Estimation Of Content Research Articles

A matter of the metric? Sugar content overestimation is less pronounced in sugar cubes versus grams

To make healthy food choices, consumers need to be aware of the sugar content of foods. Units act as an environmental cue that might influence sugar content estimation accuracy. The present study (1) tested whether estimations of sugar content are more accurate in sugar cubes vs grams, (2) compared accuracy of sugar content to estimations of the foods’ weight and energy content, and (3) investigated gender, education, and body mass index as potential correlates. A sample of 886 adults was randomly assigned to estimating the sugar content of 10 common foods in grams or cubes. Estimations of sugar content diverged considerably from actual values in both groups (0.22 ≤ Cohen's dsgrams ≤ 1.20; 0.20 ≤ Cohen's dscubes ≤ 1.10), but were more pronounced for sugar content estimations in grams in 7 out of 10 foods (ts ≥ 4.04, Ps < .001, Cohen's ds ≥ 0.14). Sugar content misestimation was somewhat more pronounced than misestimation of weight (0.05 ≤ Cohen's ds ≤ 1.43) and energy content (0.04 ≤ Cohen's ds ≤ 1.19). Relationships between sugar content misestimation and gender (0.00 ≤ Cohen's ds ≤ 0.33), education (–0.07 ≤ r ≤ 0.11), and body mass index (–0.08 ≤ r ≤ 0.06) were mostly negligible. Although sugar content estimations were somewhat more accurate in sugar cubes vs grams, estimation accuracy is generally low. In addition to promoting consumers’ knowledge through labeling and education, additional avenues for interventions might need to be explored for sizeable effects on food choices.

Be aware of the effect of electrode activation and morphology on its performance in gas diffusion electrode setups

The superior performance enhancement in catalyst research for proton exchange membrane fuel cells (PEMFC) revealed in the model rotating disc electrode (RDE) environment is rarely demonstrated in membrane electrode assemblies (MEA). The discrepancy is typically attributed to the difference in the chemical structure and morphology of the catalyst layer (CL), affecting the transport of reactants of the oxygen reduction reaction (ORR). In this study, the gas diffusion electrode (GDE) half-cell setup is used to focus on crucial aspects of CL development, especially on the activation and morphology of CLs, to gain a fundamental understanding of the development of PEMFCs. Adjusting the CL porosity by using different solvent compositions of water and isopropyl alcohol and implementing an activation method for low platinum content catalysts, we focus on understanding the contributions of macro-porosity and hydrophobicity in a liquid electrolyte-based system. We show that macro-porosity significantly influences the O2 mass transport in the CL, demonstrating increased performance with higher porosities. Coupling inductively coupled plasma mass spectrometry (ICP-MS) with the GDE half-cell setup, we propose a method for qualitative estimation of water content in the CL. Lower macro-porosity shows higher platinum dissolution, attributed to improved mass transport of dissolved ions in aqueous media.

Spectral-based estimation of chlorophyll content and determination of background interference mechanisms in low-coverage rice

The chlorophyll content is a vital indicator of rice growth and nutritional status. However, estimating the rice chlorophyll content using spectral-based techniques at the early tillering stage is challenging because of background interference. Using the energy conservation principle, this study explained the spectral variation and background interference mechanisms of clear, muddy, and green algae-covered backgrounds. We developed mathematical interference models for the three types of backgrounds and determined their interference degree and influence mode. We developed rice chlorophyll content estimation models for unclassified and classified (clear, muddy, and green algae-covered) backgrounds using 12 preprocessing, four wavelength selection, and three modeling methods, and we explored the importance of background classification. Moreover, we found that the optimal chlorophyll content estimation model for the clear background was SS+UVE+CNN, with R2 and RMSE values of 0.786 and 13.191 in the training set and 0.741 and 15.327 in the test set, respectively; that for the muddy background was MSC+GA+CNN, with R2 and RMSE values of 0.914 and 10.425 in the training set and 0.660 and 16.844 in the test set, respectively; and that for the green algae-covered background was DC+GA+CNN, with R2 and RMSE values of 0.904 and 9.111 in the training set and 0.688 and 17.694 in the test set, respectively. Our study could provide valuable insights into reducing and correcting background interference during proximal remote sensing data collection.

