Inversion Model Research Articles

Efficient physics-informed transfer learning to quantify biochemical traits of winter wheat from UAV multispectral imagery

Accurate and efficient estimation of biochemical traits, including leaf index area (LAI), leaf chlorophyll content (LCC) and canopy chlorophyll content (CCC), is crucial for crop growth monitoring in agricultural management. Recent advancements in unmanned aerial vehicle (UAV) multispectral remote sensing have enabled fast and cost-effective measurements of these traits. However, traditional statistical regression models trained on specific datasets lack scalability and transferability across practical field conditions without retraining. This study proposed an efficient physics-informed transfer learning model (PITL) for winter wheat biochemical traits estimation from UAV multispectral data. The PITL integrates the strengths of physical radiative transfer simulations and deep neural network architectures through transfer learning to improve the estimation of biochemical traits from UAV multispectral data. The PITL was tested with convolutional neural network (CNN), deep neural network (DNN), and long short-term memory (LSTM) architectures. Results indicated that PITLDNN had better accuracy than PITLCNN and PITLLSTM models in predicting LAI (R2=0.94, RMSE = 0.32 m2/m2), LCC (R2=0.81, RMSE = 5.20 μg/cm2) and CCC (R2=0.928, RMSE = 0.2 g/m2). Moreover, PITLDNN demonstrated higher capability in computational efficiency, making it suitable for processing large volumes of UAV multispectral data in crop growth monitoring applications. Furthermore, PITL's integration of radiative transfer knowledge with labeled field data yielded higher predictive accuracy compared to physically-based inversion model, pure data-driven deep neural network approaches, and hybrid models. This study highlighted the performance of PITLDNN in accurately and efficiently quantifing biochemical traits from UAV multispectral data, thereby providing timely and accurate information for guiding crop growth monitoring applications.

Transient Electromagnetic Monitoring of the Cryolithozone: Mathematical Modeling

Abstract ––The paper presents theoretical results on mathematical modeling and numerical inversion of transient electromagnetic data for the monitoring of the state of the cryolithozone. Solutions to the direct and inverse problems are considered based on the vector finite element method, the Sumudu integral transform and the apparatus of artificial neural networks. We show the capability of spatial localization of thaw zones (taliks) with transient cross-borehole exploration systems in a geoelectric model that takes into account the dispersive properties of frozen rocks. The performance and accuracy of the developed algorithms are assessed.

Identification of Nearly Pure Leakoff Phase from Pressure Falloff Curve and Its Interpretation on Stimulated Fracture Areas

Summary Evaluation of fractures post-hydraulic fracturing is crucial for optimizing fracturing designs. Pressure falloff analysis methods, due to their cost-effectiveness and ease of data acquisition, have been widely used in oil fields. However, traditional pressure falloff analysis methods are based on the assumption of simple double-wing fractures and often overlook the impact of early-time abnormal leakoff behaviors. While some scholars have proposed pressure falloff equations for individual abnormal leakoff behavior, establishing pressure falloff models that consider the coupling of multiple abnormal leakoff behaviors is challenging, making the assessment of complex fracture networks difficult. This paper proposes a method that identifies the phase of nearly pure leakoff pressure falloff through continuous wavelet transform (CWT) and utilizes this phase for fracture inversion. Additionally, the fracture inversion model considers the differences in closure pressure and fracture compliance between main and secondary fractures. This method is applied to a shale horizontal well, and the results show that the inversion fracture areas are 20.3% smaller on average than the traditional method after removing the disturbance of abnormal leakoff behaviors. The new method is verified by the production data of each stage. The correlation between the main fracture area of new method and the production of each stage is better than that of the traditional method, and the correlation coefficient is 0.767. It is also found that the development degree of natural fractures affects the proportion of main and secondary fractures. In addition, it is found from the time-frequency diagram after CWT that the stages with higher secondary fracture proportions have more high-frequency components in the time-frequency diagram.

