Inversion Model Research Articles

Estimation of Urban Tree Chlorophyll Content and Leaf Area Index Using Sentinel-2 Images and 3D Radiative Transfer Model Inversion

Urban trees play an important role in mitigating effects of climate change and provide essential ecosystem services. However, the urban environment can stress trees, requiring the use of effective monitoring methods to assess their health and functionality. The objective of this study, which focused on four deciduous tree species in Rennes, France, was to evaluate the ability of hybrid inversion models to estimate leaf chlorophyll content (LCC), leaf area index (LAI), and canopy chlorophyll content (CCC) of urban trees using eight Sentinel-2 (S2) images acquired in 2021. Simulations were performed using the 3D radiative transfer model DART, and the hybrid inversion models were developed using machine-learning regression algorithms (random forest (RF) and gaussian process regression). Model performance was assessed using in situ measurements, and relations between satellite data and in situ measurements were investigated using spatial allocation (SA) methods at the pixel and tree scales. The influence of including environment features (EFs) as model inputs was also assessed. The results indicated that random forest models that included EFs and used the pixel-scale SA method were the most accurate with R2 values of 0.33, 0.29, and 0.46 for LCC, LAI, and CCC, respectively, with notable variability among species.

Deep Learning Transient Electromagnetic Inversion for Seawater Intrusion

Abstract To enhance the capability of transient electromagnetic method(TEM) in detecting seawater intrusion and delineating the boundaries in coastal areas, we developed a deep learning inversion method for TEM data based on the Swin Transformer model in this study. First many standardized resistivity models were designed and generated to describe t the subsurface resistivity structures associated with seawater intrusion in coastal areas .Then, TEM forward modelling was performed to compute the corresponding TEM responses, thereby constructing a seawater intrusion-oriented training dataset. Then, the robust Swin Transformer model was employed as the backbone network to build a deep learning inversion model, named SITEMNet, to derive a direct nonlinear transformation that maps TEM responses to subsurface resistivity models. The proposed SITEMNet inversion technique was validated using simulated data scenarios and actual field TEM measurements, showing great promise in accurately identifying seawater intrusion interface and geological formations.

Evaluating ICESat-2 and GEDI with Integrated Landsat-8 and PALSAR-2 for Mapping Tropical Forest Canopy Height

Mapping forest canopy height is critical for climate modeling and forest management, and tropical forests present unique challenges for remote sensing due to their dense vegetation and complex structure. The advent of ICESat-2 and GEDI, two advanced lidar datasets, offers new opportunities for improving canopy height estimation. In this study, we used footprint-level canopy height products from ICESat-2 and GEDI, combined with features extracted from Landsat-8, PALSAR-2, and FABDEM products. The AutoGluon stacking ensemble learning algorithm was employed to construct inversion models, generating 30 m resolution continuous canopy height maps for the tropical forests of Puerto Rico. Accuracy validation was performed using the high-resolution G-LiHT airborne lidar products. Results show that tropical forest canopy height inversion remains challenging, with all models yielding relative root mean square errors (rRMSE) exceeding 0.30. The stacking ensemble model outperformed all base learners, and the GEDI-based map had slightly higher accuracy than the ICESat-2-based map, with RMSE values of 4.81 and 4.99 m, respectively. Both models showed systematic biases, but the GEDI-based model exhibited less underestimation for taller canopies, making it more suitable for biomass estimation. The proposed approach can be applied to other forest ecosystems, enabling fine-resolution canopy height mapping and enhancing forest conservation efforts.