Prediction interval soft sensor for dissolved oxygen content estimation in an electric arc furnace

In this study, a novel soft sensor modeling approach using Takagi–Sugeno (TS) fuzzy models and Prediction Intervals (PIs) is presented to quantify uncertainties in Electric Arc Furnace (EAF) steel production processes, namely to estimate the dissolved oxygen content in the steel bath. In real EAF operation, dissolved oxygen content is measured only a few times in the refining stage; therefore, the approach addresses the challenge of predicting unobserved output under conditions of irregular and scarce output measurements, using two distinct methods: Instant TS (I-TS) and Input Integration TS (II-TS). In the I-TS method, the model is computed for each individual indirect measurement, while the II-TS approach integrates these indirect measurements. The inclusion of PIs in TS models allows the derivation of the narrowest band containing a prescribed percentage of data, despite the presence of heteroscedastic noise. These PIs provide valuable insight into potential variability and allow decision-makers to evaluate worst-case scenarios. When evaluated against real EAF data, these methods were shown to effectively overcome the obstacles posed by scarce output measurements. Despite its simplicity, the I-TS model performed better in terms of interpretability and robustness to the operational reality of the EAF process. The II-TS model, on the other hand, showed excellent performance on all metrics but exhibited theoretical inconsistencies when deviating from typical operations. In addition, the proposed method successfully estimates carbon content in the steel bath using the established dissolved oxygen/carbon equilibrium, eliminating the need for direct carbon measurements. This shows the potential of the proposed methods to increase productivity and efficiency in the EAF steel industry.

Soil water content estimation by using ground penetrating radar data full waveform inversion with grey wolf optimizer algorithm

AbstractSoil water content (SWC) estimation is important for many areas including hydrology, agriculture, soil science, and environmental science. Ground penetrating radar (GPR) is a promising geophysical method for SWC estimation. However, at present, most of the studies are based on partial information of GPR, like travel time or amplitude information, to invert the SWC. Full waveform inversion (FWI) can use the information of the entire waveform, which can improve the accuracy of parameter estimation. This study proposes a novel SWC estimation scheme by using the FWI of GPR, optimized by the grey wolf optimizer (GWO) algorithm. The proposed scheme includes a petrophysical relationship to link the SWC with the relative dielectric permittivity, 1D GPR forward modeling, and a GWO optimization algorithm. First, numerical modeling was carried out, and the proposed scheme was applied to both noise‐free and noisy data to verify its applicability. Then, the proposed method was applied to data collected from a field experimental site. These results, derived from both synthetic and real datasets, show that the proposed inversion scheme resulted in a good match between the observed and calculated GPR data. In the numerical modeling, it was observed that the SWC could be inverted accurately, even when noise was present in the data. These demonstrate that the GWO method can be applied for the quantitative interpretation of GPR data. The proposed scheme shows potential for SWC estimation by using GPR full waveform data.

Hyperspectral Estimation of Chlorophyll Content in Wheat under CO2 Stress Based on Fractional Order Differentiation and Continuous Wavelet Transforms

