Inversion Model Research Articles

Advancing railway infrastructure maintenance: Thermodynamic parameter inversion of ballast bed and feasibility assessment of fouling detection via infrared thermography (IRT)

The fouled ballast bed significantly impacts the operational condition of the ballasted track. Timely detection of the fouled ballast bed can greatly reduce maintenance workloads and capital expenditure. This study aims to assess the feasibility of employing infrared thermography (IRT) for detecting fouling. Nonetheless, existing research has been conducted under conditions noticeably different from the actual field. Moreover, the climatic conditions surrounding the ballasted track are beyond control. Hence, this paper innovatively utilized thermodynamic simulation to simulate as many conditions as possible. Firstly, temperatures and external meteorological data were obtained on a newly constructed railway line using an infrared thermal imager, temperature sensors, and a weather station. Thermodynamic inversion models were then established based on field structures to determine crucial thermodynamic parameters. The results demonstrate excellent alignment with the existing literature. Expanding upon this groundwork, the study explored the impact of various meteorological factors and weather conditions on the detection effectiveness of IRT. The results indicate that the solar radiation intensity (S) and air temperature (T) have the most significant effect on the surface temperature of the ballast bed (STB) and the temperature difference between the clean and fouled ballast beds (CF-TD). The rainfall (R) can change the thermodynamic properties of the ballast bed. Without considering R, the higher the S or T, the greater the CF-TD. Specifically, on sunny days, the maximum CF-TD can achieve 3.26 °C, while on cloudy days or at night, it is only 0.79 °C and 0.28 °C, respectively. Furthermore, the impact of wind speed (W) on the CF-TD was found to be disregarded in regions with low wind speeds but significant in regions with high wind speeds. In summary, IRT shows promise for rapid initial assessments of ballast bed conditions on sunny days, particularly following rain. While on cloudy days or at night, higher resolution infrared thermal imagers may be required, or other detection indicators may need to be investigated.

Inversion of marine magnetotelluric and controlled source electromagnetic data with site gap and low signal-to-noise ratio

SUMMARY Marine magnetotelluric (MT) and controlled source electromagnetic (CSEM) methods have been routinely applied to survey the crustal and upper mantle electrical resistivity structures beneath the sea floor. In practice, there are inevitably site gaps and contamination by noises in the collected data because of lost ocean bottom electromagnetic receivers, unusable data, and difficulties in deploying instruments near deep trenches. So far, it remains unclear to what degree those factors will lower the resolution and the credibility of marine MT and CSEM inversion models. In this paper, we investigate the individual and combined effects of site gaps and data noises on the inversion models through synthetic analyses based on a simple block resistivity model and a realistic resistivity structure derived from the Mariana Trench. The results suggest that data with a sufficiently high signal-to-noise ratio can reasonably recover the subseafloor structures in the area of data gap. The transverse electric mode and tipper data from the MT method are much more sensitive to the structure near the site gap. The joint inversion of MT and CSEM data would improve the model's resolution at the site gap area. The inversion of data with a relatively low signal-to-noise ratio, for example, 10 per cent, can recover the structures with few artefacts if there is no site gap. But if the site gap and noisy data are combined, even a joint inversion cannot correctly recover the burial depths and geometries of the anomalous bodies beneath the site gap where vertical strips are likely present. To improve the model's resolution and suppress inversion artefacts, we propose constraining part of the model with as much a prior information as possible. Specifically, for a survey in the subduction zone, we could reduce the penalties on the model's smoothness at the upper and low interfaces of the resistive subduction slab, or even fix the resistivity of the resistive slab with the help of other information, if any. The inversion models shown in this paper provide valuable references for the site design before marine MT and CSEM surveys as well as for interpreting real data inversion models that may be subject to the same biases introduced by the site gap and noise.