Model Optimization and Application of Straw Mulch Quantity Using Remote Sensing

Straw mulch quantity is an important indicator in the detection of straw returned to the field in conservation tillage, but there is a lack of large-scale automated measurement methods. In this study, we estimated global straw mulch quantity and completed the detection of straw returned to the field. We used an unmanned aerial vehicle (UAV) carrying a multispectral camera to acquire remote sensing images of straw in the field. First, the spectral index was selected using the Elastic-net (ENET) algorithm. Then, we used the Genetic Algorithm Hybrid Particle Swarm Optimization (GA-HPSO) algorithm, which embeds crossover and mutation operators from the Genetic Algorithm (GA) into the improved Particle Swarm Optimization (PSO) algorithm to solve the problem of machine learning model prediction performance being greatly affected by parameters. Finally, we used the Monte Carlo method to achieve a global estimation of straw mulch quantity and complete the rapid detection of field plots. The results indicate that the inversion model optimized using the GA-HPSO algorithm performed the best, with the coefficient of determination (R2) reaching 0.75 and the root mean square error (RMSE) only being 0.044. At the same time, the Monte Carlo estimation method achieved an average accuracy of 88.69% for the estimation of global straw mulch quantity, which was effective and applicable in the detection of global mulch quantity. This study provides a scientific reference for the detection of straw mulch quantity in conservation tillage and also provides a reliable model inversion estimation method for the estimation of straw mulch quantity in other crops.

Comparison of interfacial injected energy between simulated lightning return stroke experiment and common Joule heat & Arc heat model application

A previous study has shown that the thermal damage of the Joule thermal arc heat transfer model is lighter than that of a natural lightning strike. Therefore, this paper focuses on the return stroke current and proposes an improved experimental method of simulated return damage without using arc-inducing wire. Combining the data with the inversion model of injected energy, the energy transfer characteristics of the samples are characterized. Furthermore, a data dimensionality reduction method based on multiple correlation coefficients is used to discuss the impact of the current peak/rise rate/wave tail time on the injected energy discrepancy. The results indicate a positive correlation between the current peak and current rise rate with the injected energy discrepancy. When the tail time exceeds 15 microseconds, the injected energy discrepancy decreases as the tail time increases. The thermal source characteristics of energy transfer during the return stroke process are determined. During the initial phase of the return stroke current, interfacial energy transfer includes contributions from ion enthalpy flux, Joule heating, and electronic enthalpy flux. When the tail time exceeds 15 microseconds, the contribution of ion enthalpy flux to the injected energy diminishes with increasing tail time.

Transient electromagnetic data denoising based on cluster analysis and locally weighted linear regression

AbstractIn transient electromagnetic surveys, the collected data inevitably contain noise originating from both natural and cultural sources. This noise has the potential to mask transient electromagnetic responses linked to geological features, thereby posing challenges in accurately interpreting subsurface structures. Hence, the implementation of effective noise reduction techniques is crucial in ensuring the accuracy and reliability of inversion outcomes in transient electromagnetic surveys. This study introduces a novel approach that merges k‐means clustering with locally weighted linear regression to denoise transient electromagnetic data. The results from synthetic examples illustrate that the k‐means locally weighted linear regression method can predict transient electromagnetic data closely resembling true values, similar to the long short‐term memory autoencoder. Occam's inversion results derived from denoised data using both the k‐means locally weighted linear regression and long short‐term memory–autoencoder methods can well reflect the true model. Notably, a key advantage of the k‐means locally weighted linear regression method is its independence from labelled data as the sample set. The k‐means locally weighted linear regression method was applied to field data collected at the Narenbaolige coalfield in Inner Mongolia, China. Occam's inversion models generated from the denoised field data delineate the boundary between the basaltic body and sedimentary rocks, aligning with drilling data. The inversion models derived from the noisy field data also can capture this boundary, but deep section views reveal the presence of numerous intricate high‐resistivity anomalous bodies. These observations highlight the effectiveness of the k‐means locally weighted linear regression method in denoising transient electromagnetic data.