The leaf chlorophyll content (LCC) of winter wheat, an important food crop widely grown worldwide, is a key indicator for assessing its growth and health status in response to CO2 stress. However, the remote sensing quantitative estimation of winter wheat LCC under CO2 stress conditions also faces challenges such as an unclear spectral sensitivity range, baseline drift, overlapping spectral peaks, and complex spectral response due to CO2 stress changes. To address these challenges, this study introduced the fractional order derivative (FOD) and continuous wavelet transform (CWT) techniques into the estimation of winter wheat LCC. Combined with the raw hyperspectral data, we deeply analyzed the spectral response characteristics of winter wheat LCC under CO2 stress. We proposed a stacking model including multiple linear regression (MLR), decision tree regression (DTR), random forest (RF), and adaptive boosting (AdaBoost) to filter the optimal combination from a large number of feature variables. We use a dual-band combination and vegetation index strategy to achieve the accurate estimation of LCC in winter wheat under CO2 stress. The results showed that (1) the FOD and CWT methods significantly improved the correlation between the raw spectral reflectance and LCC of winter wheat under CO2 stress. (2) The 1.2-order derivative dual-band index (RVI (R720, R522)) constructed by combining the sensitive spectral bands of the CO2 response of winter wheat leaves achieved a high-precision estimation of the LCC under CO2 stress conditions (R2 = 0.901). Meanwhile, the red-edged vegetation stress index (RVSI) constructed based on the CWT technique at specific scales also demonstrated good performance in LCC estimation (R2 = 0.880), verifying the effectiveness of the multi-scale analysis in revealing the mechanism of the CO2 impact on winter wheat. (3) By stacking the sensitive spectral features extracted by combining the FOD and CWT methods, we further improved the LCC estimation accuracy (R2 = 0.906). This study not only provides a scientific basis and technical support for the accurate estimation of LCC in winter wheat under CO2 stress but also provides new ideas and methods for coping with climate change, optimizing crop-growing conditions, and improving crop yield and quality in agricultural management. The proposed method is also of great reference value for estimating physiological parameters of other crops under similar environmental stresses.

Total organic carbon content estimation for mixed shale using Xgboost method and implication for shale oil exploration

In this study, the Xgboost method is employed for TOC estimation in mixed carbonate and siliciclastic shale from the Hashan area, Junggar Basin. The results show that this approach is effective for TOC estimation in this area although the model performance is not very excellent with a correlation coefficient of 0.54 between measured TOC and predicted TOC values, likely due to a small samples dataset. Therefore, the PCA method is applied to debase dimension of well log data from five dimensional to two-dimensional data, which enhances the correlation coefficient between the predicted and measured TOC from 0.54 to 0.68. Based on the model, the isopleth maps of TOC distributions in Fengcheng Formation were redrawn showing two shale oil exploration targets, which likely correspond to two depositional centers of this strata. All the same, the model in this work provides reliable data for shale oil evaluation in the study area and a good example under similar geological setting.

Pore structure of the mixed sedimentary reservoir of Permian Fengcheng Formation in the Hashan area, Junggar Basin

The Permian Fengcheng Formation (P1f.) in the Hashan area, situated on the southwestern margin of the Junggar Basin, has witnessed a remarkable breakthrough in shale oil exploration in recent years with nearly 789 million tons of shale oil resources. As a unique set of mixed sedimentary shales, the Fengcheng Formation in the Hashan area is characterized by mixed sedimentation of terrigenous siliciclastic sediments, authigenic minerals, and tuffaceous materials. However, the understanding of pore characteristics in the mixed sedimentary reservoir still remains limited, prohibiting accurate estimation of the oil content and insights into oil mobility. Scanning electron microscopy (SEM), X-ray diffraction (XRD), mercury injection capillary pressure (MICP), nuclear magnetic resonance (NMR), X-Ray Computer Tomography (X-CT), and geochemical analysis were performed to investigate the pore size distribution and main controlling factors of the mixed sedimentary reservoir. Results showed that the main pore types in the mixed sedimentary reservoir are intergranular pores and dissolution pores. The pores of the P1f. mixed shales in the Hashan area were classified into II-micropores (< 25 nm), I-micropores (25–100 nm), mesopores (100–1000 nm) and macropores (> 1000 nm). In general, the mixed sedimentary rocks of P1f. formation feature few macropores but a large number of micropores and mesopores. The CS exhibits the most favourable physical properties among all lithofacies. It is concluded that the abundance and maturity of organic matter, mineral composition, sedimentary structure, and diagenesis of reservoir together impact the pore structure in the mixed sedimentary reservoirs. The maturity of organic matter and the content of tuffaceous minerals are the most significant in influencing the pore structure of P1f. shales. Overall, the pore structure of complex lithologic reservoir formed by mixed deposition and its influence on physical properties are studied, and the characteristics of the microscopic pore-throat system of the dominant lithofacies in the Hashan area are clarified, which is of great significance as a guide for the exploration and development of mixed sedimentary reservoirs in continental shale oil in China.

