Quantitative Inversion Research Articles

Monitoring Urban Change in Conflict from the Perspective of Optical and SAR Satellites: The Case of Mariupol, a City in the Conflict between RUS and UKR

Modern armed conflicts can cause serious humanitarian disasters, and remote sensing technology is critical in monitoring war crimes and assessing post-war damage. In this study, a constrained energy minimization algorithm incorporating the feature bands (IFB-CEM) is designed to detect urban burning areas in optical images. Due to the difficulty of obtaining the ground survey data of the battlefield, the dual-polarization normalized coherence index (DPNCI) is designed based on the multi-temporal synthetic aperture radar (SAR) image, and the quantitative inversion and evaluation of the destruction of urban architecture are combined with the public images on the Internet. The results show that the burning area is widely distributed in the armed conflict region, and the distribution is most concentrated around the Azovstal steel and iron works. The burning area reached its peak around 22 March, and its change is consistent with the conflict process in time and space. About 79.2% of the buildings in the city were severely damaged or completely destroyed, and there was a significant correlation with burning exposure. The results of this study show that publicly available medium-resolution remote sensing data and Internet information have the ability to respond quickly to the damage assessment of armed conflict and can provide preliminary reference information for dealing with humanitarian disasters.

Landsat 8 and Sentinel-2 Fused Dataset for High Spatial-Temporal Resolution Monitoring of Farmland in China’s Diverse Latitudes

Crop growth and development exhibit high temporal heterogeneity. It is crucial to capture the dynamic characteristics of crop growth using intensive time-series data. However, single satellites are limited by revisit cycles and weather conditions to provide dense time-series data for earth observations. However, up until now, there has been no proposed remote sensing fusion product that offers high spatial-temporal resolution specifically for farmland monitoring. Therefore, focusing on the demands of farmland remote sensing monitoring, identifying quantitative conversion relationships between multiple sensors, and providing high spatial-temporal resolution products is the first step that needs to be addressed. In this study, a fused Landsat 8 (L8) Operational Land Imager (OLI) and Sentinel-2 (S-2) multi-spectral instruments (MSI) data product for regional monitoring of farmland at high, mid, and low latitudes in China is proposed. Two image pairs for each study area covering different years were acquired from simultaneous transits of L8 OLI and S-2 MSI sensors. Then, the isolation forest (iForest) algorithm was employed to remove the anomalous pixels of image pairs and eliminate the influence of anomalous data on the conversion relationships. Subsequently, the adjustment coefficients for multi-source sensors at mixed latitudes with high spatial resolution were obtained using an ordinary least squares regression method. Finally, the L8-S-2 fused dataset based on the adjustment coefficients is proposed, which is suitable for different latitude farming areas in China. The results showed that the iForest algorithm could effectively improve the correlation between the corresponding spectral bands of the two sensors at a spatial resolution of 10 m. After the removal of anomalous pixels, excellent correlation and consistency were obtained in three study areas, and the Pearson correlation coefficients between the corresponding spectral bands almost all exceeded 0.88. Furthermore, we mixed the six image pairs of the three latitudes to obtain the adjustment coefficients derived for integrated L8 and S-2 data with high-spatial-resolution. The significance and accuracy quantification of the adjustment coefficients were thoroughly examined from three dimensions: qualitative and quantitative analyses, and spatial heterogeneity assessment. The obtained results were highly satisfactory, affirming the validity and precision of the adjustment coefficients. Finally, we applied the adjustment coefficients to crop monitoring in three latitudes. The normalized difference vegetation index (NDVI) time-series curves drawn by the integrated dataset could accurately describe the cropping system and capture the intensity changes of crop growth within the high, middle, and low latitudes of China. This study provides valuable insights into enhancing the application of multi-source remote sensing satellite data for long-term, continuous quantitative inversion of surface parameters and is of great significance for crop remote sensing monitoring.