Forest Canopy Height Estimation Combining Dual-Polarization PolSAR and Spaceborne LiDAR Data

Forest canopy height data are fundamental parameters of forest structure and are critical for understanding terrestrial carbon stock, global carbon cycle dynamics and forest productivity. To address the limitations of retrieving forest canopy height using conventional PolInSAR-based methods, we proposed a method to estimate forest height by combining single-temporal polarimetric synthetic aperture radar (PolSAR) images with sparse spaceborne LiDAR (forest height) measurements. The core idea of our method is that volume scattering energy variations which are linked to forest canopy height occur during radar acquisition. Specifically, our methodology begins by employing a semi-empirical inversion model directly derived from the random volume over ground (RVoG) formulation to establish the relationship between forest canopy height, volume scattering energy and wave extinction. Subsequently, PolSAR decomposition techniques are used to extract canopy volume scattering energy. Additionally, machine learning is employed to generate a spatially continuous extinction coefficient product, utilizing sparse LiDAR samples for assistance. Finally, with the derived inversion model and the resulting model parameters (i.e., volume scattering power and extinction coefficient), forest canopy height can be estimated. The performance of the proposed forest height inversion method is illustrated with L-band NASA/JPL UAVSAR from AfriSAR data conducted over the Gabon Lope National Park and airborne LiDAR data. Compared to high-accuracy airborne LiDAR data, the obtained forest canopy height from the proposed approach exhibited higher accuracy (R2 = 0.92, RMSE = 6.09 m). The results demonstrate the potential and merit of the synergistic combination of PolSAR (volume scattering power) and sparse LiDAR (forest height) measurements for forest height estimation. Additionally, our approach achieves good performance in forest height estimation, with accuracy comparable to that of the multi-baseline PolInSAR-based inversion method (RMSE = 5.80 m), surpassing traditional PolSAR-based methods with an accuracy of 10.86 m. Given the simplicity and efficiency of the proposed method, it has the potential for large-scale forest height estimation applications when only single-temporal dual-polarization acquisitions are available.

A Method by using predefined windows to improve the InSAR atmospheric correction model based on GNSS ZTD and its horizontal delay gradient

ABSTRACT Correcting the signal delay caused by electromagnetic waves passing through the atmosphere is crucial in InSAR. Atmospheric delays are significant enough to obscure the true deformation signal, making it difficult for InSAR to detect deformation signals at the millimetre scale. Therefore, it is imperative to mitigate atmospheric interference appropriately. To interpolate sparse GNSS networks and capture small-scale atmospheric delays, this study presents an enhanced method for constructing an atmospheric correction model based on GNSS Zenith Total Delay (ZTD) and its horizontal gradient, which is integrated within a predefined window. To validate the effectiveness of the improved model, we applied it to 27 InSAR short-time and short-spatial baseline unwrapped interferograms. These interferograms are composed of images observed by the Sentinel-1A satellite from January to December 2022 in Southern California. The corrective impact of the enhanced model is also compared against that of the initial inversion model and GACOS to confirm the viability of the approach. Results of the evaluation, considering root mean square error (RMSE), standard deviation (STD), and the correlation between phase and elevation, indicate that our enhanced method reduces correction errors by 35.10%, 20.05%, and 63.52%, respectively, compared to GACOS. Compared to the initial model, these reductions are 58.19%, 57.23%, and 4.34%, respectively. By integrating the outcomes from all three evaluation methods and analysing the corrected images, our enhanced model effectively resolves the inconsistency in correction effects resulting from the spatial variability of atmospheric delay. Moreover, incorporating the horizontal gradient in the inversion method has proven effective in capturing small-scale atmospheric delay signals. This highlights the feasibility and benefits of our enhanced model.

Hybrid sea surface temperature inversion model for the South China sea based on IMLP and DBN

ABSTRACT Sea surface temperature (SST) is a key variable in the study of the global climate system and one of the important parameters in the process of air–sea interaction. Therefore, the demand for SST is developing towards high quality and high precision. In this paper, the ability of the multi-layer perceptron (MLP) optimized by the Aquila optimizer (AO) to solve nonlinear problems and the advantages of the deep belief network (DBN) to effectively process complex data are used to construct the IMLP-DBN inversion algorithm. The algorithm takes into account the influences of atmospheric conditions and satellite zenith angles. The data set is the infrared remote sensing data of the moderate resolution imaging spectroradiometer (MODIS) and the actual measurement data of buoys on sunny days and few clouds. Analysis of the inversion results shows that the root mean square error (RMSE) of the inversion value and the measured value is 0.14, and the sum of square errors (SSE) is 0.78. Compared with the MOD28 product data, the RMSE and SSE of the inversion are reduced by 66% and 24%, respectively.