A deep learning-based surrogate model for trans-dimensional inversion of discrete fracture networks

Fractures and their geometrical patterns are usually required to analyze the mechanical and flow properties of porous media in the subsurface. Fracture characterization is therefore regarded of crucial importance for optimizing production management or achieving maximum storage capacity. In this research, we propose to invert the fracture networks under the Bayesian framework for the uncertainty quantification. In particular, the number of fractures in the modelling system is treated as unknown, leading to a trans-dimensional inverse problem, and the reversible jump Markov chain Monte Carlo algorithm is applied to sample the model space with possible model moves proposed in the sampling process. A deep learning network is further applied as a surrogate model in the sampling process for increasing the computational efficiency, instead of using the physical forward simulator. We apply the proposed methodology to estimate the spatial distribution of fracture networks based on the head measurements from the steady-state flow simulation. The prior distributions of fracture parameters such as position, orientation and length are described using the discrete fracture networks approach that is deeply rooted in stochastic modelling. Due to the high non-uniqueness, the correct spatial distribution of fracture patterns cannot be successfully recovered in this case study, even a good match between observed and simulated head data is reached. More analysis could be performed in the future with the production historical data or more informative priors.

Inversion of image-only intrinsic parameters for steel fibre concrete under combined rate-temperature conditions: An adaptively enhanced machine learning approach

Concrete not only bears quasi-static loads during the service of engineering structures, but also bears impact or explosion due to accidental accidents, so more and more attention has been paid to the study of concrete deformation characteristics and stress distribution. Obtaining the correct constitutive parameters is crucial for the study of the mechanical behavior of concrete, and the determination of constitutive parameters is essentially an inverse process, which is very challenging. In this paper, third-order Bessel curves are used to construct dynamic constitutive equations for steel-fibre concrete under rate-temperature union conditions, and to establish a database of the constitutive parameters corresponding to the factors influencing the mechanical behaviour of concrete. In order to select highly accurate and adaptive intelligent inversion models, Firstly, the black-winged kite optimisation algorithm (BKA) has been improved by improving the black-winged kite leader condition and integrating the optimal perturbation strategy of Morlet wavelet factor, which proves the superiority of the MBKA algorithm by comparing it with the BKA, Dung Beetle Optimisation Algorithm (DBO), Grey Wolf Optimisation Algorithm (GWO), and Harris Hawk Algorithm (HHO) in searching for the optimal results. Secondly, based on the database, the MBKA-LSSVR model, MBKA-LSSVR-Adaboost model, CNN-GRU model and CNN-LSTM model were built sequentially, respectively. The final results show that the MBKA-LSSVR-Adaboost model has the highest accuracy and the best performance for the parameter Pi (i = 0,1,2,3), and the inverted stress-strain curve proves that the proposed method is effective and accurate in determining the proposed constitutive parameters.

Inversion study of dam incidence angle under oblique incidence of seismic waves

Determination of seismic wave incidence angle is a critical step in performing seismic oblique incidence analysis of dams. However, for structures with complex dynamic responses, such as dams, it is difficult to derive the incident angle by traditional mathematical methods. For this reason, this paper proposes a method for determining the seismic wave incidence angle based on the dynamic response of the dam. The model calculates the dynamic response of the dam under different incidence angles of seismic waves, establishes the mapping relationship between the dynamic response and the incidence angle by using a hybrid particle network, and performs feature screening to obtain the optimal inversion features by the SHAP method, and then constructs the optimal inversion model. The results show that there is a significant correlation between the dynamic response of the dam body and the incident angle, the correlation coefficients of the model are all greater than 0.96, and the average absolute percentage errors are less than 5 %, which proves that the model can effectively invert the incident angle. The method can also be applied to the seismic safety analysis of other structures, providing a new way to determine the seismic wave incidence angle.

Research on fertilization decision method for rice tillering stage based on the coupling of UAV hyperspectral remote sensing and WOFOST.

The use of chemical fertilizers in rice field management directly affects rice yield. Traditional rice cultivation often relies on the experience of farmers to develop fertilization plans, which cannot be adjusted according to the fertilizer requirements of rice. At present, agricultural drones are widely used for early monitoring of rice, but due to their lack of rationality, they cannot directly guide fertilization. How to accurately apply nitrogen fertilizer during the tillering stage to stabilize rice yield is an urgent problem to be solved in the current large-scale rice production process. WOFOST is a highly mechanistic crop growth model that can effectively simulate the effects of fertilization on rice growth and development. However, due to its lack of spatial heterogeneity, its ability to simulate crop growth at the field level is weak. This study is based on UAV remote sensing to obtain hyperspectral data of rice canopy and assimilation with the WOFOST crop growth model, to study the decision-making method of nitrogen fertilizer application during the rice tillering stage. Extracting hyperspectral features of rice canopy using Continuous Projection Algorithm and constructing a hyperspectral inversion model for rice biomass based on Extreme Learning Machine. By using two data assimilation methods, Ensemble Kalman Filter and Four-Dimensional Variational, the inverted biomass of the rice biomass hyperspectral inversion model and the localized WOFOST crop growth model were assimilated, and the simulation results of the WOFOST model were corrected. With the average yield as the goal, use the WOFOST model to formulate fertilization decisions and create a fertilization prescription map to achieve precise fertilization during the tillering stage of rice. The research results indicate that the training set R2 and RMSE of the rice biomass hyperspectral inversion model are 0.953 and 0.076, respectively, while the testing set R2 and RMSE are 0.914 and 0.110, respectively. When obtaining the same yield, the fertilization strategy based on the ENKF assimilation method applied less fertilizer, reducing 5.9% compared to the standard fertilization scheme. This study enhances the rationality of unmanned aerial vehicle remote sensing machines through data assimilation, providing a new theoretical basis for the decision-making of rice fertilization.