PYRS: A PYTHON PACKAGE TO PROCESS REMOTELY SENSED DATA FOR GEOMATICS EDUCATION

Abstract. PyRS is a Python package developed for processing remotely sensed data. It provides a user-friendly interface to handle various remote sensing tasks, including data reading, radiometric correction, image enhancement, image denoising, image segmentation, land cover classification, change detection, construction of remote sensing indices, quantitative inversion. PyRS includes a wide range of state-of-the-art algorithms and tools for processing optical data. PyRS is an open-source project that is continually updated and maintained by a group of developers. It is designed to be modular and extensible, allowing users to add new algorithms and functionality to the package. PyRS is widely used in the remote sensing community and has been applied to various applications, such as land cover mapping, crop monitoring, and disaster response. PyRS is a comprehensive and powerful Python package for processing remotely sensed data. Its user-friendly interface, broad range of functionalities, and open-source nature make it an attractive tool for researchers and practitioners in the remote sensing field. PyRS encapsulates the current mainstream remote sensing algorithms, which can greatly improve the scientific research efficiency of researchers in the field of remote sensing. The form of Python packages and the characteristics of open source make it more flexible and transparent than professional remote sensing processing software. PyRS will contribute to the progress and development of geomatics education.

A Modular Method for GPR Hyperbolic Feature Detection and Quantitative Parameter Inversion of Underground Pipelines

Ground penetrating radar (GPR) is widely used to inspect underground pipelines because it is non-destructive. When the scan line of GPR is perpendicular to the pipe, it will exhibit hyperbolic features in GPR B-scan images, which have no intuitive relationship with the geometric and physical parameters of the pipeline, making the interpretation of GPR images difficult. This paper proposes a modular detection and quantitative inversion method for the hyperbolic features in GPR B-scan images, which is divided into two steps. In the first step, the YOLOv7 object detection network is used to automatically detect the hyperbolic features in GPR images. In the second step, a two-stage curve fitting method is proposed based on the characteristics of the detection model. It uses a few key point annotations of the hyperbolic pattern and some parameters of the GPR system to quantitatively invert the depth and radius of pipes. Using the same hardware and data set, YOLOv7 achieves an 11.1% improvement in detection accuracy and an 18.2% improvement in speed compared to YOLOv5. The relative errors of the proposed method for the depth and radius of the synthetic data in homogeneous media are 0.6% and 4.4%, respectively, and 4.8% and 15% in non-homogeneous media. The relative error of the depth inversion of the measured data TU1208 is less than 10%. The results show that the method can effectively invert the depth and radius of underground pipelines and reduce the difficulty of GPR data interpretation.

Quantitative Inversion Ability Analysis of Oil Film Thickness Using Bright Temperature Difference Based on Thermal Infrared Remote Sensing: A Ground-Based Simulation Experiment of Marine Oil Spill

Oil spills on the sea surface have caused serious harm to the marine ecological environment and coastal environment. Oil film thickness (OFT) is an important parameter for estimating oil spills amount, and accurate quantification of OFT is of great significance for rapid response and risk assessment of oil spills. In recent years, thermal infrared remote sensing has been gradually applied to quantify the OFT. In this paper, the outdoor oil spill simulation experiments were designed, and the bright temperature (BT) data of different OFTs were obtained for 24 consecutive hours in summer and autumn. On the basis of the correlation analysis of OFT and bright temperature difference (BTD) between oil and water, the traditional regression fitting model, classical machine learning model, ensemble learning model, and deep learning model were applied to the inversion of OFT. At the same time, inversion results of the four models were compared and analyzed. In addition, the best OFT inversion time using thermal infrared was studied based on 24-h thermal infrared data. Additionally, the inversion results were compared with the measured results; the optimal OFT range detectable using thermal infrared was explored. The experimental results show that: (1) Compared with ensemble learning model, traditional regression fitting model, and classical machine learning model, Convolutional Neural Network (CNN) has the advantages of high stability while maintaining high-precision inversion, and can be used as the preferred model for oil film thickness inversion; (2) The optimal time for OFT detection is around 10:00 to 13:00 of the day, and is not affected by seasonal changes; (3) During the day, thermal infrared has good detection ability for OFT greater than 0.4 mm, and weak detection ability for thinner oil films; (4) At night, thermal infrared has certain detection ability for relatively thick oil film, but the accuracy is lower than that in the daytime.