Early detection of N, P, K deficiency in Choy Sum using hyperspectral imaging-based spatial spectral feature mining

Leafy vegetables are widely consumed around the world for their rich nutritional qualities. To ensure a reliable and cost-effective supply of leafy vegetables in the future, advancements in their production are essential. Deficiencies of nitrogen (N), phosphorus (P), and potassium (K) impair growth of leafy vegetables and the ensuing visual symptoms make the plants unmarketable. We studied the use of non-contact large area hyperspectral imaging (HSI) for early detection of N, P and K deficiencies in the leafy vegetable, Choy Sum, before the appearance of visual symptoms. The wide spectral data of 500–900 nm extracted from the plants were subjected to advanced feature mining, facilitating the creation of novel spectral indices tailored to each vital nutrient by leveraging the Pearson’s correlations of 0.85 for N, 0.64 for P, and 0.68 for K with gold standard elemental concentration data. Early detection of deficiencies and timely replenishment of macronutrient(s) can prevent the development of obvious symptoms and thus maintain the visual quality of Choy Sum. These newly created spectral indices hold the potential to provide non-destructive estimation of nutrient content in plants, offering a promising avenue for future advancements in precision agriculture and resource-efficient crop management.

Estimation of Soil Moisture during Different Growth Stages of Summer Maize under Various Water Conditions Using UAV Multispectral Data and Machine Learning

Accurate estimation of soil moisture content (SMC) is vital for effective farmland water management and informed irrigation decision-making. The utilization of unmanned aerial vehicle (UAV)-based remote sensing technology to monitor SMC offers advantages such as mobility, high timeliness, and high spatial resolution, thereby compensating for the limitations of in-situ observations and satellite remote sensing. However, previous research has primarily focused on SMC diagnostics for the entire crop growth period, often neglecting the development of targeted soil moisture modeling paradigms that account for the specific characteristics of the canopy and root zone at different growth stages. Furthermore, the variations in soil moisture status between fields, resulting from the hysteresis of water flow in irrigation channels at different levels, may influence the development of soil moisture modeling schemes, an area that has been seldom explored. In this study, SMC models based on UAV spectral information were constructed using Random Forest (RF) and Particle Swarm Optimization-Support Vector Machine (PSO-SVM) algorithms. The soil moisture modeling paradigms (i.e., input–output mapping) under different growth stages and soil moisture conditions of summer maize were systematically compared and discussed, along with the corresponding physical interpretability. Our results showed that (1) the SMC modeling schemes differ significantly across the various growth stages, with distinct input–output mappings recommended for the early (i.e., jointing, tasselling, and silking stages), middle (i.e., blister and milk stages), and late (i.e., maturing stage) periods. (2) these machine learning-based models performed best at the jointing stage, while subsequently, their accuracy generally exhibited a downward trend as the maize grew. (3) the RF model demonstrates superior robustness in estimating soil moisture status across different fields (moisture conditions), achieving optimal estimation accuracy in fields with overall higher SMC in line with the PSO-SVM model. (4) unlike the RF model’s robustness in spatial SMC diagnostics, the PSO-SVM model more reliably captured the temporal dynamics of SMC across different growth stages of summer maize. This study offers technical references for future modelers in UAV-based SMC modeling across various spatial and temporal conditions, addressing both the types of models as well as their input features.