Soil–water contamination assessment due to dumpsite-impacted leachates in a metamorphic environment

Environmental contamination is a complex issue that significantly impacts human health and ecological systems, especially in developing countries like Nigeria. This study explores the interplay between surface-subsurface soil-rock resistivity and subsurface geotechnical properties, along with multiple soil-water pollution indices at and around the Old Owo-Ikare road (OOIR) dumpsite in southwestern Nigeria. Water samples from five wells and one stream were collected and analyzed, alongside twelve soil samples from four trial pits at varied depths for geoenvironmental assessment. Analyzed hydrogeochemical parameters include Na, Ca, Mg, K, Cr, Pb, Zn, Ni, Mn, Fe, and cations (NO, SO4, Cl) in the water samples. Nine dipole-dipole electrical resistivity tomography (ERT) profiles and six Schlumberger vertical electrical sounding (VES) points were used to determine surface–subsurface resistivity distribution. Water analysis results showed elevated levels of K, Ni, Cr, and Pb concentrations. Geotechnical tests indicated that most dumpsite soil meets good landfill material criteria with low permeability (5.92×10−6 to 1.49×10−4cm/s). ERT and VES inverted models revealed four lithologic layers with two main curve types (HA and QA). Elevated heavy metal levels in the dumpsite soil and inverted resistivity models suggested leachate contamination in the topsoil and weathered bedrock units. Despite relatively lower heavy metal concentrations compared to other Nigerian and Ghanaian dumpsites, multi-pollution indices indicated significant contamination in both water and soil samples, posing moderate to very high environmental risks based on World Health Organisation standards. Additionally, human health risk assessment suggested a risk of cancer and non-cancer-related diseases from prolonged well water consumption. Therefore, ongoing monitoring and remediation efforts are essential to forestall imminent pollution in uncovered areas.

Quantitative analysis of spatiotemporal coverage and uncertainty decomposition in OCO-2/3 XCO2 across China

The Orbiting Carbon Observatory 2/3 (OCO-2/3) employs a full-physics inversion algorithm and estimates the column-averaged dry air mole fraction of carbon dioxide (XCO2). This research focuses on the spatiotemporal coverage and associated uncertainties of OCO-2/3's XCO2 measurements within the Chinese region. We undertook an extensive statistical examination of the daily, weekly, 16-day, and monthly spatiotemporal coverage of XCO2 across China, employing data from both OCO-2 and OCO-3 and their amalgamated datasets. Additionally, we used a posterior error analysis method to rigorously evaluate the uncertainties induced by various input parameters in the XCO2 inversion model across different regions in China. Our findings reveal that OCO-2's spatial coverage spans 2.3–53.8% in China at a spatial resolution of 1° × 1°, and OCO-3's spatial coverage spans 3.3 and 47.4% across these time scales in China. Notably, the combined coverage rates of both satellites are significantly higher, ranging from 5.4 to 72.6%. Our analysis also reveals a gradual decrease in XCO2 coverage and inversion rates from north to south and west to east across China. A significant portion of the uncertainties in OCO-2/3 XCO2 measurements, over 90%, were attributed to primary error sources such as instrument, aerosol, and CO2 prior profile. Aerosol-induced errors show regional variations, with higher levels observed in Southeast China. Compared to TCCON data, the accuracy of XCO2 measurements in the two Chinese sites was lower than the global average. Our research identifies the spatiotemporal distribution and uncertainty decomposition of carbon satellites at a regional scale in China, providing an essential reference for future research.