Modeling Soil CO2 Efflux in a Subtropical Forest by Combining Fused Remote Sensing Images with Linear Mixed Effect Models

Monitoring tropical and subtropical forest soil CO2 emission efflux (FSCO2) is crucial for understanding the global carbon cycle and terrestrial ecosystem respiration. In this study, we addressed the challenge of low spatiotemporal resolution in FSCO2 monitoring by combining data fusion and model methods to improve the accuracy of quantitative inversion. We used time series Landsat 8 LST and MODIS LST fusion images and a linear mixed effect model to estimate FSCO2 at watershed scale. Our results show that modeling without random factors, and the use of Fusion LST as the fixed predictor, resulted in 47% (marginal R2 = 0.47) of FSCO2 variability in the Monthly random effect model, while it only accounted for 19% of FSCO2 variability in the Daily random effect model and 7% in the Seasonally random effect model. However, the inclusion of random effects in the model’s parameterization improved the performance of both models. The Monthly random effect model that performed optimally had an explanation rate of 55.3% (conditional R2 = 0.55 and t value > 1.9) for FSCO2 variability and yielded the smallest deviation from observed FSCO2. Our study highlights the importance of incorporating random effects and using Fusion LST as a fixed predictor to improve the accuracy of FSCO2 monitoring in tropical and subtropical forests.

Chang’E-5 samples—New “ground truth” for the quantitative inversion of lunar remote sensing

<p indent="0mm">The Chang’E-5 (CE-5) mission successfully returned <sc>1731 g</sc> of lunar soil samples from the northeast of the Oceanus Procellarum. The newly returned lunar soil can provide a new ground truth for the quantitative inversion of lunar remote sensing data. In this study, the mineral and chemical compositions (FeO, TiO₂, Th) of the landing region were obtained from the remote sensing data and compared with the laboratory analysis results of the CE-5 lunar soil samples. The differences between them (especially the olivine content) indicate that there is still room for improvement in the methods for retrieving the elemental and mineral abundances using remote sensing data. Through a systematic comparative analysis, we discovered that: (1) the geochemical characteristics of high FeO, medium TiO₂, and abundant Th in the CE-5 lunar soil are significantly different from the existing Apollo lunar soil, which can provide unique ground truth for remote sensing research; (2) The CE-5 lunar soil represents mature lunar soil developed on the young basalt unit on the moon, with a small proportion of distantly sourced ejecta, representing the mare basalt composition characteristics of the landing region; (3) The CE-5 lunar soil is very fine, which meets the sample standard <sc>(&lt;45 μm)</sc> of the Lunar Soil Characterization Consortium for providing ground truth. Notably, the CE-5 landing region also exhibits unique characteristics: (1) it is located in the mid-latitude region on the western hemisphere of the moon, different from the low-latitude region where the United States and the Soviet Union sample return missions landed; (2) it is located on the late-stage lunar basalt unit; it is different from the existing spectral calibration area. Therefore, the construction of the CE-5 landing area as a new remote sensing calibration site based on the measured data of its lunar soil can significantly expand the geographical scope and global representation of the lunar calibration sites. Based on the results of the remote sensing and sample studies, we delineate the calibration area in the unit where the CE-5 landing site is located (Em4), thus providing a reference for establishing a remote sensing calibration area in the future.