The effect of antigorite dehydration on velocity structure and water migration in subduction zones

The water migration in subduction zones, primarily driven by the phase transition in hydrous minerals, can give rise to hydrated regions with reduced velocity. A fundamental element in comprehending and deciphering these low-velocity zones revolves around acquiring insights into the stability and elasticity of relevant hydrous minerals. As one of the main water carriers in shallow areas, antigorite can dehydrate to form talc, forsterite, and fluid (talc–bearing peridotites) in deep areas of subduction zones, and then the talc thus serves as one of the minerals that can bring water to the deep Earth. Here, the elasticity of talc up to 24 GPa and forsterite up to 12 GPa are calculated by using the first principles method. The result supposes that the talc structure transforming from talc I to talc II is at a pressure between 6 GPa and 8 GPa, impacting the trend of elastic wave velocity in response to pressure. Furthermore, the elastic wave velocity of forsterite can be significantly affected by iron concentration. Meanwhile, a variation velocity model with antigorite consumption and talc content is set up for talc-bearing serpentinized peridotite based on the elastic properties of talc and forsterite in this study, and antigorite in Wang et al. (2022). The results of our model demonstrate a decrease in the low-velocity anomaly in subduction zones, particularly in deep regions or areas with higher initial serpentinization degrees. The results also suggest that the mode of antigorite dehydration can diminish the estimation of water content transported to depths of subduction zones, such as the Mariana Trench and Northern Japan subduction zones. The mode of antigorite dehydration thus provides a useful tool for constraining the composition, seismic velocity structure, and water migration in subduction zones.

An unmanned ground vehicle phenotyping-based method to generate three-dimensional multispectral point clouds for deciphering spatial heterogeneity in plant traits

Fusing three-dimensional (3D) and multispectral (MS) imaging data holds promise for high-throughput and comprehensive plant phenotyping to decipher genome-to-phenome knowledge. Acquiring high-quality 3D MS point clouds (3DMPCs) of plants remains challenging because of poor 3D data quality and limited radiometric calibration methods for plants with a complex canopy structure. Here, we present a novel 3D spatial–spectral data fusion approach to collect high-quality 3DMPCs of plants by integrating the next-best-view planning for adaptive data acquisition and neural reference field (NeREF) for radiometric calibration. This approach was used to acquire 3DMPCs of perilla, tomato, and rapeseed plants with diverse plant architecture and leaf morphological features evaluated by the accuracy of chlorophyll content and equivalent water thickness (EWT) estimation. The results showed that the completeness of plant point clouds collected by this approach was improved by an average of 23.6% compared with the fixed viewpoints alone. The NeREF-based radiometric calibration with the hemispherical reference outperformed the conventional calibration method by reducing the root mean square error (RMSE) of 58.93% for extracted reflectance spectra. The RMSE for chlorophyll content and EWT predictions decreased by 21.25% and 14.13% using partial least squares regression with the generated 3DMPCs. Collectively, our study provides an effective and efficient way to collect high-quality 3DMPCs of plants under natural light conditions, which improves the accuracy and comprehensiveness of phenotyping plant morphological and physiological traits, and thus will facilitate plant biology and genetic studies as well as crop breeding.

Temperature and water content estimation in soils of the semi‐arid region of Brazil using finite difference and CFD