Evaluating the Geo-Environmental Conditions within a Working Face Using a Hybrid Intelligent Optimization Model

Geological anomalies within the working face likely induce geological disasters, such as water, gas, and coal mine roof fall, directly impacting the rational planning and safe mining of underground resources. Constrained by the conditions of underground closed spaces, effective reconstruction under incomplete and highly sparse projection is the central challenge when evaluating geo-environmental conditions. This work proposes a new hybrid intelligent optimization model (MPGA-SIRT) that integrates a multiple-population genetic algorithm (MPGA) with the simultaneous iterative reconstruction technique (SIRT) to finely reconstruct the geo-environmental conditions within a working face based on electromagnetic wave tomography theory. MPGA-SIRT can provide a more precise initial inversion model for the conventional linear reconstruction technique of SIRT, incorporating a local search property by leveraging the robust global search capacity of MPGA. The advantages of MPGA-SIRT have been demonstrated through numerical modeling, theoretical testing, and engineering practices on the 8208 working face in the Datong mining area, Shanxi Province. In comparison to individual SIRT inversion models, MPGA-SIRT reconstruction yields more accurate and stable performance, as demonstrated by the evolution curve of the objective function and the corresponding convergence tomography results. Consequently, the geomagnetic wave absorption coefficient within the area of reconstruction can be precisely ascertained through the use of our proposed technique. This innovation represents a groundbreaking strategy for assessing geological anomaly zones within a working face. The introduced method stands as a valuable theoretical instrument for confronting the complexities associated with geo-environmental reconstruction in underground engineering.

Improved maize leaf area index inversion combining plant height corrected resampling size and random forest model using UAV images at fine scale

ContextAccurate monitoring of leaf area index (LAI) is conducive to timely and targeted management measures. Unmanned aerial vehicle (UAV) remote sensing provides an important way for non-destructive monitoring of crop leaf area index. ObjectiveIn this study, visible light (RGB) and multispectral remote sensing data from the UAV and ground-measured LAI data from the Plant Canopy Analyzer LAI-2200 C were used to conduct inversion of maize LAI on a fine scale. MethodsTo address the problem of spatial scale mismatch between the spatial resolution of UAV images and the ground-measured LAI, the scale difference between UAV image data and ground-measured data was reduced by removing the outermost ring data measured by the LAI-2200 C instrument, calculating the spatial resolution of the UAV images after resampling based on the height of the plant, and the resampling method based on the circle. Finally, through the above method to resample the UAV images, we extract the vegetation index and canopy height features as the input variables of the random forest model to build the maize LAI inversion model in vegetative stages and reproductive stages respectively, which is referred to as the Vis_H+RF method. Results and conclusionsThe Vis_H+RF method of Tongliao experimental station has an R2 of 0.96 in the vegetative stages and a R2 of 0.61 in the reproductive stages, both of which perform well and have certain migration capabilities. SignificanceThe LAI inversion model constructed based on the method in this study is basically consistent with the actual situation and can provide data support for maize growth monitoring.

Seismic travel-time tomography based on Ensemble Kalman Inversion

Abstract In this paper, we present a new seismic travel-time tomography approach that combines ensemble Kalman inversion (EKI) with Neural Networks (NNs) to facilitate the inference of complex underground velocity fields. Our methodology tackles the challenges of high-dimensional velocity models through an efficient neural network parameterization, enabling efficient training on coarse grids and accurate output on finer grids. This unique strategy, combined with a reduced-resolution forward solver, significantly enhances computational efficiency. Leveraging the robust capabilities of EKI, our method not only achieves rapid computations but also delivers informative uncertainty quantification for the estimated results. Through extensive numerical experiments, we demonstrate the exceptional accuracy and uncertainty quantification capabilities of our EKI-NNs approach. Even in the face of challenging geological scenarios, our method consistently generates valuable initial models for full wave inversion (FWI).

An Integrated Hydrogeophysical Approach for Unsaturated Zone Monitoring Using Time Domain Reflectometry, Electrical Resistivity Tomography and Ground Penetrating Radar

Continuous measurements of soil moisture in the deeper parts of the unsaturated zone remain an important challenge. This study examines the development of an integrated system for the continuous and 3-D monitoring of the vadose zone processes in a cost- and energy-efficient way. This system comprises TDR, ERT and GPR geophysical techniques. Their capacities to adequately image subsurface moisture changes with continuous and time-lapse measurements are assessed during an artificial infiltration experiment conducted in a characteristic urban site with anthropogenic fills and much compaction. A 3-D array was designed for each method to expand the information of a single TDR probe and obtain a broader image of the subsurface. Custom spatial TDR probes installed in boreholes made with a percussion drilling instrument were used for soil moisture measurements. Moisture profiles along the probes were estimated with a numerical one-dimensional inversion model and a standard calibration equation. High conductivity water used during all infiltration tests led to the detection of the flow by all techniques. Preferential flow was present throughout the experiment and imaged sufficiently by all methods. Overall, the integrated approach conceals each method’s weaknesses and provides a reliable 3-D view of the subsurface. The results suggest that this approach can be used to monitor the unsaturated zone at even greater depths.