Soil Salinity Inversion Based on a Stacking Integrated Learning Algorithm

Soil salinization is an essential risk factor for agricultural development and food security, and obtaining regional soil salinity information more reliably remains a priority problem to be solved. To improve the accuracy of soil salinity inversion, this study focuses on the Manas River Basin oasis area, the largest oasis farming area in Xinjiang, as the study area and proposes a new soil salinity inversion model based on stacked integrated learning algorithms. Firstly, we selected four machine learning regression models, namely, random forest (RF), back propagation neural network, support vector regression, and convolutional neural network, for performance evaluation. Based on the model performance, we selected the more effective RF and BPNN as the basic regression models and further constructed a stacking integrated learning model. This stacking integration learning model improved the prediction accuracy by training a secondary model to fuse the prediction results of these two basic models as new features. We compared and analyzed the stacking integrated learning model with four single machine learning regression models. Findings indicated that the stacking integrated learning regression model fitted better and had good stability; on the test set, the stacking integrated learning regression model showed a relative increase of 8.2% in R2, a relative decrease of 14.0% in RMSE, and a relative increase of 6.5% in RPD when compared to the RF model, which was the single most effective machine learning regression model, and the stacking model was able to achieve soil salinity inversion more accurately. The soil salinity in the oasis areas of the Manas River Basin tended to decrease from north to south from 2016 to 2020 from a spatial point of view, and it was reduced in April from a temporal point of view. The percentage of pixels with a high soil salinity content of 2.75–2.80 g kg−1 in the study area had decreased by 19.6% in April 2020 compared to April 2016. The innovatively constructed stacking integrated learning regression model improved the accuracy of soil salinity estimation on the basis of the superior results obtained in the training of the single optimal machine learning regression model. As a consequence, this model can provide technological backup for fast monitoring and inversion of soil salinity as well as prevention and containment of salinization.

Use of three-dimensional crossgradient joint inversion of marine controlled-source electromagnetic (CSEM) and seismic refraction data for overburden geohazards mapping.

Offshore energy-transition activities typically require the construction of new subsurface facilities where overburden heterogeneity and deformation pattern may not be fully understood. Anisotropic marine controlled-source electromagnetic (CSEM) and seismic refraction imaging can be adapted to geohazards assessment of the overburden to mitigate safety and costs risks in such areas. This is investigated using an area in deepwater NW Borneo as the type-example. For this proof-of-concept study, a well-established Multiphysics joint inversion code was extended to handle marine seismic refraction data from conventional towed streamers. Realistic field-scale synthetic models with anomalous resistivities and velocities in the shallow section (∼50-200 m below mudline) were built and their CSEM and seismic refraction responses were first inverted separately and then jointly in 3D using the crossgradient method. The joint inversion recovered the anomalous bodies better than the separate inversions. The velocity model from the joint inversion showed improvement from the separate inversion but required a good starting model. As a further test of the methodology, first-break travel times were picked from legacy 3D seismic data and inverted with the available sparse CSEM data. For model verification, resistivity and velocity profiles from separate and joint inversion models were extracted at a well location in the survey area and compared with actual well logs. The joint inversion models matched the well logs better than the separate inversion models. Moreover, known geohazards (complex fault-sets and lateral-inflows) from independent geomatic study were imaged in the joint inversion models. Also, the resistivity anisotropy ratio tracked anomalous zones that correspond to zones of recent complex faulting and potential lateral inflows in the overburden mass transport systems. Thus, joint inversion of CSEM and seismic refraction data has potential to improve the mapping of overburden hazards offshore, with the associated resistivity anisotropy serving as a proxy for active ground deformation.

Influence of Vegetation Phenology on the Temporal Effect of Crop Fractional Vegetation Cover Derived from Moderate-Resolution Imaging Spectroradiometer Nadir Bidirectional Reflectance Distribution Function–Adjusted Reflectance