AbstractDetermining the temperature and water content of soil, at a given instant or along time, is fundamental to understand several soil‐related phenomena and processes. Evaporation, aeration, chemical‐reaction rates and types, biological processes such as germination and growth of seeds, root development, nutrient and water uptake by roots, and decomposition of organic matter by microbes, are all strongly influenced by soil temperature. On the other hand, infiltration of water through the soil surface allows soil to temporarily store water, making it available for uptake by plants and organisms living in soil. Furthermore, soil water content is closely related to physical and chemical properties of soil, such as oxygen content and demand, which impacts root breathing, microbial activity and soil chemical balance. The accurate evaluation of these two parameters and their interconnection is even relevant in semi‐arid regions, where climate conditions are particularly difficult, such as the north‐eastern zone of Brazil. Thus, the use of computational models and coupled approaches are imperative for rigorous descriptions. This work presents a contribution to estimate soil temperature and water content, by solving the heat transfer equation and the Richards equation, respectively, through finite differences. As input, the model uses the experimental material composition of the soil, the time‐dependent temperature profile at the surface and information about the regional rain regime. Three different numerical approaches were implemented: explicit, simple implicit and the Crank–Nicolson method. The calculations for temperature and water content of the soil obtained with these computational models were compared with the results from Computational Fluid Dynamics (CFD). The relative differences between the numerical methods were less than 0.006% by solving the heat transfer equation and less than 2.75% using the Richards equation. The maximum relative differences within the model, including both a constant and a variable water‐content profile, were 3.28%. The results from the computational model using the CFX tool have maximum relative differences of 0.6%, which contributes to verifying the accuracy of the implemented methods.

The Right Triangle Model: Overcoming the Sparse Data Problem in Thermal/Optical Remote Sensing of Soil Moisture

The simplicity of the so-called triangle method allows estimates of evapotranspiration and soil water content to be made without ancillary data external to the image and with just a few simple algebraic calculations. Drawing on many examples in the literature showing that the pixel distribution in temperature/fractional vegetation cover (NDVI) space closely resembles a right triangle, this paper shows that adoption of a right triangle shape further simplifies the triangle model. Moreover, it allows one to mostly avoid the problem of sparse or low-resolution data. A time dimension can be included showing that trajectories inside the triangle can provide additional information on root zone soil water content. After discussing some of the ambiguities in the triangle method, and the advantageous properties of the right triangle, a proposal is made to illuminate the relationship between thermal/optical measurements and root zone water content within the right triangle framework.

Reflectance spectroscopy analysis and lithium content estimation in lithium-rich rocks and stream sediments: Insights from Tuanjie Peak, Western Kunlun, China

Lithium, a rare metal of strategic importance, has garnered heightened global attention. This investigation delves into the laboratory visible-near infrared and short-wavelength infrared reflectance (VNIR-SWIR 350 nm–2500 nm) spectral properties of lithium-rich rocks and stream sediments, aiming to elucidate their quantitative relationship with lithium concentration. This research seeks to pave new avenues and furnish innovative technical solutions for probing sedimentary lithium reserves. Conducted in the Tuanjie Peak region of Western Kunlun, Xinjiang, China, this study analyzed 614 stream sediments and 222 rock specimens. Initial steps included laboratory VNIR-SWIR spectral reflectance measurements and lithium quantification. Following the preprocessing of spectral data via Savitzky-Golay (SG) smoothing and continuum removal (CR), the absorption positions (Pos2210nm, Pos1910nm) and depths (Depth2210, Depth1910) in the rock spectra, as well as the Illite Spectral Maturity (ISM) of the rock samples, were extracted. Employing both the Successive Projections Algorithm (SPA) and genetic algorithm (GA), wavelengths indicative of lithium content were identified. Integrating the lithium-sensitive wavelengths identified by these feature selection methods, A quantitative predictive regression model for lithium content in rock and stream sediments was developed using partial least squares regression (PLSR), support vector regression (SVR), and convolutional neural network (CNN). Spectral analysis indicated that lithium is predominantly found in montmorillonite and illite, with its content positively correlating with the spectral maturity of illite and closely related to Al-OH absorption depth (Depth2210) and clay content. The SPA algorithm was more effective than GA in extracting lithium-sensitive bands. The optimal regression model for quantitative prediction of lithium content in rock samples was SG-SPA-CNN, with a correlation coefficient prediction (Rp) of 0.924 and root-mean-square error prediction (RMSEP) of 0.112. The optimal model for the prediction of lithium content in stream sediment was SG-SPA-CNN, with an Rp and RMSEP of 0.881 and 0.296, respectively. The higher prediction accuracy for lithium content in rocks compared to sediments indicates that rocks are a more suitable medium for predicting lithium content. Compared to the PLSR and SVR models, the CNN model performs better in both sample types. Despite the limitations, this study highlights the effectiveness of hyperspectral technology in exploring the potential of clay-type lithium resources in the Tuanjie Peak area, offering new perspectives and approaches for further exploration.