A Novel Algorithm of Haze Identification Based on FY3D/MERSI-II Remote Sensing Data

Since 2013, frequent haze pollution events in China have been attracting public attention, generating a demand to identify the haze areas using satellite observations. Many studies of haze recognition algorithms are based on observations from space-borne imagers, such as the Moderate Resolution Imaging Spectroradiometer (MODIS), the Visible Infrared Imaging Radiometer Suite (VIIRS) and the Advanced Himawari Imager (AHI). Since the haze pixels are frequently misidentified as clouds in the official cloud detection products, these algorithms mainly focus on recovering them from clouds. There are just a few studies that provide a more precise distinction between haze and clear pixels. The Medium Resolution Imaging Spectrometer II (MERSI-II), the imager aboard the FY-3D satellite, has similar bands to those of MODIS, hence, it appears to have equivalent application potential. This study proposes a novel MERSI haze mask (MHAM) algorithm to directly categorize haze pixels in addition to cloudy and clear ones. This algorithm is based on the fact that cloudy and clear pixels exhibit opposing visible channel reflectance and infrared channel brightness temperature characteristics, and clear pixels are relative brighter, and as well as this, there is a positive difference between their apparent reflectance values, at 0.865 μm and 1.64 μm, respectively, over bright surfaces. Compared with the Aqua/MODIS and MERSI-II official cloud detection products, these two datasets treat the dense aerosol loadings as certain clouds, possible clouds and possible clear pixels, and they treat distinguished light or moderate haze as possible clouds, possible clear pixels and certainly clear pixels, while the novel algorithm is capable of demonstrating the haze region’s boundary in a manner that is more substantially consistent with the true color image. Using the PM2.5 (particle matter with a diameter that is less than 2.5 μm) data monitored by the national air quality monitoring stations as the test source, the results indicated that when the ground-based PM2.5 ≥ 35 μg/cm3 is considered to be haze days, the samples with the recognition rate that is higher than 85% accounted for 72.22% of the total samples. When PM2.5 ≥ 50 μg/cm3 is considered as haze days, 83.33% of the samples had an identification rate that was higher than 85%. A cross-comparison with similar research methods showed that the method proposed in this study had better sensitivity to bright surface clear and haze areas. This study will provide a haze mask for subsequent quantitative inversion of aerosol characteristics, and it will further exert the application benefits of MERSI-II instrument aboard on FY3D satellite.

HY-1C/D CZI Image Atmospheric Correction and Quantifying Suspended Particulate Matter

HY-1C/D both carry a coastal zone imager (CZI) with a spatial resolution of 50 m and a swath width of 950 km, two observations can be achieved in three days when two satellites operating in a network. Accurate atmospheric correction is the basis for quantitative inversion of ocean color parameters using CZI However, atmospheric correction in estuarine and coastal waters with complex optical properties is a challenge due to the band setting of CZI. This paper proposed a novel atmospheric correction algorithm for CZI images applicable to turbid waters in estuarine and coastal zone. The Rayleigh scattering reflectance of CZI was calculated based on a vector radiative transfer model. Next, a semi-empirical radiative transfer model with suspended particle concentration as the parameter is used to model the water-atmosphere coupling. Finally, the parameters of the coupling model are solved by combining a global optimization method based on a genetic algorithm. The results indicate that the CZI-derived remote-sensing reflectance (Rrs) are in good agreement with the quasi-synchronous Landsat-8/9 operational land imager (OLI) derived Rrs in the green and red bands (R2 &gt; 0.96). Validation using in situ data revealed that the RMSE of the CZI-derived Rrs in the green and red bands was 0.0036 sr−1 and 0.0035 sr−1. More importantly, the values and spatial distributions of suspended particulate matter (SPM) estimated by CZI and those estimated by OLI in the Subei Shoal and the Yangtze River Estuary are basically consistent, and the validation using in situ data revealed that the inversion of SPM concentration by CZI was effective (R2 = 0.86, RMSE = 0.0362 g/L), indicating that CZI has great potential and broad application prospects for monitoring the spatial and temporal dynamics of SPM in estuarine and coastal waters. The study results will lay the foundation for further estimating suspended sediment fluxes and carbon fluxes, thus providing data support and scientific basis for promoting resource development, utilization and conservation strategies in estuarine and coastal areas.