Moderate-Resolution Imaging Spectroradiometer (MODIS) Nadir Bidirectional Reflectance Distribution Function (BRDF)-Adjusted Reflectance (NBAR) products are being increasingly used for the quantitative remote sensing of vegetation. However, the assumption underlying the MODIS NBAR product’s inversion model—that surface anisotropy remains unchanged over the 16-day retrieval period—may be unreliable, especially since the canopy structure of vegetation undergoes stark changes at the start of season (SOS) and the end of season (EOS). Therefore, to investigate the MODIS NBAR product’s temporal effect on the quantitative remote sensing of crops at different stages of the growing seasons, this study selected typical phenological parameters, namely SOS, EOS, and the intervening stable growth of season (SGOS). The PROBA-V bioGEOphysical product Version 3 (GEOV3) Fractional Vegetation Cover (FVC) served as verification data, and the Pearson correlation coefficient (PCC) was used to compare and analyze the retrieval accuracy of FVC derived from the MODIS NBAR product and MODIS Surface Reflectance product. The Anisotropic Flat Index (AFX) was further employed to explore the influence of vegetation type and mixed pixel distribution characteristics on the BRDF shape under different stages of the growing seasons and different FVC; that was then combined with an NDVI spatial distribution map to assess the feasibility of using the reflectance of other characteristic directions besides NBAR for FVC correction. The results revealed the following: (1) Generally, at the SOSs and EOSs, the differences in PCCs before vs. after the NBAR correction mainly ranged from 0 to 0.1. This implies that the accuracy of FVC derived from MODIS NBAR is lower than that derived from MODIS Surface Reflectance. Conversely, during the SGOSs, the differences in PCCs before vs. after the NBAR correction ranged between –0.2 and 0, suggesting the accuracy of FVC derived from MODIS NBAR surpasses that derived from MODIS Surface Reflectance. (2) As vegetation phenology shifts, the ensuing differences in NDVI patterning and AFX can offer auxiliary information for enhanced vegetation classification and interpretation of mixed pixel distribution characteristics, which, when combined with NDVI at characteristic directional reflectance, could enable the accurate retrieval of FVC. Our results provide data support for the BRDF correction timescale effect of various stages of the growing seasons, highlighting the potential importance of considering how they differentially influence the temporal effect of NBAR corrections prior to monitoring vegetation when using the MODIS NBAR product.

Predicting leaf nitrogen content of coffee trees using the canopy hyperspectral reflectance feature bands, vegetation index and machine learning

ABSTRACT Leaf nitrogen content (LNC) is an essential indicator of crop nitrogen status. To rapidly and correctly estimate the LNC using hyperspectral remote sensing, the canopy hyperspectral reflectance of coffee trees treated with five levels of nitrogen fertilization in a greenhouse was obtained in this study. Five methods were used for hyperspectral data preprocessing, namely, Savitzky–Golay (SG) smoothing, a combination of SG and standard normal variate transformation (SG-SNV), a combination of SG and first-order derivative (SG-FD), a combination of SG and second-order derivative (SG-SD), and a combination of SG and multiplicative scatter correction (SG-MSC). Feature wavelengths were extracted using variables combination population analysis (VCPA), competitive adaptive reweighted sampling (CARS), and the combination (CARS-SPA) of CARS and successive projections algorithm (SPA). Vegetation indexes (VIs) were constructed and subjected to correlation analysis and variance inflation factor (VIF) analysis. Linear and nonlinear models including partial least squares regression (PLSR), back propagation neural network (BPNN), extreme learning machine (ELM), random forest regression (RFR), and support vector regression (SVR), were adopted to construct LNC retrieval models for coffee trees. The results indicated that SG-MSC could increase the signal-to-noise ratio of hyperspectral data well. The wavelengths selected by CARS-SPA were more relevant to LNC, and combined with ELM resulted in the best performance of LNC prediction (R2 P = 0.901, RMSEP = 0.825 g·kg−1, RPD = 3.229). Ten VIs were obtained through correlation analysis and VIF, and the VIs-based ELM prediction model also performed moderately well (R2 P = 0.814, RMSEP = 1.131 g·kg−1, RPD = 2.354). By comparing the coffee LNC prediction models established by different methods, two coffee LNC inversion models with better prediction accuracy were obtained, which provide a scientific basis for accurate diagnosis of coffee trees LNC, and are of great significance for optimizing the field management.