Using the AIEM and Radarsat-2 SAR to Retrieve Bare Surface Soil Moisture

Taking the Jiefangzha irrigation area of the Inner Mongolia Autonomous Region as the research area, the response relationships between the backscattering coefficient and radar frequency, radar incidence angle, root-mean-square height, correlation length, and soil water content under different conditions were simulated using advanced integral equations. The backscattering characteristics of exposed surfaces in cold and dry irrigation areas were discussed, and the reasons for the different effects were analyzed. Based on this, surface roughness models and statistical regression moisture inversion models were constructed through co-polarized backscatter coefficients and combined surface roughness. The correlation between the inverted surface roughness values and the measured values was R2 = 0.7569. The correlation between the soil moisture simulation values and the measured values was R2 = 0.8501, with an RMSE of 0.04. The findings showed a strong correlation between the values from the regression simulation and the measured data, indicating that the model can be applied to soil moisture inversion and has a good inversion accuracy. Compared with previous studies in the same area, the inversion model proposed in this paper has a higher accuracy and is more suitable for the inversion of soil moisture in the Jiefangzha irrigation area. These findings can support research on the water cycle and water environment assessment in the region.

The response mechanism and testing method of the rock elastic modulus while drilling

The elastic modulus is a basic parameter reflecting the deformation resistance and energy concentration characteristics of rock. The traditional testing method of the rock elastic modulus needs to core the surrounding rock and testing it in the laboratory, which has difficulty reflecting the rock mechanical properties under the engineering site environment, and uncontrolled broken surrounding rock is difficult to core for testing. Digital drilling technology provides a new idea for in situ testing of the rock’s elastic modulus. The key is to establish a quantitative relationship between the elastic modulus and drilling data. In this paper, the relationship between rock cutting energy density and drilling data is established, and a series of rock digital drilling tests are carried out. The response law of the drilling data and cutting energy density to the rock elastic modulus is clarified. The inversion model of the rock elastic modulus while drilling (DP-E model) is established. The verification test results indicate that the average difference rate of the test results based on the DP-E model is 7.06% compared with the compression test method, which verifies the effectiveness of the model. This study provides a theoretical basis for in situ testing of the rock elastic modulus.

UReslham: Radar reflectivity inversion for smart agriculture with spatial federated learning over geostationary satellite observations

AbstractThe frequent occurrence of severe convective weather has certain adverse effects on the smart agriculture industry. To enhance the prediction of severe convective weather, the inversion model effectively fills radar reflectivity data gaps by leveraging geostationary satellite data, offering more comprehensive and accurate support for meteorological information in smart agriculture systems. Nevertheless, collaborative cross‐regional inversion driven by dispersed radar data faces challenges in efficiency, privacy, and model accuracy. To this end, we employ an U‐shaped residual network with an embedded light hybrid attention mechanism and utilize a federated averaging algorithm for efficient distributed training across multiple devices which could preserve the privacy of data from different locations, thereby improving inversion performance. In addition, to address the unbalanced nature of radar data, a weighted loss function is designed to enhance the model's sensitivity to high radar reflectivity. Experimental results demonstrate that the proposed model exhibits a certain level of improvement in evaluating radar reflectivity inversion performance across different thresholds compared to other models, thus substantiating the superiority of the proposed approach.

Inversion of Chlorophyll-a Concentration in Wuliangsu Lake Based on OGolden-DBO-XGBoost

Chlorophyll-a (Chl-a) concentration is one of the important indicators in water bodies for assessing the ecological health of water quality. In this paper, an OGolden-DBO-XGBoost Chl-a concentration inversion model is proposed using Wuliangsu Lake as the study area, and by combining the Sentinel-2 remote-sensing satellite images and measured Chl-a concentration data in Wuliangsu Lake, the XGBoost model is optimized using the hybrid-strategy-improved dung beetle optimization algorithm (OGolden-DBO), and an OGolden-DBO-XGBoost Chl-a concentration inversion model. The OGolden-DBO-XGBoost model’s coefficients of determination (R2s) were 0.8936 and 0.8850 on the training set and test set, according to the results. The root mean squared errors (RMSEs) were 3.1353 and 2.9659 μg/L, and the mean absolute errors (MAEs) were 1.8918 and 2.4282 μg/L. The model performed well and provided a strong support for the detection of Chl-a concentration in Wuliangsu Lake.