Research on quantitative inversion of ion adsorption type rare earth ore based on convolutional neural network

Rare earth resource is a national strategic resource, which plays an essential role in the field of high technology research and development. In this paper, we aim to use remote sensing quantitative inversion prospecting technology, use surface-to-surface mode, and model inversion and evaluation through convolutional neural network model to achieve a new research method for large-scale, low-cost, rapid and efficient exploration of ion-adsorbed rare earth ore. The results show that the RE2O3 content of samples has significant negative correlation with the second, third and fourth band of GF-2 image, but has no significant correlation with the first band of GF-2 image; the convolution neural network model can be used to reconstruct the RE2O3 content. The content distribution map of RE2O3 obtained by inversion is similar to that of geochemical map, which indicates that the convolution neural network model can be used to invert the RE2O3 content in the sampling area. The quantitative inversion results show that the content distribution characteristics of ion adsorption rare earth ore in the study area are basically consistent with the actual situation; there are two main high anomaly areas in the study area. The high anomaly area I is a known mining area, and the high anomaly area II can be a prospective area of ion adsorption type rare earth deposit. It shows that the remote sensing quantitative inversion prospecting method of ion adsorption type rare earth deposit based on Convolutional Neural Networks (CNN) model is feasible.

Inspection and Imaging of Tree Trunk Defects Using GPR Multifrequency Full-Waveform Dual-Parameter Inversion

Ground-penetrating radar (GPR) has been regarded as a potentially efficient way of evaluating the growth status of trees and preventing deterioration associated with trunk defects. However, the majority of current GPR data inversions focused on imaging the macroscale location of defects. As the first attempt to seek a preferable quantitative inversion methodology for specifying tree protection and remedies, this paper proposes a full-waveform inversion approach involving dual-parameter attributes applied to common-offset GPR data from a commercial antenna. Specifically, the synchronous inversion of both dielectric constant and conductivity improves the identification accuracy of certain defect types. In particular, both a multi-frequency strategy and total-variation regularization are seamlessly introduced to assure inversion stability by overcoming local minima and cycle skipping. Through an irregular trunk model test, the effectiveness of the optimized inversion is initially verified by presenting the precise features of the crack, hollow, and decay with the dual parameter inversion results. In addition, several other synthetic trunk models and in-site trunk model tests further demonstrate the robustness and practicability of the proposed algorithm, which can offer more specific and comprehensive guidance for the formulation of tree protection and restoration measures.

Soil Organic Matter Inversion Based on Imaging Spectral Data in Straw‐Covered Noncultivated Land

The rapid inversion of soil organic matter is of great significance for agricultural soil testing and fertilization in order to protect and utilize land resources effectively. This study selected the developmental base in Lishu County with typical characteristics of China’s black soil as its research area. This study established a 600 nm “bow curvature difference” spectral index and the partial least squares regression model, and the accuracy of their results was compared. The correlation between the 600 nm “bow curvature” spectral index and the soil organic matter of straw cover no‐tillage is analyzed. The average soil organic matter content in the study area was 16.67 g·kg-1, and the organic matter increased significantly from NT‐0 to NT‐100 by 16.26 g·kg-1. The study provided a deep insight to improve the quantitative inversion methods to estimate soil organic matter.