Hyper-Parameter Optimization-based multi-source fusion for remote sensing inversion of non-photosensitive water quality parameters

ABSTRACT The constraints of spatiotemporal heterogeneity and spatial resolution constitute two crucial challenges in the establishment of remote sensing inversion models. Spatiotemporal heterogeneity gives rise to an inadequate generalization capacity of remote sensing models, demanding extensive manual parameter adjustment for each model construction. This not only escalates the task’s work intensity but also leads to unstable performance. The limited spatial resolution of remote sensing images leads to suboptimal inversion accuracy for sampling points influenced by mixed pixel effects. To tackle these problems, we take the case of non-photosensitive water quality parameter inversion in the narrow rivers of Longnan area. By integrating advanced Hyper-Parameter Optimization (HPO) techniques, such as Optuna from machine learning, an inversion model was developed, incorporating the bands of Sentinel-2 and Sentinel-3 as model features. Among these features, bands with lower spatial resolution are employed to furnish surrounding information, thereby enhancing the inversion accuracy. The research outcomes demonstrate that: 1) The model constructed based on the HPO method, Optuna, attained favourable inversion results, with R2 values of 0.68, 0.77, 0.35, and 0.60 for Total Nitrogen (TN), Total Phosphorus (TP), Ammonia Nitrogen (NH3-N), and Chemical Oxygen Demand (COD), respectively. 2) The fusion of Sentinel-2 and Sentinel-3 data enhanced the inversion accuracy compared to using them separately, highlighting the considerable significance of multi-source data fusion methods in improving inversion accuracy. This research fills a void in the remote sensing inversion domain and lays the groundwork for future endeavours.

Enhancing Remote Sensing Water Quality Inversion through Integration of Multisource Spatial Covariates: A Case Study of Hong Kong’s Coastal Nutrient Concentrations

The application of remote sensing technology for water quality monitoring has attracted much attention recently. Remote sensing inversion in coastal waters with complex hydrodynamics for non-optically active parameters such as total nitrogen (TN) and total phosphorus (TP) remains a challenge. Existing studies build the relationships between remote sensing spectral data and TN/TP directly or indirectly via the mediation of optically active parameters (e.g., total suspended solids). Such models are often prone to overfitting, performing well with the training set but underperforming with the testing set, even though both datasets are from the same region. Using the Hong Kong coastal region as a case study, we address this issue by incorporating spatial covariates such as hydrometeorological and locational variables as additional input features for machine learning-based inversion models. The proposed model effectively alleviates overfitting while maintaining a decent level of accuracy (R2 exceeding 0.7) during the training, validation and testing steps. The gap between model R2 values in training and testing sets is controlled within 7%. A bootstrap uncertainty analysis shows significantly improved model performance as compared to the model with only remote sensing inputs. We further employ the Shapely Additive Explanations (SHAP) analysis to explore each input’s contribution to the model prediction, verifying the important role of hydrometeorological and locational variables. Our results provide a new perspective for the development of remote sensing inversion models for TN and TP in similar coastal waters.

3-D parallel anisotropic inversion of controlled-source electromagnetic data using nested tetrahedral grids