Introduction to this special section: Subsurface uncertainty

Handling the nonuniqueness and ambiguity of geophysical inversion results is a major challenge in characterizing the subsurface. Geophysical inversion is the process of estimating the physical properties of the subsurface from the measured geophysical data, such as seismic, electromagnetic, gravity, or magnetic signals. However, there are often many different subsurface models that can fit the same data adequately well. Therefore, it is important to evaluate the uncertainty and reliability of the inverted models and integrate other sources of information, such as geologic, petrophysical, or geochemical data, to reduce uncertainty. Despite the industry's advances in recent decades, the number of subsurface outcomes that fall outside predicted ranges is disproportionate to the supposed certainty of those ranges. This leads to inefficient allocation of investment budgets, which is particularly painful due to the capital-intensive nature of the oil and gas industry.

Cunninghamia lanceolata Canopy Relative Chlorophyll Content Estimation Based on Unmanned Aerial Vehicle Multispectral Imagery and Terrain Suitability Analysis

This study aimed to streamline the determination of chlorophyll content in Cunninghamia lanceolate while achieving precise measurements of canopy chlorophyll content. Relative chlorophyll content (SPAD) in the Cunninghamia lanceolate canopy were assessed in the study area using the SPAD-502 portable chlorophyll meter, alongside spectral data collected via onboard multispectral imaging. And based on the unmanned aerial vehicle (UAV) multispectral collection of spectral values in the study area, 21 vegetation indices with significant correlation with Cunninghamia lanceolata canopy SPAD (CCS) were constructed as independent variables of the model’s various regression techniques, including partial least squares regression (PLSR), random forests (RF), and backpropagation neural networks (BPNN), which were employed to develop a SPAD inversion model. The BPNN-based model emerged as the best choice, exhibiting test dataset coefficients of determination (R2) at 0.812, root mean square error (RSME) at 2.607, and relative percent difference (RPD) at 1.942. While the model demonstrated consistent accuracy across different slope locations, generalization was lower for varying slope directions. By creating separate models for different slope directions, R2 went up to about 0.8, showcasing favorable terrain applicability. Therefore, constructing inverse models with different slope directions samples separately can estimate CCS more accurately.

Smoothness: The key factor in well-log information-assisted PINNtomo

Seismic traveltime tomography is popularly used to provide a smooth background model for subsequent depth migration or full waveform inversion (FWI). The core of the traveltime tomography process requires an eikonal solver for seismic traveltime calculation. Most of the conventional eikonal solvers, suffer from first-order convergence errors and difficulties in dealing with irregular topography, which will lead to inaccurate seismic tomography results. To solve these issues, physics-informed neural networks (PINNs) have been developed and demonstrated remarkable success in simultaneous traveltime calculation and velocity construction, which is referred to as PINNtomo. It combines the physics constraint (eikonal equation) and data constraint (traveltime fitting) to form the loss function of the networks. Though PINNtomo can manage to recover a smooth background velocity, it will fail in the inversion for a complicated high-resolution velocity model. We take advantage of the well-log velocity as a prior information to provide an additional model constraint in the loss function to improve the performance of PINNtomo. We found that the well-log information will provide high-resolution components in the inverted velocity model. However, it will destroy the lateral structure of the true model. We propose to use a smoothed version of the well-log velocity as the prior model constraint in PINNtomo. As a result, the accuracy of the inverted background velocity will be improved, which will be beneficial to the subsequent FWI.