Quantitative Diffraction Tomography for Weak Scatterers Based on Aliasing Modification of the Multifrequency Spatial Spectrum

Diffraction tomography (DT) is a linear approach to solving electromagnetic inverse scattering problems. Based on the weak scattering assumptions (such as Born or Rytov approximations), the spatial spectrum of the contrast at one certain frequency is a linear mapping of the scattered field data. As is well known, using more frequencies means better performance of noise suppression. However, the spatial spectra are aliased in cases of multi-frequency data. In addition, the permittivity and the conductivity are coupled in the aliased multi-frequency spatial spectrum. In this paper, a quantitative DT for weak scatterers is proposed by firstly doing a coordinate transformation, then decoupling the permittivity and the conductivity, and finally modifying the aliased multi-frequency spatial spectrum. In doing so, the modified spatial spectra of the permittivity and conductivity are formulated respectively, leading to a quantitative diffraction tomography for weak scatterers based on aliasing modification of the multi-frequency spectrum. Inversion results with synthetic and experimental data demonstrate that the proposed method outperforms the conventional DT approach in terms of quantitative inversion accuracy for weak scatterers of multi-frequency data while maintaining the anti-noise performance.

Retrieving chlorophyll content and equivalent water thickness of Moso bamboo (Phyllostachys pubescens) forests under Pantana phyllostachysae Chao-induced stress from Sentinel-2A/B images in a multiple LUTs-based PROSAIL framework

Biochemical components of Moso bamboo (Phyllostachys pubescens) are critical to physiological and ecological processes and play an important role in the material and energy cycles of the ecosystem. The coupled PROSPECT with SAIL (PROSAIL) radiative transfer model is widely used for vegetation biochemical component content inversion. However, the presence of leaf-eating pests, such as Pantana phyllostachysae Chao (PPC), weakens the performance of the model for estimating biochemical components of Moso bamboo and thus must be considered. Therefore, this study considered pest-induced stress signals associated with Sentinel-2A/B images and field data and established multiple sets of biochemical canopy reflectance look-up tables (LUTs) based on the PROSAIL framework by setting different parameter ranges according to infestation levels. Quantitative inversions of leaf area index (LAI), leaf chlorophyll content (LCC), and leaf equivalent water thickness (LEWT) were derived. The scale conversions from LCC to canopy chlorophyll content (CCC) and LEWT to canopy equivalent water thickness (CEWT) were calculated. The results showed that LAI, CCC, and CEWT were inversely related with PPC-induced stress. When applying multiple LUTs, the p-values were <0.01; the R2 values for LAI, CCC, and CEWT were 0.71, 0.68, and 0.65 with root mean square error (RMSE) (normalized RMSE, NRMSE) values of 0.38 (0.16), 17.56 μg·cm‒2 (0.20), and 0.02 cm (0.51), respectively. Compared to the values obtained for the traditional PROSAIL model, for October, R2 values increased by 0.05 and 0.10 and NRMSE decreased by 0.09 and 0.02 for CCC and CEWT, respectively and RMSE decreased by 0.35 μg·cm‒2 for CCC. The feasibility of the inverse strategy for integrating pest-induced stress factors into the PROSAIL model, while establishing multiple LUTs under different pest-induced damage levels, was successfully demonstrated and can potentially enhance future vegetation parameter inversion and monitoring of bamboo forest health and ecosystems.

Intelligent Analysis Cloud Platform for Soil Moisture-Nutrients-Salinity Content Based on Quantitative Remote Sensing