SUMMARY Geophysicists today face the challenge of quickly and reliably interpreting extensive controlled-source electromagnetic (CSEM) data sets to map subsurface conductivity structures within realistic geological environments. An ideal 3-D CSEM inversion algorithm using tetrahedral grids should be capable of distinguishing different resolution requirements between forward modelling and inversion grids, have an optimal parallel strategy that fully exploits the inherent independence of CSEM data sets while also possessing the capability to handle large-scale geo-electrical models, and incorporate conductivity anisotropy which should be a common characteristic in realistic subsurface environments. However, existing tools in the geo-electromagnetic community often fall short of these three demands. Addressing this gap, our study introduces a scalable and parallel anisotropic inversion technique for CSEM data, capitalizing on the potential of unstructured tetrahedral grids. We first apply the tetrahedral longest-edge bisection method to create a refined dense, heterogeneous forward modelling grid from a coarse inversion grid. This refinement, focused on areas around transmitters and receivers, is seamlessly integrated within the coarser inversion grid’s topology, enabling precise conductivity mapping and preserving electromagnetic response accuracy during model updates. We further innovate with a source-mesh double-level parallel strategy, utilizing the message passing interface technique for parallel handling of independent CSEM data sets and large-scale geo-electrical models. Externally, we dedicate a processor for inversion model updates employing the Limited-memory Broyden–Fletcher–Goldfarb–Shanno optimization algorithm and divide other processors into groups, each associated with specific transmitting sources and frequencies. Internally, in each group, we employ a domain-decomposition-based scalable and robust iterative solvers using the Auxiliary-Space Maxwell pre-conditioner to parallel quickly calculate the electromagnetic responses from its assigned source-frequency set. Additionally, recognizing the potential for electrical conductivity anisotropy in field data, we incorporate the case of vertical transverse isotropy. We validate the effectiveness of our method through examples, including an isotropic land model with undulating topography, an anisotropic marine model and a real-field data case. Results from both synthetic and field data inversions underscore our method’s significant advancements in efficiency and practicality, particularly in addressing large-scale 3-D CSEM data sets inversion challenges in realistic geological environments.

Ecological footprint in Beijing-Tianjin-Hebei urban agglomeration: Evolution characteristics, driving mechanism, and compensation standard

Unbalancing ecological supply-demand is an obstacle to sustainable development in the Beijing-Tianjin-Hebei urban agglomeration (BTHUA), which can be efficiently addressed through regional ecological compensation. However, the driving mechanism of this unbalance is unclear, and the determination of ecological compensation standard (ECS) is not unified. This study uses the ecological footprint (EF) model and integrated inversion model to complete the intelligent evaluation and prediction of ecological supply-demand performance in the BTHUA. The extended nonlinear STIRPAT model and geographic detector are integrated to identify the driving mechanism of EF and its spatial differentiation. ECS at different spatiotemporal scales are then given with consideration of ecological service value and Monte Carlo simulation. Results reveal that EF of the BTHUA exhibits a downward trend, especially under the sustainable scenario (SSP1), falling by 16.85 %. But all cities would be still in unsafety states by 2035, with an average EF value of 6.210 hm2/cap, which is over 27 times greater than its EC. The spatial distribution of EF differs significantly, and high-value areas gradually migrate to the north. The variation of EF is primarily influenced by industrial structure, with population factors and environmental factors following. Per capita GDP is a key factor causing spatial differentiation of EF. The key driving factors and their explanatory powers on EF vary across cities, and the interaction of multiple factors affects EF more than a single in the BTHUA. Past-to-future ECS of the BTHUA shows a decreasing trend, with high-value areas distributed in the pivotal cities for economic development. A total of 19.13 × 1010 CNY needs to be paid to compensate for ecological damage of the BTHUA. Uncertainty analysis shows that ECS is extremely sensitive to grassland footprint in most cities. Furthermore, environmental footprints as well as system dynamics considering multiple factors are still required for comprehensively evaluating the ecological environment quality and further exploring ecological compensation evaluation framework with incorporating various ecological service functions such as carbon sinks in the BTHUA. Findings can facilitate improving regional sustainability and provide a valid approach to determine ECS for the BTHUA and similar regions worldwide.

Integrated UAV and Satellite Multi-Spectral for Agricultural Drought Monitoring of Winter Wheat in the Seedling Stage.

Agricultural droughts are a threat to local economies, as they disrupt crops. The monitoring of agricultural droughts is of practical significance for mitigating loss. Even though satellite data have been extensively used in agricultural studies, realizing wide-range, high-resolution, and high-precision agricultural drought monitoring is still difficult. This study combined the high spatial resolution of unmanned aerial vehicle (UAV) remote sensing with the wide-range monitoring capability of Landsat-8 and employed the local average method for upscaling to match the remote sensing images of the UAVs with satellite images. Based on the measured ground data, this study employed two machine learning algorithms, namely, random forest (RF) and eXtreme Gradient Boosting (XGBoost1.5.1), to establish the inversion models for the relative soil moisture. The results showed that the XGBoost model achieved a higher accuracy for different soil depths. For a soil depth of 0-20 cm, the XGBoost model achieved the optimal result (R2 = 0.6863; root mean square error (RMSE) = 3.882%). Compared with the corresponding model for soil depth before the upscaling correction, the UAV correction can significantly improve the inversion accuracy of the relative soil moisture according to satellite remote sensing. To conclude, a map of the agricultural drought grade of winter wheat in the Huaibei Plain in China was drawn up.