Inversion of Glycyrrhiza Chlorophyll Content Based on Hyperspectral Imagery

Glycyrrhiza is an important medicinal crop that has been extensively utilized in the food and medical sectors, yet studies on hyperspectral remote sensing monitoring of glycyrrhiza are currently scarce. This study analyzes glycyrrhiza hyperspectral images, extracts characteristic bands and vegetation indices, and constructs inversion models using different input features. The study obtained ground and unmanned aerial vehicle (UAV) hyperspectral images and chlorophyll content (called Soil and Plant Analyzer Development (SPAD) values) from sampling sites at three growth stages of glycyrrhiza (regreening, flowering, and maturity). Hyperspectral data were smoothed using the Savitzky–Golay filter, and the feature vegetation index was selected using the Pearson Correlation Coefficient (PCC) and Recursive Feature Elimination (RFE). Feature extraction was performed using Competitive Adaptive Reweighted Sampling (CARS), Genetic Algorithm (GA), and Successive Projections Algorithm (SPA). The SPAD values were then inverted using Partial Least Squares Regression (PLSR), Support Vector Regression (SVR), Random Forest (RF), and Extreme Gradient Boosting (XGBoost), and the results were analyzed visually. The results indicate that in the ground glycyrrhiza inversion model, the GA-XGBoost model combination performed best during the regreening period, with R2, RMSE, and MAE values of 0.95, 0.967, and 0.825, respectively, showing improved model accuracy compared to full-spectrum methods. In the UAV glycyrrhiza inversion model, the CARS-PLSR combination algorithm yielded the best results during the maturity stage, with R2, RMSE, and MAE values of 0.83, 1.279, and 1.215, respectively. This study proposes a method combining feature selection techniques and machine learning algorithms that can provide a reference for rapid, nondestructive inversion of glycyrrhiza SPAD at different growth stages using hyperspectral sensors. This is significant for monitoring the growth of glycyrrhiza, managing fertilization, and advancing precision agriculture.

Monitoring Dissolved Oxygen Concentrations in the Coastal Waters of Zhejiang Using Landsat-8/9 Imagery

The Zhejiang coastal waters (ZCW), which exhibit various turbidity levels, including low, medium, and high turbidity levels, are vital for regional ecological balance and sustainable marine resource utilization. Dissolved oxygen (DO) significantly affects marine organism survival and ecosystem health, yet there is limited research on remote sensing monitoring of DO in the ZCW, and the underlying mechanisms are unclear. This study addresses this gap by utilizing high-resolution Landsat 8/9 imagery and sea surface temperature (SST) data to develop a multiple linear regression (MLR) model for DO estimation. Compared to previous studies that utilize remote sensing band reflectance data as inputs, the results show that the red and blue bands are more suitable for establishing DO inversion models for such water bodies. The model was applied to analyze variations in the DO concentrations in the ZCW from 2013 to 2023, with a focus on Hangzhou Bay (HZB), Xiangshan Bay (XSB), Sanmen Bay (SMB), and Yueqing Bay (YQB). The temporal and spatial distributions of DO concentrations and their relationships with environmental factors, such as chlorophyll-a (Chl-a) concentrations, total suspended matter (TSM) concentrations, and thermal effluents, are analyzed. The results reveal significant seasonal fluctuations in DO concentrations, which peak in winter (e.g., 9.02 mg/L in HZB) and decrease in summer (e.g., 6.83 mg/L in HZB). Changes in the aquatic environment, particularly in the thermal effluents from the Sanmen Nuclear Power Plant (SNPP), significantly decrease coastal dissolved oxygen (DO) concentrations near drainage outlets. Chl-a and TSM directly or indirectly affect DO concentrations, with notable correlations observed in XSB. This study offers a novel approach for monitoring and managing water quality in the ZCW, facilitating the early detection of potential hypoxia issues in critical zones, such as nuclear power plant heat discharge outlets.

Construction and Validation of Surface Soil Moisture Inversion Model Based on Remote Sensing and Neural Network

Surface soil moisture (SSM) reflects the dry and wet states of soil. Microwave remote sensing technology can accurately obtain regional SSM in real time and effectively improve the level of agricultural drought monitoring, and it is of great significance for agricultural precision irrigation and smart agriculture construction. Based on Sentinel-1, Sentinel-2, and Landsat-8 images, the effect of vegetation was removed by the water cloud model (WCM), and SSM was retrieved and validated by a radial basis function (RBF) neural network model in bare soil and vegetated areas, respectively. The normalized difference vegetation index (NDVI) calculated by Landsat-8 (NDVI_Landsat-8) had a better effect on removing the influence the of vegetation layer than that of NDVI_Sentinel-2. The RBF network model, established in a bare area (R = 0.796; RMSE = 0.029 cm3/cm3), and the RBF neural network model, established in vegetated areas (R = 0.855; RMSE = 0.024 cm3/cm3), have better simulation effects on SSM than a linear SSM inversion model with single polarization. The introduction of surface parameters to the RBF neural network model can improve the accuracy of the model and realize the high-accuracy inversion of SSM in the study area.