Screening the characteristic hyperspectral wavelength variables of peanut leaves based on coupled algorithms to predict SPAD value in peanut

ABSTRACT The accurate acquisition and prediction of the chlorophyll content in peanuts can provide reference for scientifically sound planting and management of peanut crops. In this study, peanut canopy leaves analysed during the flowering and pegging period were taken as the research object, and the spectral data collected by a portable hyperspectral spectrometer were used as the data source to study the inversion model of peanut leaf soil and plant analyser development (SPAD). To address the issues of randomness, the need for multiple iterations, and strong subjectivity in threshold selection in the initial variable set of the random frog (RF) algorithm, an improved RF algorithm was developed based on the sparse group lasso tool. By analysing and comparing the results of variable extraction of the improved RF algorithm and six characteristic wavelength screening algorithms, three algorithms were selected for pairwise coupling. These six algorithms were the correlation coefficient method (CC), successive projections algorithm, iteratively retains informative variables (IRIV), uninformative variable elimination (UVE), competitive adaptive reweighed sampling, and a genetic algorithm. The results show that: (1) the results of CC, UVE, and IRIV algorithms are better in a single algorithm experiment; and (2) in the coupled algorithms experiment, CC-UVE, CC-IRIV, and UVE-IRIV methods can effectively reduce dimensions, and the model has a certain level of stability. Among them, the UVE-IRIV-Extreme gradient boosting model had the highest accuracy, R 2 = 0.652, RMSE = 1.867, and the variable compression rate was as high as 97.84%. The research results can provide a theoretical basis for rapid and accurate monitoring of the SPAD values of peanut leaves.

Prediction of soil heavy metal content around mine tailings using multiple methods combined with transformed hyperspectral reflectance data

The extensive accumulation of tailings can potentially cause heavy metal contamination in the surrounding farmland soil. Accurately predicting the spatial distribution of heavy metals in farmland soil is crucial for assessing the potential environmental hazards of tailings.This study focuses on the spatial distribution and the quantitative prediction of heavy metals (chromium (Cr), vanadium (V), and copper (Cu)) in soils surrounding mine tailings using advanced spectral data analysis and multiple prediction models. The original hyperspectral reflectance data were processed using first-order differential (FD), second-order differential (SD), reciprocal logarithmic (LR), and continuum removal (CR) transformations to highlight the positions of characteristic bands. Multiple linear regression (MLR), stepwise linear regression (SLR), partial least squares regression (PLSR), random forest (RF), and back propagation artificial neural network (BP-ANN) models were used to establish inversion models for Cr, V, and Cu based on bands with high correlation coefficients. The performance of the inversion models was evaluated using the coefficient of determination (R2), root mean square error (RMSE), mean absolute error (MAE), and residual predictive deviation (RPD). The results indicate that the raw hyperspectral data from the measured soil exhibit a weak response to heavy metal content in the study area. However, applying FD, SD, and CR transformations significantly enhances the sensitivity of soil spectral data to heavy metal concentrations, facilitating subsequent modeling. Among these, the SD transformation is particularly beneficial for modeling the Cr and Cu elements in the soil. For the V element, the FD transformation yields data that are more suitable for modeling. Regarding the inversion models based on the measured spectral data, the BP-ANN model exhibited the best predictive performance. Specifically, when combined with SD spectral data, the BP-ANN achieved the highest predictive accuracy for Cu content (R² = 0.85, RPD = 2.12). The RF model demonstrated the next best performance, with its optimal inversion model also utilizing SD spectral data for predicting Cu content (R² = 0.76, RPD = 1.90). On the other hand, the MLR model exhibited the poorest performance and is unsuitable for predicting heavy metal content in the region using the measured spectral data. This study highlights the potential of spectral data in environmental monitoring and provides a technical reference for the inversion assessment and regulation of heavy metals in farmlands surrounding tailing sites.

