Assessment of FSDAF Accuracy on Cotton Yield Estimation Using Different MODIS Products and Landsat Based on the Mixed Degree Index with Different Surroundings

Association of residential greenness with chronotype among children

BACKGROUND

The association between residential greenness and chronotype remains unclear, especially among children. The current study aimed to explore the associations between residential greenness and chronotype parameters in children and examine potential pathways for these associations.

METHODS

In this cross-sectional study, 16,421 children ages 3-12 were included. Two satellite-derived vegetation indices, i.e., the normalized difference vegetation index (NDVI) and the enhanced vegetation index (EVI), were used to estimate residential greenness. The mid-sleep point on a workday (MSW) and the mid-sleep point on free days (MSF) were considered. And mid-sleep time on free days adjusted for sleep debt (MSFsc) was used as an indicator of chronotype. In addition to multivariable linear regression models, subgroup analyses were conducted to explore effect modifiers, and mediation analyses were used to explore possible mediating mechanisms of air pollutants underlying the associations.

RESULTS

An interquartile range (IQR) increase in both NDVI500-m and EVI500-m was significantly associated with an earlier MSFsc of -0.061 (95 % confidence interval (CI): -0.072, -0.049) and -0.054 (95 % CI: -0.066, -0.042), respectively. Non-linear dose response relationships were discovered between greenness indices and MSFsc and MSF. The results of stratified analyses showed the effect of residential greenness on MSW was stronger among primary school children and individuals with higher household income than among kindergarten children and those with lower household income. The joint mediation effects of PM2.5, PM1, PM10, NO2, and SO2 on the associations of NDVI500-m and EVI500-m with MSFsc were 89.6 % and 76.0 %, respectively.

CONCLUSIONS

Higher levels of residential greenness may have beneficial effects on an earlier chronotype in the child population, by reducing the effects of air pollutants, especially PM2.5. Our research hopes to promote the deployment of green infrastructure and healthy urban design strategies.

Impact assessment of urbanization on vegetation net primary productivity: A case study of the core development area in central plains urban agglomeration, China

Rapid urbanization process has a negative or positive impact on vegetation growth. Net primary productivity (NPP) is an effective indicator to characterize vegetation growth status. Taking the core development area of the Central Plains urban agglomeration as the study area, we estimated the NPP and its change trend in the past four decades using the Carnegie-Ames-Stanford Approach (CASA) model and statistical analysis based on meteorological and multi-source remote sensing data. Meanwhile, combined with the urbanization impact framework, we further analyzed urbanization's direct and indirect impact on NPP. The results showed that the urban area increased by 2688 km² during a high-speed urbanization process from 1983 to 2019. As a result of the intense urbanization process, a continuous NPP decrease (direct impact) can be seen, which aggravated along with the acceleration of the urban expansion, and the mean value of direct impact was 130.84 g C·m^-2·a^-1. Meanwhile, urbanization also had a positive impact on NPP (indirect impact). The indirect impact showed an increasing trend during urbanization with a mean value of 10.91 g C·m^-2·a^-1. The indirect impact was mainly related to temperature in climatic factors. The indirect impact has a seasonal heterogeneity, and high-temperature environments of urban areas are more effective in promoting vegetation growth in autumn and winter than in summer. Among different cities, high-speed development cities have higher indirect impact values than medium's and low's because of better ecological construction. This study is of great significance for understanding the impact of urbanization on vegetation growth in the Central Plains urban agglomeration area, supporting urban greening plans, and building sustainable and resilient urban agglomerations.

Integrating environmental and satellite data to estimate county-level cotton yield in Xinjiang Province

Accurate and timely estimation of cotton yield over large areas is essential for precision agriculture, facilitating the operation of commodity markets and guiding agronomic management practices. Remote sensing (RS) and crop models are effective means to predict cotton yield in the field. The satellite vegetation indices (VIs) can describe crop yield variations over large areas but can't take the exact environmental impact into consideration. Climate variables (CVs), the result of the influence of spatial heterogeneity in large regions, can provide environmental information for better estimation of cotton yield. In this study, the most important VIs and CVs for estimating county-level cotton yield across Xinjiang Province were screened out. We found that the VIs of canopy structure and chlorophyll contents, and the CVs of moisture, were the most significant factors for cotton growth. For yield estimation, we utilized four approaches: least absolute shrinkage and selection operator regression (LASSO), support vector regression (SVR), random forest regression (RFR) and long short-term memory (LSTM). Due to its ability to capture temporal features over the long term, LSTM performed best, with an R² of 0.76, root mean square error (RMSE) of 150 kg/ha and relative RMSE (rRMSE) of 8.67%; moreover, an additional 10% of the variance could be explained by adding CVs to the VIs. For the within-season yield estimation using LSTM, predictions made 2 months before harvest were the most accurate (R² = 0.65, RMSE = 220 kg/ha, rRMSE = 15.97%). Our study demonstrated the feasibility of yield estimation and early prediction at the county level over large cotton cultivation areas by integrating satellite and environmental data.

Study on the Estimation of Forest Volume Based on Multi-Source Data

We performed a comparative analysis of the prediction accuracy of machine learning methods and ordinary Kriging (OK) hybrid methods for forest volume models based on multi-source remote sensing data combined with ground survey data. Taking Larix olgensis, Pinus koraiensis, and Pinus sylvestris plantations in Mengjiagang forest farms as the research object, based on the Chinese Academy of Forestry LiDAR, charge-coupled device, and hyperspectral (CAF-LiTCHy) integrated system, we extracted the visible vegetation index, texture features, terrain factors, and point cloud feature variables, respectively. Random forest (RF), support vector regression (SVR), and an artificial neural network (ANN) were used to estimate forest volume. In the small-scale space, the estimation of sample plot volume is influenced by the surrounding environment as well as the neighboring observed data. Based on the residuals of these three machine learning models, OK interpolation was applied to construct new hybrid forest volume estimation models called random forest Kriging (RFK), support vector machines for regression Kriging (SVRK), and artificial neural network Kriging (ANNK). The six estimation models of forest volume were tested using the leave-one-out (Loo) cross-validation method. The prediction accuracies of these six models are better, with RLoo2 values above 0.6, and the prediction accuracy values of the hybrid models are all improved to different extents. Among the six models, the RFK hybrid model had the best prediction effect, with an RLoo2 reaching 0.915. Therefore, the machine learning method based on multi-source remote sensing factors is useful for forest volume estimation; in particular, the hybrid model constructed by combining machine learning and the OK method greatly improved the accuracy of forest volume estimation, which, thus, provides a fast and effective method for the remote sensing inversion estimation of forest volume and facilitates the management of forest resources.