Quickly obtaining accurate soil quality information is the premise for accurate agricultural production and increased crop yield. With the development of the digital information industry, smart agriculture has become a new trend in agricultural development and there is increasing demand for efficiently and intelligently acquiring good soil quality information. Scientists worldwide have developed many remote sensing quantitative inversion models, which need to be systematized and intelligent for agricultural personnel to enjoy the dividends of information technology such as 3S (remote sensing, geographic information system, and global navigation satellite system) techniques. Accordingly, to meet the need of farmers, agricultural managers, and agricultural researchers to acquire timely information on regional soil quality, in this paper, we designed a cloud platform for inversion analysis of moisture, nutrient, salinity, and other important soil quality indicators. The platform was developed using ArcGIS (The software is produced by the Environmental Systems Research Institute, Inc. of America in Redlands, CL, USA) and GeoScene (The software is produced by GeoScene Information Technology Co.,Ltd., Beijing, China) software, with Java and JavaScript as programing languages and SQL Server as the database management system with a PC client, a web client, and a mobile app. On the basis of the existing quantitative remote sensing models, the platform realizes mapping functions, intelligent inversion of soil moisture–nutrient–salinity (SMNS) content, data analysis mining, soil knowledge base, platform management, and so on. It can help different users acquire, manage, and analyze data and make decisions based on the data. In addition, the platform can customize model parameters according to regional characteristics, improving analysis accuracy and expanding the application area. Overall, the platform employs 3S techniques, Internet technology, and mobile communication technology synthetically and realizes intelligent inversion and decision analysis of significant soil quality information, such as moisture–nutrient–salinity content. This platform has been applied to the analysis of soil indicators in several areas and has produced good operational results and benefits. This study will enable rapid data analysis and provide technical support for regional agriculture production, contributing to the development of smart agriculture.

Identification interface stiffness of coating/substrate structure based on ultrasonic pressure reflection coefficient amplitude spectrum

Due to the variation of interface stiffness, interface morphology, and testing frequency, coating/substrate interface stiffness identified through ultrasonic methods are usually non-unique and inaccuracy. This paper, a method for quantitative inversion interface stiffness of coating/substrate structure is proposed based on a constructed ultrasonic pressure reflection coefficient amplitude spectrum (URCAS). The URCAS of coupling medium/non-metallic coating/metal substrate structure is derived based on interfacial spring model. The high-sensitivity frequency and interface stiffness ranges are determined by analyzing the sensitivity of URCAS to testing frequency and interface stiffness in advance. The accurate inversion of interface stiffness is achieved through a least square objective function under the high-sensitivity frequency and interface stiffness ranges. The feasibility of proposed ultrasonic method was verified by numerical simulations and ultrasonic experiments implemented on epoxy resin coating/aluminum substrate specimens. Simulation results show that the inverted interface stiffnesses are in good agreement with the preset values, and the maximum relative error is less than 6% in the high-sensitivity interface stiffness range, while the inversion accuracy of interface stiffness significantly reduces, and the relative error exceeds 21% in the low-sensitivity range. Experimental results show that the tested tensile strength and interface stiffness have similar trends. The proposed ultrasonic method can quantitatively identify the interface stiffness and effectively characterize the bonding quality of coating/substrate interface.

Inhibitory control and visuospatial working memory contribute to 5-year-old children's use of quantitative inversion

This study examined the hypothesis that general cognitive resources moderated 5-year-old children's performance differences between the Concrete Identical and the Pure Quantity conditions on inversion problems (a + b – b) but not on standard problems (a + b – c). Study 1 (N = 104) showed that children who experienced higher visuospatial working memory burden performed significantly poorer in solving the inversion problems in the Pure Quantity condition than in the Concrete Identical condition, whereas those who experienced lower working memory burden showed no such difference. Study 2 (N = 194) demonstrated that children with lower levels of inhibitory control solved significantly fewer inversion problems in the Pure Quantity condition than in the Concrete Identical condition, whereas no such difference was found in children with higher levels of inhibitory control. These findings suggest that inhibitory control and visuospatial working memory may support children's use of quantitative inversion.

Quantitative Inversion of Lunar Surface Chemistry Based on Hyperspectral Feature Bands and Extremely Randomized Trees Algorithm