Monitoring soil salinity based on Sentinel-1/2 remote sensing parameters and two-dimensional space theory

Multi-spectral satellite remote sensing is widely used in soil salinity monitoring by virtue of its wide coverage, fast update and low cost, but it is susceptible to cloudy weather, limiting the accuracy and application conditions of the inversion, while SAR (synthetic aperture radar) is hardly affected by cloud with strong penetration ability and enables almost round-the-clock, climate-wide monitoring of soil salinity. Two-dimensional feature space model, being easily interpretive and widely used in soil salinity monitoring, requires only some easily extracted parameters and can reduce other influences on soil salinity inversion. Therefore, in order to take full advantages of the SAR and multi-spectral satellite, achieve monitoring of soil salinity in more climatic conditions and longer time, and further improve soil salinity monitoring accuracy, this paper combine SAR-Spectral data with two-dimensional space theory to construct two-dimensional models for monitoring soil salinity. In this study, two-dimensional space models are constructed using Sentinel-1 and Sentinel-2 feature parameters, and models are evaluated with R2 with measured soil salinity data of the Shahaoqu irrigation area in Hetao irrigation district, Inner Mongolia. The results show that it is feasible to use the parameters extracted from Sentinel-1 after H/a polarization for constructing two-dimensional feature space models for the soil salinity inversion(R2parameters of Sentinel-1 = 0.467,0.587,0.517), and the two-dimensional space models combining Sentinel-1/2 parameters are more accurate and stable in soil salinity monitoring than those using Sentinel-1 or Sentinel-2 alone((Rparameters of Sentinel-2 = 0.320,0.517,0.341),(R2parameters of Sentinel-1/2 = 0.507,0.545,0.562,0.512,0.557,0.568)).

New 3-D Fluorescence Spectral Indices for Multiple Pigment Inversions of Plant Leaves via 3-D Fluorescence Spectra

High-sensitivity fluorescence monitoring has been widely used in agriculture and environmental science. However, the active fluorescence detection information of leaf segments mainly focuses on total chlorophyll, and the fluorescence information of chlorophyll a, chlorophyll b, and some other pigments has not been explored. This only considers the fluorescence spectrum characteristics at a single wavelength or the fluorescence integral from a range of wavelength regions and does not completely consider the linkage relation between the excitation, emission, and interference information. In this paper, the three-dimensional fluorescence spectrum, containing the excitation and emission fluorescence spectra, and the corresponding multiple pigment characteristics from the upgraded LOPEX_ZJU database were collected. The linkages of excitation and emission of the three-dimensional fluorescence spectra of these pigments were analyzed for the newly built multiple pigment 3-D fluorescence spectral indices (3-D FSIs), including those of chlorophyll a, chlorophyll b, carotenoid, anthocyanin, and flavonoid 3-D FSIs. Then, these pigment inversion models were established and validated. The results show that the 3-D FSIs performances for the photosynthetic pigment content inversion (including chlorophyll a and b, and carotenoids) were much better than those for the photo-protective pigments (including anthocyanins and flavonoids) from the 3-D fluorescence spectra of these plant leaves. Here, the 3-D fluorescence normalization index (FNI ((F430,690-F430,763)/(F430,690+F430,763))) for the chlorophyll a inversion model has a high accuracy, the RMSE is 2.96 μg/cm2, and the 3-D fluorescence reciprocal difference index (FRI (F650,704/F650,668) for the chlorophyll b model has an encouraging RMSE (2.01 μg/cm2). The RMSE of the 3-D fluorescence ratio index (FRI (F500,748/F500,717)) for the carotenoid inversion is 3.77 μg/cm2 RMSE. Only FRI (F370,615/F370,438) was selected for the modeling and validating evaluation of the leaf Flas content inversion, but the evaluation metrics were not good, with an RMSE (151.13 μg/cm2). For Ants, although there was a 3-D FSI (FRDI (1/F540,679-1/F540,557)), and its evaluation metrics, with an RMSE (2.8 μg/cm2), were at or over 0.05 level, the validating evaluation metric VC (98.3577%) was not encouraging. These results showed that fluorescence, as a nondestructive and efficient detection method, could determine the contents of chlorophyll a, chlorophyll b, and carotenoid in plant leaves, providing a new method to detect plant information. It can also provide a potential chance for the fluorescence images of fine photo-protective pigments, especially chlorophyll a and b, using the special active fluorescence excitation light source and a few fluorescence imaging channels from the optimal FSIs.