Biological control of downy mildew Peronosclerospora spp. L. by application of endophytic agents in corn

ABSTRACT This research was carried out to evaluate endophytic biocontrol agents (eBCA) in inducing corn plant resistance to downy mildew Peronosclerospora spp. In this context, the estimation of total phenolic content and peroxidase (POD) enzyme activity was carried out in contrast to control without induction treatments. Corn seed treatment using three eBCA II.D2.2, DC.5, and AC.3 on susceptible and tolerant cultivars in a screenhouse and field showed variation in downy mildew reduction. The results showed that Trichoderma asperellum AC.3 treatment suppressed downy mildew by 2.7–23.0% in the screen house. In the field, resistance induction by applications of Bacillus cereus II.D2.2, Fusarium verticillioides DC.5, and T. asperellum AC.3 suppressed the infection from 23% to 79% on susceptible cultivars. However, the T. asperellum AC.3 treatment reported the highest suppression of 55–79% on tolerant and susceptible cultivars. Resistant induction with F. verticillioides DC.5 increased POD enzyme activity by 300.5% and suppressed the infection by 63% in the field. The pathogen suppression ability through the induction with eBCA was related to triggering the increase of total phenolic content and POD enzyme activity in plant tissues. Applications of B. cereus II.D2.2, F. verticillioides DC.5, and T. asperellum AC.3 suppressed downy mildew infection in the field through the induction of total phenolic content and POD enzyme activity in corn. Meanwhile, the best treatment using T. asperellum AC.3 was lower by 30–70% than the application of synthetic fungicide as control.

Estimation of soil organic matter content by combining Zhuhai-1 hyperspectral and Sentinel-2A multispectral images

Hyperspectral satellite imagery has significant advantages in rapidly monitoring soil organic matter (SOM) content over a large area. However, limitations in the timely acquisition of site-specific data may affect its effectiveness due to weather influences and revisit cycles. This paper proposes a novel method for estimating SOM content that combines the high temporal resolution of the Zhuhai-1 hyperspectral satellite image and Sentinel-2A multispectral satellite image to broaden the spectral range of Zhuhai-1 images. Multisource features, including spectral bands, topographic features, textural features, and spectral indexes, are extracted from the combined image and digital elevation model. An improved genetic algorithm (IGA) is proposed to optimize feature selection and extreme gradient boosting (XGBoost) is then applied to estimate SOM content. The proposed method was validated using 197 topsoil samples and satellite images collected from a demonstration area in Lishu County, Jilin Province, China. The results indicate that the estimation accuracy using the combined image was greater than using one single image. Compared with only using the Sentinel-2A and Zhuhai-1 images, the coefficient of determination (R2) values improved from 0.61 and 0.73 to 0.82, the ratio of the prediction to the deviation (RPD) values improved from 1.62 and 1.93 to 2.35, and the ratio of performance to the interquartile distance (RPIQ) values improved from 2.16 and 2.58 to 3.15, respectively. Furthermore, the XGBoost algorithm outperformed the random forest algorithm in terms of model accuracy and mapping reliability. The use of multisource features improved the R2 value from 0.45 to 0.82 compared to only using 31 spectral bands of Zhuhai-1 and Sentinel-2A image, with contribution rates in descending order of spectral indexes (48.2%), topographic features (24.7%), spectral bands (19.9%), and textural features (7.2%). This paper thus presents a promising method for efficient periodic mapping of the SOM content by combining data from a hyperspectral satellite constellation and a multispectral satellite image.