In situ resource utilization (ISRU) is required for the operation of both medium and long-term exploration missions to provide metallic materials for the construction of lunar base infrastructure and H2O and O2 for life support. The study of the distribution of the lunar surface elements (Fe, Ti, Al, and Si) is the basis for the in situ utilization of mineral resources. With the arrival of the era of big data, the application of big data concepts and technical methods to lunar surface chemistry inversion has become an inevitable trend. This paper is guided by big data theory, and the Apollo 17 region and the area near the Copernicus crater are selected for analysis. The dimensionality of the first-order differential spectral features of lunar soil samples is reduced based on Pearson correlation analysis and the successive projections algorithm (SPA), and the extremely randomized trees (Extra-Trees) algorithm is applied to Chang’E-1 Interference Imaging Spectrometer (IIM) data to establish a prediction model for the lunar surface chemistry and generate FeO, TiO2, Al2O3, and SiO2 distribution maps. The results show that the optimum number of variables for FeO, TiO2, Al2O3, and SiO2 is 17, 5, 8, and 30, respectively. The accuracy of the Extra-Trees model using the best variables was improved over that of the original band model, with determination coefficients (R2) of 0.962, 0.944, 0.964, and 0.860 for FeO, TiO2, Al2O3, and SiO2, and root mean square errors (RMSEs) of 1.028, 0.672, 0.942, and 0.897, respectively. The modeling feature variables and model preference methods in this study can improve the inversion accuracy of chemical abundance to some extent, demonstrating the potential of IIM data in predicting chemical abundance and providing a good data basis for lunar geological evolution studies and ISRU.

Near-Infrared Model and Its Robustness as Affected by Fruit Origin for ‘Dangshan’ Pear Soluble Solids Content and pH Measurement

Soluble solid content (SSC) and acidity (pH) are two important factors indicating the fruit quality of pears and can be measured by near-infrared spectroscopy (NIRS). However, the robustness of these measurements as affected by different origins of pears remains largely unknown. In this study, we developed an NIRS method to measure ‘Dangshan’ pear (Pyrus spp.) SSC and pH and evaluated the robustness of this non-destructive detection method by examining the effects of pears from three different origins in 2019 and 2020. First, the Kennard–Stone method was used to divide the calibration set of the 2020 pear samples from different orchards. The partial least squares (PLS) model was used to establish the local origin and hybrid origin models to predict the pears’ SSC and pH. Second, a combination of competitive adaptive reweighted sampling (CARS), successive projections algorithm (SPA), and uninformative variable elimination (UVE) was implemented to construct spectral prediction models based on effective variables for assessing the pears’ SSC and pH from local and hybrid origins. The results showed that the local origin detection model produced large errors in predicting the SSC and pH of pears from different origins, and the model, established based on the pear samples of three origins, performed better than the local origin and other hybrid origin models. Finally, the model could be effectively simplified using 70 and 52 characteristic variables selected by the CARS method. Pear samples harvested from three different orchards in 2019 were used as an independent set to verify the validity of the selected characteristic variables. The results showed that the predicted R2p for the SSC and pH measurements of pears of three different origins were more than 0.9 and 0.85, respectively. This finding indicates that the difference in the origin of pears has an important influence on the quantitative inversion of pear SSC and pH measurements, and the combination of the hybrid origin model constructed based on the characteristic variables can improve the prediction accuracy. These findings provide an important theoretical basis for the development of rapid detection devices for the measurements of pears’ SSC and pH.

Soil heavy metal Pb concentration quantitative inversion method based on hyperspectral remote sensing

Taking a mining area in the Liangshan section of the Anning River Basin as the research area, soil samples were collected, and the collected soil samples were respectively measured for Pb concentration and soil spectral reflectance, and the correlation between soil Pb concentration and soil spectral reflectance was analyzed, using the partial least squares regression method to construct the soil Pb concentration estimation model and carry out the accuracy test. The results show that: (1) The correlation between the original soil spectral curve and the measured value of soil Pb concentration is greatly improved after the first-order differential(FD), second-order differential(SD), reciprocal first-order differential(RTFD) and inverse second-order differential(RTSD) transformation, and there are many characteristic bands. (2) The best model for estimating soil Pb concentration is the model established by second-order differential, whose coefficient of determination (R2) is 0.56 and root mean square error (RMSE) is 1.28. It provides a reference for the retrieval of remote sensing hyperspectral soil Pb concentration in this area.