([NO 2 ] pw ) during 2005-2012 was estimated as 40.2 and 50.9 µg/m 3 by Base-RF and RBE-RF, respectively, i.e., 21.0% difference. While both models predicted that the national annual [NO 2 ] pw increased during 2005-2011 and then decreased, the interannual trends were underestimated by >50.2% by Base-RF relative to RBE-RF. During 2005-2018, the nationwide population that lived in the areas with NO 2  > 40 µg/m 3 were estimated as 259 and 460 million by Base-RF and RBE-RF, respectively., Conclusion: With RBE-RF, we corrected the estimation bias in back-extrapolation and obtained a full-coverage dataset of daily surface NO 2 across China during 2005-2018, which is valuable for environmental management and epidemiological research., (Copyright © 2021 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

Satellite-retrieved aerosol optical depth (AOD) is commonly used to estimate ambient levels of fine particulate matter (PM 2.5 ), though it is important to mitigate the estimation bias of PM 2.5 due to gaps in satellite-retrieved AOD. A nonparametric approach with two random-forest submodels is proposed to estimate PM 2.5 levels by filling gaps in satellite-retrieved AOD. This novel approach was employed to estimate the spatiotemporal distribution of daily PM 2.5 levels during 2013-2015 in the Sichuan Basin of Southwest China, where the coverage rate of composite AOD retrieved by the Terra and Aqua satellites was only 11.7%. Based on the retrieved AOD and various covariates (including meteorological conditions and land use types), the first random-forest submodel (named AOD-submodel) was trained to fill the gaps in the AOD dataset, giving a cross-validation R 2 of 0.95. Subsequently, the second random-forest submodel (named PM 2.5 -submodel) was trained to estimate the PM 2.5 levels for unmonitored areas/days based on the gap-filled AOD, ground-monitored PM 2.5 levels, and the covariates, and achieved a cross-validation R 2 of 0.86. By comparing the complete and incomplete (i.e., without the days when AOD data were missing) estimates, we found that the monthly PM 2.5 levels could be overestimated by 34.6% if the PM 2.5 values coincident with AOD gaps were not considered. The newly developed approach is valuable for deriving the complete spatiotemporal distribution of daily PM 2.5 from incomplete remote-sensing data, which is essential for air quality management and human exposure assessment., (Copyright © 2018 Elsevier Ltd. All rights reserved.)

A novel model named random-forest-spatiotemporal-kriging (RF-STK) was developed to estimate the daily ambient NO 2 concentrations across China during 2013-2016 based on the satellite retrievals and geographic covariates. The RF-STK model showed good prediction performance, with cross-validation R 2 = 0.62 (RMSE = 13.3 μg/m 3 ) for daily and R 2 = 0.73 (RMSE = 6.5 μg/m 3 ) for spatial predictions. The nationwide population-weighted multiyear average of NO 2 was predicted to be 30.9 ± 11.7 μg/m 3 (mean ± standard deviation), with a slowly but significantly decreasing trend at a rate of -0.88 ± 0.38 μg/m 3 /year. Among the main economic zones of China, the Pearl River Delta showed the fastest decreasing rate of -1.37 μg/m 3 /year, while the Beijing-Tianjin Metro did not show a temporal trend ( P = 0.32). The population-weighted NO 2 was predicted to be the highest in North China (40.3 ± 10.3 μg/m 3 ) and lowest in Southwest China (24.9 ± 9.4 μg/m 3 ). Approximately 25% of the population lived in nonattainment areas with annual-average NO 2 > 40 μg/m 3 . A piecewise linear function with an abrupt point around 100 people/km 2 characterized the relationship between the population density and the NO 2 , indicating a threshold of aggravated NO 2 pollution due to urbanization. Leveraging the ground-level NO 2 observations, this study fills the gap of statistically modeling nationwide NO 2 in China, and provides essential data for epidemiological research and air quality management.