Rapid estimation of wood density and total extractives of two important Dalbergia species using near-infrared (NIR) spectroscopy and multivariate analysis

ABSTRACT Wood density and chemical extractives are important parameters influencing timber quality and utility in wood industry. Several wood properties, particularly mechanical parameters and durability, depend respectively on density and extractive contents. This study aimed to apply the near-infrared (NIR) spectroscopy technique for rapid and accurate estimation of density and extractive contents of Dalbergia latifolia and D. sissoo wood species. The objective was to develop a predictive model for properties, addressing the critical gap in efficient and large-scale assessment techniques for wood industries. Density and extractive contents were determined by conventional and wet chemistry methods. Spectral data of woods were subjected to multivariate analysis to improve the accuracy of calibration models and performance was assessed using coefficient of determination (R²) and root mean square error of prediction. The optimised models exhibited good predictive accuracy with high R²of 0.85 and 0.82 for density and extractive contents respectively. Spectral patterns correlating density and extractive variations identified provided valuable insights into the chemical composition and density relationship in Dalbergia species. The study thus highlights the potential of this non-invasive analytical approach in assessing the quality parameters, which would facilitate sustainable utilisation and contribute to the advancement of woodworking industries.

Study on Soil Moisture Status of Soybean and Corn across the Whole Growth Period Based on UAV Multimodal Remote Sensing

Accurate estimation of soil moisture content (SMC) in the field is a critical aspect of precise irrigation management. The development of unmanned aerial vehicle (UAV) platforms has provided an economically efficient means for field-scale SMC measurements. However, previous studies have mostly focused on single-sensor estimates of SMC. Additionally, the lack of differentiation between various crops and their growth stages has resulted in an unclear understanding of how crop types and growth stages affect the accuracy of SMC estimation at different soil depths. Therefore, the purpose of this paper was to use UAV multimodal remote sensing and a machine learning algorithm to estimate the SMC in agricultural fields and investigate estimation’s effectiveness under different scenarios. The results indicated the following: (1) The multispectral remote sensing method provided higher accuracy in SMC estimation compared to thermal infrared remote sensing. Moreover, the integration of multimodal data improved the accuracy of SMC estimation, enhancing the coefficient of determination (R2) by approximately 14% over that achieved through the use of multispectral data alone and 39% over that of thermal infrared data alone. (2) Across the entire growth period, the optimal soil depths of SMC estimation for soybean were 10 cm and 20 cm (average R2 were 0.81 and 0.82, respectively), while for corn, they were 10 cm, 20 cm, and 40 cm (average R2 were 0.59, 0.60, and 0.55, respectively). (3) The SMC estimation model performed better for both crops during the first three growth stages, with accuracy declining in the maturity stage. These results demonstrate that this approach can provide relatively accurate root zone SMC estimates for different crops throughout their main growth periods. Thus, it can be employed for SMC monitoring and precision irrigation system design.

CASCADE: Dataset of extant coccolithophore size, carbon content and global distribution

Coccolithophores are marine calcifying phytoplankton important to the carbon cycle and a model organism for studying diversity. Here, we present CASCADE (Coccolithophore Abundance, Size, Carbon And Distribution Estimates), a new global dataset for 139 extant coccolithophore taxonomic units. CASCADE includes a trait database (size and cellular organic and inorganic carbon contents) and taxonomic-unit-specific global spatiotemporal distributions (Latitude/Longitude/Depth/Month/Year) of coccolithophore abundance and organic and inorganic carbon stocks. CASCADE covers all ocean basins over the upper 275 meters, spans the years 1964-2019 and includes 33,119 gridded taxonomic-unit-specific abundance observations. Within CASCADE, we characterise the underlying uncertainties due to measurement errors by propagating error estimates between the different studies. This error propagation pipeline is statistically robust and could be applied to other plankton groups. CASCADE can contribute to (observational or modelling) studies that focus on coccolithophore distribution and diversity and the impacts of anthropogenic pressures on historical populations. Additionally, our new taxonomic-unit-specific cellular carbon content estimates provide essential conversions to quantify the role of coccolithophores on ecosystem functioning and global biogeochemistry.