Zhibin Sun

Climate change and water scarcity are pressing public concerns, severely impacting agricultural production and ecological balance. Leveraging advanced remote sensing, this study analyzed 30m-resolution Landsat normalized difference vegetation index (NDVI) and evapotranspiration (ET) data during Colorado's growing seasons (April–August) from 2000 to 2018. Examining correlations across various land cover types, it detailed data processing, relation analysis at different lead times, and correlation classification. Results show a mostly positive correlation in Colorado, aligning with coarser-resolution studies, with some negative exceptions. Uniquely, ET's influence on NDVI weakens as lead time extends, regardless of correlation. Combining with land cover analysis, forests with high water storage maintain stable ET. Additionally, human intervention significantly affects these correlations.

Camellia oleifera is an oilseed crop that holds significant economic, ecological, and social value. In the realm of Camellia oleifera cultivation, utilizing hyperspectral analysis techniques to estimate chlorophyll content can enhance our understanding of its physiological parameters and response characteristics. However, hyperspectral datasets contain information from many wavelengths, resulting in high-dimensional data. Therefore, selecting effective wavelengths is crucial for processing hyperspectral data and modeling in retrieval studies. In this study, by using hyperspectral data and chlorophyll content from Camellia oleifera samples, three different dimensionality reduction methods (Taylor-CC, NCC, and PCC) are used in the first round of dimensionality reduction. Combined with these methods, various thresholds and dimensionality reduction methods (with/without further dimensionality reduction) are used in the second round of dimensionality reduction; different sets of core wavelengths with equal size are identified respectively. Using hyperspectral reflectance data at different sets of core wavelengths, multiple machine learning models (Lasso, ANN, and RF) are constructed to predict the chlorophyll content of Camellia oleifera. The purpose of this study is to compare the performance of various dimensionality reduction methods in conjunction with machine learning models for predicting the chlorophyll content of Camellia oleifera. Results show that (1) the Taylor-CC method can effectively select core wavelengths with high sensitivity to chlorophyll variation; (2) the two-stage hybrid dimensionality reduction methods demonstrate superiority in three models; (3) the Taylor-CC + NCC method combined with an ANN achieves the best predictive performance of chlorophyll content. The new two-stage dimensionality reduction method proposed in this study not only improves both the efficiency of hyperspectral data processing and the predictive accuracy of models, but can serve as a complement to the study of Camellia oleifera properties using the Taylor-CC method.

In 1960, R.E. Kalman published his famous paper describing a recursive solution, the Kalman filter, to the discrete-data linear filtering problem. In the following decades, thanks to the continuous progress of numerical computing, as well as the increasing demand for weather prediction, target tracking, and many other problems, the Kalman filter has gradually become one of the most important tools in science and engineering. With the continuous improvement of its theory, the Kalman filter and its derivative algorithms have become one of the core algorithms in optimal estimation. This paper attempts to systematically collect and sort out the basic principles of the Kalman filter and some of its important derivative algorithms (mainly including the Extended Kalman filter (EKF), the Unscented Kalman filter (UKF), the Ensemble Kalman filter (EnKF)), as well as the scope of their application, and also to compare their advantages and limitations. In addition, because there are a large number of applications based on the Kalman filter in data assimilation, this paper also provides examples and classifies the applications of both the Kalman filter and its derivative algorithms in the field of data assimilation.

The interference of background noise leads to the extremely high spatial complexity of hyperspectral data. Sensitive band selecting is an important task to minimize or eliminate the influence of non-target elements. In this study, Taylor expansion is innovatively used to identify core wavelengths/bands of hyperspectral data. Unlike other traditional methods, this proposed Taylor-CC method considers more local and global information of spectral function to estimate the linear/nonlinear correlation between two wavelengths. Using samples of hyperspectral data with a wavelength range of 350–2500 nm and SPAD for Camellia oleifera, this Taylor-CC method is compared with the traditional PCC method derived from the Pearson correlation coefficient. Using the 240 samples with their different 57 core wavelengths identified by the Taylor-CC method and PCC method, three machine models (i.e., random forest-RF, linear regression-LR, and artificial neural network-ANN) are trained to compare their performances. Their results show that the correlation matrix from the Taylor-CC method represents a clear diagonal pattern with near zero values at most locations away from the diagonal, and all three models confirm that the Taylor-CC method is superior to the PCC method. Moreover, the SPAD spectral response relationship based on machine learning algorithms is constructed, and ANN is the best prediction performance among the three models when using the core wavelengths identified by the Taylor-CC method. The Taylor-CC method proposed in this study not only lays a mathematical foundation for the next analysis of the response mechanism between spectral characteristics and nutrient content of Camellia leaf, but also provides a new idea for the correlation analysis of adjacent spectral bands for hyperspectral signals in many applications.