One Billion Years of Stability in the North American Midcontinent Following Two‐Stage Grenvillian Structural Inversion

AbstractThe North American craton interior preserves a >1 Ga history of near surface processes that inform ongoing debates regarding timing and drivers of continental‐scale deformation and erosion associated with far‐field orogenesis. We tested various models of structural inversion on a major segment of the Midcontinent Rift along the Douglas Fault (DF) in northern Wisconsin, which accommodated ≳10 km of total vertical displacement. U‐Pb detrital zircon and vein calcite Δ47/U‐Pb thermochronometry from the hanging wall constrain the majority of uplift (≳8.5 km) and deformation to 1052–1036 Ma during the Ottawan phase of the Grenvillian orogeny. Combined U‐Pb zircon dates, Δ47/U‐Pb calcite thermochronometry, and field data that document syn‐ to early post‐depositional deformation in the footwall constrain a second stage of uplift (1–1.5 km) ca. 995–980 Ma during the Rigolet phase of the Grenvillian orogeny. A minor phase of Appalachian far‐field orogenesis is associated with minimal thrust reactivation. Our combined analyses identified the 995–980 Ma Bayfield Group as a Grenvillian foreland basin with an original thickness 0.5–2 km greater than currently preserved. By quantifying flexural loading and other subsidence mechanisms along the Douglas Fault, we identify dynamic subsidence as a mechanism that could be consistent with the development of late‐Grenvillian transcontinental fluvial systems. Minimal post‐Grenvillian erosion (0.5–2 km) in this part of the craton interior has preserved the Bayfield Group and equivalent successions, limiting the magnitude of regional erosion that can be attributed to Neoproterozoic glaciation.

An efficient method for estimating tropical forest canopy height from airborne PolInSAR data

The study on PolInSAR forest canopy height(FCH) inversion is an essential branch within the SAR field, where the temporal decorrelation(TD) inherent in the interferometric complex coherence of repeat-pass Polarization Interferometric Synthetic Aperture Radar (PolInSAR) data significantly impacts the precision of FCH estimation.This study is grounded in airborne multi-baseline PolInSAR data, employing Random Volume over Ground (RVoG) + VTD and Random Motion over Ground(RMoG) as the FCH inversion models. The classical RVoG three-stage method(TS) process was used to simplify the solution of RVoG+VTD and RMoG, and a novel, more efficient method was attempted to further streamline the solution of the RVoG+VTD model(RVoG+VTDs). The results demonstrate that the RVoG+VTDs model obtains the most satisfactory results, and the efficiency of the RVoG+VTDs model is 23 times more efficient than that of the RVoG+VTD; 19 times more efficient than that of the RMoG model, and 22 times more efficient than that of the RVoG model, respectively; and the inversion time consumed by the four models is 1.5 min, 35 min, 28 min, and 33 min, respectively. In the accuracy validation, the estimation accuracy of RVoG+VTDs still keeps the best accuracy, with corresponding R2 values for the four methods (RVoG+VTDs, RVoG+VTD, RMoG, and RVoG) equal to 0.813, 0.774, 0.804, and 0.770, respectively. The RMSE values are 6.626 m, 7.277 m, 6.774 m, and 7.335 m.The study’s conclusions illustrate the feasibility of solving the RVoG+VTD and RMoG model by fixing the extinction coefficients in the model, with RVoG+VTDs taking precedence over the model selection, which can provide a valuable reference for the application of spaceborne PolInSAR data.