Seismic Impedance Inversion Based on Improved Cycle-WGAN

Abstract The application of deep learning methods for seismic impedance inversion usually requires a large amount of labeled data to train the network, while labeled data available in practical applications is often limited, which affects the effectiveness of the relevant methods. In order to address this problem, this paper proposes one kind of deep learning method of a closed-loop cycle Wasserstein generative adversarial network (Cycle-WGAN) for seismic impedance inversion based on the combination of “data-driven and model-driven”. The method uses a small amount of labeled data and unlabeled seismic data generated by the forward modeling. It constitutes a bidirectional cycle of inversion and forward modeling through the generative and adversary networks for the inversion and generative and adversary networks for the forward modeling by convolution model, which improves the conventional GAN networks. The proposed method also introduces the Wasserstein loss function to improve the neural network’s training stability. Tested on the Marmousi model with complex structure, the proposed Cycle-WGAN network can effectively obtain the seismic impedance inversion results. Moreover, it is highly robust when seismic data are noisy and has higher accuracy than the inversion results from some conventional neural networks, such as RNN (Recurrent Neural Network), TCN (Temporal Convolutional Network, WGAN (Wasserstein Generative Adversarial Network), etc.

Permafrost Region Greenhouse Gas Budgets Suggest a Weak CO2 Sink and CH4 and N2O Sources, But Magnitudes Differ Between Top‐Down and Bottom‐Up Methods

AbstractLarge stocks of soil carbon (C) and nitrogen (N) in northern permafrost soils are vulnerable to remobilization under climate change. However, there are large uncertainties in present‐day greenhouse gas (GHG) budgets. We compare bottom‐up (data‐driven upscaling and process‐based models) and top‐down (atmospheric inversion models) budgets of carbon dioxide (CO2), methane (CH4) and nitrous oxide (N2O) as well as lateral fluxes of C and N across the region over 2000–2020. Bottom‐up approaches estimate higher land‐to‐atmosphere fluxes for all GHGs. Both bottom‐up and top‐down approaches show a sink of CO2 in natural ecosystems (bottom‐up: −29 (−709, 455), top‐down: −587 (−862, −312) Tg CO2‐C yr−1) and sources of CH4 (bottom‐up: 38 (22, 53), top‐down: 15 (11, 18) Tg CH4‐C yr−1) and N2O (bottom‐up: 0.7 (0.1, 1.3), top‐down: 0.09 (−0.19, 0.37) Tg N2O‐N yr−1). The combined global warming potential of all three gases (GWP‐100) cannot be distinguished from neutral. Over shorter timescales (GWP‐20), the region is a net GHG source because CH4 dominates the total forcing. The net CO2 sink in Boreal forests and wetlands is largely offset by fires and inland water CO2 emissions as well as CH4 emissions from wetlands and inland waters, with a smaller contribution from N2O emissions. Priorities for future research include the representation of inland waters in process‐based models and the compilation of process‐model ensembles for CH4 and N2O. Discrepancies between bottom‐up and top‐down methods call for analyses of how prior flux ensembles impact inversion budgets, more and well‐distributed in situ GHG measurements and improved resolution in upscaling techniques.

Fifty years of studying the GCR intensity during inversion of the heliospheric magnetic fields. II. HMF inversion on the inner heliospheric boundary

Phenomena in the outer layer of the solar atmosphere, the heliosphere, including the supersonic solar wind, the heliospheric magnetic field (HMF) carried by it, and cosmic rays propagating in the heliosphere are important for many processes occurring in this layer. For some of these processes such as geomagnetic activity or propagation of cosmic rays, not only the strength, but also the direction of the field is significant. Nonetheless, if in this regard the situation during periods of low sunspot activity is quite clear — the heliosphere is divided into two hemispheres with opposite polarity (toward the Sun/away from the Sun), — during periods of high sunspot activity when the HMF inversion occurs, there is no simple model of this phenomenon. The paper is a sequel to the study of the HMF inversion phenomenon and associated effects in the intensity of galactic cosmic rays (GCR). Previously, general ideas about the 22-year cyclicity in the characteristics of the Sun, heliosphere, and cosmic rays have been formulated, and the effects observed in the GCR intensity, which we associate with the HMF inversion, have been discussed in detail. This paper deals with a model of HMF inversion, associated only with the evolution of the magnetic field in the layer between the photosphere and the base of the heliosphere due to changes in the distribution of photospheric fields from one solar rotation to the next one, and shows that this is not enough to explain the main effects in the GCR intensity. In this layer, the magnetic field is the main energy factor. A more complete model of HMF inversion, including the transformation of its characteristics due to the interaction of different-speed solar wind streams in the heliosphere itself, where the solar wind is the main energy factor, will be discussed in the next paper.