Forest is the largest vegetation carbon pool in the global terrestrial ecosystem. The spatial distribution and change of forest biomass are of importance to reveal the surface spatial variation and driving factors, to analyze and evaluate forest productivity, and to evaluate ecological function of forest. In this study, broad-leaved forests located in a typical state nature reserve in northern subtropics were selected as the study area. Based on ground survey data and high-resolution remote sensing images, three machine learning models were used to identify the best remote sensing quantitative inversion model of forest biomass. The biomass of broad-leaved forest with 30-m resolution in the study area from 1998 to 2016 was estimated by using the best model about every two years. With the estimated biomass, multiple leading factors to cause biomass temporal change were then identified from dozens of remote sensing factors by investigating their nonlinear correlations. Our results showed that the artificial neural network (ANN) model was the best (R² = 0.8742) among the three, and its accuracy was also much higher than that of the traditional linear or nonlinear models. The mean biomass of the broad-leaved forest in the study area from 1998 to 2016 ranged from 90 to 145 Mg ha⁻¹, showing an obvious temporal variation. Instead of biomass, biomass change (BC) was studied further in this research. Significant correlations were found between BC in broad-leaved forest and three climate factors, including average daily maximum surface temperature, maximum precipitation, and maximum mean temperature. It was also found that BC has a strong correlation with the biomass at the previous time (i.e., two years ago). Those quantitative correlations were used to construct a linear model of BC with high accuracy (R² = 0.8873), providing a new way to estimate the biomass change of two years later based on the observations of current biomass and the three climate factors.

The information of the locations and numbers of failures is crucial to precise management of new afforestation, especially during seedling replanting in young forests. In practice, foresters are more accustomed to determining the locations of failures according to their rows than based on their geographical coordinates. The relative locations of failures are more difficult to collect than the absolute geographic coordinates which are available from an orthoimage. This paper develops a novel methodology for obtaining the relative locations of failures in rows and counting the number of failures in each row. The methodology contains two parts: (1) the interpretation of the direction angle of seedlings rows on an unmanned aerial vehicle (UAV) orthoimage based on the probability statistical theory (called the grid-variance (GV) method); (2) the recognition of the centerline of each seedling rows using K-means and the approach to counting failures in each row based on the distribution of canopy pixels near the centerline of each seedling row (called the centerline (CL) method). The experimental results showed that the GV method can accurately interpret the direction angle of rows (45°) in an orthoimage and the CL method can quickly and accurately obtain the numbers and relative locations of failures in rows. The failure detection rates in the two experimental areas were 91.8% and 95%, respectively. These research findings can provide technical support for the precise cultivation of planted seedling forests.

In recent years, satellite remote sensing has been increasingly used in the long-term observation of ozone (O₃) precursors and its formation regime. In this work, formaldehyde (HCHO) data from Ozone Monitoring Instrument (OMI) were used to analyse the temporal and spatial distribution of HCHO vertical column densities (VCDs) in Shanghai from 2010 to 2019. HCHO VCDs exhibited the highest value in summer and the lowest in winter, the high-VCD concentrated in western Shanghai. Temperature largely influence HCHO by affecting the biogenic emissions and photochemical reactions, and industry was the major anthropogenic source. The satellite observed formaldehyde to nitrogen dioxide ratio (FNR_SAT) reflects that the O₃ formation regime had significant seasonal characteristics and gradually manifested as transitional ozone formation regime dominated in Shanghai. The uneven distribution in space was mainly reflected as the higher FNR_SAT and surface O₃ concentration in rural area. To compensate the shortcoming of FNR_SAT that it can only characterize O₃ formation around satellite overpass time, correction of FNR_SAT was implemented with hourly surface FNR and O₃ data. After correction, O₃ formation regime showed the trend moving towards VOC-limited in both time and space, and regime indicated by FNR_SAT can better reflect O₃ formation for a day. This study can help us better understand HCHO characteristics and O₃ formation regime in Shanghai, and also provide a method to improve FNR_SAT for characterizing O₃ formation in a day, which will be significant for developing O₃ prevention and control strategies.