Detection of Rice Leaf SPAD and Blast Disease Using Integrated Aerial and Ground Multiscale Canopy Reflectance Spectroscopy

Rice blast disease is one of the major diseases affecting rice plant, significantly impacting both yield and quality. Current detecting methods for rice blast disease mainly rely on manual surveys in the field and laboratory tests, which are inefficient, inaccurate, and limited in scale. Spectral and imaging technologies in the visible and near-infrared (Vis/NIR) region have been widely investigated for crop disease detection. This work explored the potential of integrating canopy reflectance spectra acquired near the ground and aerial multispectral images captured with an unmanned aerial vehicle (UAV) for estimating Soil-Plant Analysis Development (SPAD) values and detecting rice leaf blast disease in the field. Canopy reflectance spectra were preprocessed, followed by effective band selection. Different vegetation indices (VIs) were calculated from multispectral images and selected for model establishment according to their correlation with SPAD values and disease severity. The full-wavelength canopy spectra (450–850 nm) were first used for establishing SPAD inversion and blast disease classification models, demonstrating the effectiveness of Vis/NIR spectroscopy for SPAD inversion and blast disease detection. Then, selected effective bands from the canopy spectra, UAV VIs, and the fusion of the two data sources were used for establishing corresponding models. The results showed that all SPAD inversion models and disease classification models established with the integrated data performed better than corresponding models established with the single of either of the aerial and ground data sources. For SPAD inversion models, the best model based on a single data source achieved a validation determination coefficient (Rcv2) of 0.5719 and a validation root mean square error (RMSECV) of 2.8794, while after ground and aerial data fusion, these two values improved to 0.6476 and 2.6207, respectively. For blast disease classification models, the best model based on a single data source achieved an overall test accuracy of 89.01% and a Kappa coefficient of 0.86, and after data fusion, the two values improved to 96.37% and 0.95, respectively. These results indicated the significant potential of integrating canopy reflectance spectra and UAV multispectral images for detecting rice diseases in large fields.

An inter-satellite laser occultation method profiling atmospheric temperature and pressure from troposphere to lower mesosphere

We propose a laser occultation method for simultaneous profiling atmospheric temperature and pressure. Measurements can be performed on the optical link between two low-orbit satellites, where frequency-stepwise laser pulses are transmitted from one to the other. These pulses, covering several oxygen absorption lines in the wavelength domain, measure the broadened atmospheric absorption optical depth along the transmission path with a spectral resolution of tens of megahertz. In this way, atmospheric temperature and pressure are obtained by analysing the retrieved shape and intensity of the spectral lines. With the motion of the two satellites, the inter-satellite optical link penetrates different atmospheric layers at various altitudes, enabling the measurement of the vertical structure of atmospheric thermodynamic parameters from the troposphere to the lower thermosphere. This paper presents an end-to-end simulation of the proposed method, including models for laser occultation beam tracing, radiative transfer, and data inversion. The simulation results reveal that with minimal satellite payload resources, this method can accurately measure temperature and pressure at a vertical resolution of 100 m from 5 km to 90 km altitude with accuracies of ±1.5 K and 5 %, respectively. As the proposed differential absorption laser occultation method is independent of the hydrostatic equilibrium hypothesis for data inversion, it can eliminate errors associated with prior data at reference altitudes. It is believed that our method has provided a promising approach to laser satellite constellation for atmospheric observation.

A Multi-Baseline Forest Height Estimation Method Combining Analytic and Geometric Expression of the RVoG Model

As an important parameter of forest biomass, forest height is of great significance for the calculation of forest carbon stock and the study of the carbon cycle in large-scale regions. The main idea of the current forest height inversion methods using multi-baseline P-band polarimetric interferometric synthetic aperture radar (PolInSAR) data is to select the best baseline for forest height inversion. However, the approach of selecting the optimal baseline for forest height inversion results in the process of forest height inversion being unable to fully utilize the abundant observation data. In this paper, to solve the problem, we propose a multi-baseline forest height inversion method combining analytic and geometric expression of the random volume over ground (RVoG) model, which takes into account the advantages of the selection of the optimal observation baseline and the utilization of multi-baseline information. In this approach, for any related pixel, an optimal baseline is selected according to the geometric structure of the coherence region shape and the functional model for forest height inversion is established by the RVoG model’s analytic expression. In this way, the other baseline observations are transformed into a constraint condition according to the RVoG model’s geometric expression and are also involved in the forest height inversion. PolInSAR data were used to validate the proposed multi-baseline forest height inversion method. The results show that the accuracy of the forest height inversion with the algorithm proposed in this paper in a coniferous forest area and tropical rainforest area was improved by 17% and 39%, respectively. The method proposed in this paper provides a multi-baseline PolInSAR forest height inversion scheme for exploring regional high-precision forest height distribution. The scheme is an applicable method for large-scale, high-precision forest height inversion tasks.