Пятьдесят лет исследования поведения интенсивности ГКЛ в периоды инверсии гелиосферного магнитного поля. II. Инверсия ГМП на внутренней границе гелиосферы

Phenomena in the outer layer of the solar atmosphere, the heliosphere, including the supersonic solar wind, the heliospheric magnetic field (HMF) carried by it, and cosmic rays propagating in the heliosphere are important for many processes occurring in this layer. For some of these processes such as geomagnetic activity or propagation of cosmic rays, not only the strength, but also the direction of the field is significant. Nonetheless, if in this regard the situation during periods of low sunspot activity is quite clear — the heliosphere is divided into two hemispheres with opposite polarity (toward the Sun/away from the Sun), — during periods of high sunspot activity when the HMF inversion occurs, there is no simple model of this phenomenon. The paper is a sequel to the study of the HMF inversion phenomenon and associated effects in the intensity of galactic cosmic rays (GCR). Previously, general ideas about the 22-year cyclicity in the characteristics of the Sun, heliosphere, and cosmic rays have been formulated, and the effects observed in the GCR intensity, which we associate with the HMF inversion, have been discussed in detail. This paper deals with a model of HMF inversion, associated only with the evolution of the magnetic field in the layer between the photosphere and the base of the heliosphere due to changes in the distribution of photospheric fields from one solar rotation to the next one, and shows that this is not enough to explain the main effects in the GCR intensity. In this layer, the magnetic field is the main energy factor. A more complete model of HMF inversion, including the transformation of its characteristics due to the interaction of different-speed solar wind streams in the heliosphere itself, where the solar wind is the main energy factor, will be discussed in the next paper.

Soil Nutrients Inversion in Open‐Pit Coal Mine Reclamation Area of Loess Plateau, China: A Study Based on ZhuHai‐1 Hyperspectral Remote Sensing

ABSTRACTSoil nutrients are crucial to assess land reclamation quality, and the use of various types of remote sensing data for soil nutrient inversion has been a key focus for soil monitoring. However, fewer studies have been conducted using satellite‐based hyperspectral remote sensing. To explore the potential of satellite‐based hyperspectral remote sensing in soil nutrient monitoring, this study selected soil organic matter, total nitrogen, available phosphorus, and available potassium content data from 83 sample sites using ZhuHai‐1 hyperspectral data. After spectral transformation and feature extraction, various inversion models were constructed, including partial least squares regression, support vector machine, recurrent neural network, and random forest. After verification by accuracy, the best spectral‐model combination was used for inversion. The results showed that the R‐squared range of the inversion models was 0.67748–0.78115. High content areas of soil organic matter and available potassium exhibited concentrated and contiguous features, while high content areas of total nitrogen and available phosphorus were more fragmented and fine‐grained. Alfalfa grassland plays a vital role in improving reconstructed soil in the early reclamation stage, and agricultural activities have differential impacts on soil nutrient accumulation. This study provides a theoretical basis for verifying the application capability of ZhuHai‐1 hyperspectral satellite data in soil monitoring.

Spectro-environmental factors integrated ensemble learning for urban river network water quality remote sensing

Remote sensing water quality monitoring technology can effectively supplement the shortcomings of traditional water quality monitoring methods in spatiotemporal dynamic monitoring capabilities. At present, although the spectral feature-based remote sensing water quality inversion models have achieved many successes, there could still be a problem of insufficient generalization ability in monitoring the water quality of complex river networks in large cities. In this paper, we propose a spectro-environmental factors integrated ensemble learning model for urban river network water quality inversion. We analyzed the correlation between water quality parameters, spectral reflectance, and environmental factors based on an in-situ dataset collected in the northern part of Shanghai. Using the Hot Spot Analysis (Getis-Ord Gi*), we found that river network water quality parameters have different patterns in different urban functional zones. Furthermore, daily average temperature, total rainfall within the seven days, and several band combinations were also selected as the environmental and spectral features using factor analysis and Pearson correlation coefficient analysis. After the feature analysis, the spectro-environmental factors integrated ensemble learning model was trained. Compared with the spectral-based machine learning inversion models, the coefficients of determination R2 increased by about 0.50. Our model was also tested in three different test areas within and outside the in-situ sampling areas in Shanghai based on low-altitude multispectral remote sensing images. The R2 results for total phosphorus (TP), ammonia nitrogen (NH3-N), and chemical oxygen demand (COD) within the in-situ sampling areas were 0.52, 0.58, and 0.56 respectively. The mean absolute percentage error (MAPE) results were 53.36%, 63.95%, and 22.46% respectively. After adding the area outside the in-situ sampling areas, the R2 results for TP, NH3-N, and COD were 0.47, 0.47, and 0.53. The MAPE were 49.38%, 74.46%, and 20.49%. Our research provided a new remote sensing water quality inversion method to be utilized in complex urban river networks which exhibited solid accuracy and generalization ability.

Inversing NOx emissions based on an optimization model that combines a source-receptor relationship correction matrix and monitoring data: A case study in Linyi, China

Inversion based on observational data and the source-receptor relationship (SRR) simulated by an air quality model is an effective means to estimate NOx emissions. However, the SRR bias induced by the inherent uncertainty of the simulated model leads to potential errors in the inversed emissions, but this is seldom considered in NOx inversion. In this study, we constructed an inversion model based on the SRR correction (IMSC) by introducing a correction matrix, combined with joint regularization scheme and genetic algorithm. We innovated the dynamic acquisition method of center-restricted parameters for the correction matrix, combined this with other parts of the IMSC, attaining the multi-month and multi-region pollutant emission inversion. Hypothetical examples demonstrated that the IMSC effectively corrected the SRR and accurately estimated the NOx emissions. The IMSC was used to estimate Linyi county-level NOx emissions for January, April, July and October (typical months) during 2020 and 2021. An inversion model without SRR correction (IMWSC) was developed for comparison with the IMSC. Results showed that the IMSC more accurately, robustly, and reasonably estimated NOx emissions. Compared to the IMWSC, the IMSC improved the mean correlation between NOx emissions and NO2 observational concentrations by 25.0%, enhancing the correlation between NOx emissions and NO2 column concentrations by 111.3%. The similar NOx emission change ratios (σaverage = 5.1%) between the typical months in 2021 and 2020 among the different counties indicated a more robust performance of the IMSC than the IMWSC (σaverage = 55.2%). In addition, the NOx emissions inversed by the IMSC also showed better consistency with social activity levels (i.e., electricity consumption). The typical monthly Linyi's county-level NOx emission characterization was also studied. NOx emissions were lower in April, July, and October 2021 than the same period in 2020 due to COVID-19 and pollution controls. This study provides strategies for swiftly and accurately estimating pollutant emissions.

Determination of biogeochemical properties in sea waters using the inversion of the three-stream irradiance model

Inversion models, in the context of oceanography, relate the observed ocean color to the concentrations of the different biogeochemical components present in the water of the ocean. However, building accurate inversion models can be quite complex due to the many factors that can influence the observed ocean color, such as variations in the composition or the optical properties of biogeochemical products. Here we assess the feasibility of the inversion approach, by implementing the three-stream light inversion model in a one-dimensional water column configuration, represented at the BOUSSOLE site in the northwestern Mediterranean Sea. Moreover, we provide a comprehensive sensitivity analysis of the model’s skill by perturbing the parameters of the bio-optical properties and phytoplankton physiology. Analysis of the inversion indicates that the model is able to reconstruct the variability of the optical constituents. Results indicate that chlorophyll-a and coloured dissolved organic matter play a major role in light modulation. The sensitivity analysis shows that the parameterization of the ratio of chlorophyll-a to carbon is important for the performance of the inversion model. A coherent inversion model, as presented, can be used as an observational operator to assimilate remote sensing reflectance.