Hyperspectral Remote Sensing Research Articles

Research on the Quantitative Inversion of Soil Iron Oxide Content Using Hyperspectral Remote Sensing and Machine Learning Algorithms in the Lufeng Annular Structural Area of Yunnan, China.

This study used hyperspectral remote sensing to rapidly, economically, and non-destructively determine the soil iron oxide content of the Dinosaur Valley annular tectonic region of Lufeng, Yunnan Province. The laboratory determined the iron oxide content and original spectral reflectance (OR) in 138 surface soil samples. We first subjected the OR data to Savizky-Golay smoothing, followed by four spectral transformations-continuum removal reflectance, reciprocal logarithm reflectance, standard normal variate reflectance, and first-order differential reflectance-which improved the signal-to-noise ratio of the spectral curves and highlighted the spectral features. Then, we combined the correlation coefficient method (CC), competitive adaptive reweighting algorithm, and Boruta algorithm to screen out the characteristic wavelength. From this, we constructed the linear partial least squares regression model, nonlinear random forest, and XGBoost machine learning algorithms. The results show that the CC-Boruta method can effectively remove any noise and irrelevant information to improve the model's accuracy and stability. The XGBoost nonlinear machine learning algorithm model better captures the complex nonlinear relationship between the spectra and iron oxide content, thus improving its accuracy. This provides a relevant reference for the rapid and accurate inversion of iron oxide content in soil using hyperspectral data.

Dynamics of phytoplankton communities in the Baltic Sea: insights from a multi-dimensional analysis of pigment and spectral data—part I, pigment dataset

This study aims to investigate the seasonal and spatial distribution of surface phytoplankton communities in the Baltic Sea, using pigment analysis and hydrological parameters. Data were collected during six oceanographic campaigns between 2005 and 2008, including high-performance liquid chromatography (HPLC) pigment characterization and hydrological measurements. The first part of this comprehensive study was focused on the HPLC phytoplankton pigment dataset in relation to hydrological conditions. The research highlighted the importance of high-quality input data for accurate taxonomic analysis. Several unsupervised machine learning approaches, such as hierarchical cluster analysis (HCA), principal component analysis (PCA), and network-based community detection analysis (NCA), were used to analyze the data and identify phytoplankton communities based on biomarker pigments. Five main phytoplankton communities were identified: diatoms, dinoflagellates, cryptophytes, green algae, and cyanobacteria. The results evidenced distinct seasonal patterns, with diatom blooms dominating in spring, cyanobacterial blooms in mid-summer, and haptophyte and dinoflagellate peaks occurring in late summer and autumn. While PCA and NCA provided consistent insights into community structure, HCA offered less clarity in distinguishing between groups. The results of the statistical analysis were then compared with those of traditional approaches such as CHEMTAX and region-specific bio-optical algorithms, providing new perspectives on the taxonomic composition of phytoplankton groups. This study provides valuable insights into phytoplankton dynamics in the Baltic Sea and the effectiveness of different analytical approaches in understanding community structure, providing metrics that can enhance current and future advancements in remote sensing, including support for hyperspectral ocean color remote sensors.

Assessment of Water Content in Plant Leaves Using Hyperspectral Remote Sensing and Chemometrics, Application: Rosmarinus officinalis

Assessment of Water Content in Plant Leaves Using Hyperspectral Remote Sensing and Chemometrics, Application: Rosmarinus officinalis

Spatial–Spectral Hypergraph-Based Unsupervised Band Selection for Hyperspectral Remote Sensing Images

Spatial–Spectral Hypergraph-Based Unsupervised Band Selection for Hyperspectral Remote Sensing Images

Monitoring the Nitrogen Nutrition Index Using Leaf-Based Hyperspectral Reflectance in Cut Chrysanthemums

Precise nitrogen supply is crucial for ensuring the quality of cut chrysanthemums (Chrysanthemum morifolium Ramat.). The nitrogen nutrition index (NNI) serves as an important indicator for diagnosing crop nitrogen (N) nutrition. Hyperspectral remote sensing (HRS) technology has been widely used in monitoring crop N status due to its rapid, accurate, and non-destructive capabilities. However, its application in estimating the NNI of cut chrysanthemums has received limited attention. Therefore, this study aimed to use HRS to accurately determine the cut chrysanthemum NNI, thereby providing valuable guidance for managing N fertilization. During several key growth stages, a hyperspectral spectroradiometer was used to capture hyperspectral reflectance data (350–2500 nm) from three leaf layers. Subsequently, cut chrysanthemum canopies were sampled for aboveground biomass (AGB) and plant nitrogen concentration (PNC). The collected AGB and PNC data were then utilized to fit the critical N (Nc) dilution curve of cut chrysanthemums using a Bayesian hierarchical model, enabling the calculation of the NNI. Finally, spectral indices and partial least squares regression (PLSR) were used to establish the NNI estimation model for cut chrysanthemums. The results showed that the Nc dilution curve of the cut chrysanthemums was Nc = 5.401 × AGB−0.468. The first leaf layer (L1) proved to be optimal for estimating cut chrysanthemum NNI. Additionally, a newly proposed two-band spectral index, DVI-L1 (R1105, R700), demonstrated moderate predictive capabilities for the NNI of cut chrysanthemums (R2 = 0.5309, RMSE = 0.3210). Compared with the spectral index-based NNI estimation model, PLSR-L1 showed the best performance in estimating the cut chrysanthemum NNI (R2 = 0.8177, RMSE = 0.2000). Our results highlight the rapid NNI prediction potential of HRS and its significance in facilitating precise N management in cut chrysanthemums.

Applications of Hyperspectral Remote Sensing, GIS, and Artificial Intelligence in Agriculture

Applications of Hyperspectral Remote Sensing, GIS, and Artificial Intelligence in Agriculture

Modelling Water Depth, Turbidity and Chlorophyll Using Airborne Hyperspectral Remote Sensing in a Restored Pond Complex of Doñana National Park (Spain)

Restored wetlands should be closely monitored to fully evaluate the effectiveness of restoration efforts. However, regular post-restoration monitoring can be time-consuming and expensive, and is often absent or inadequate. Satellite and airborne remote sensing systems have proven to be cost-effective tools in many fields, but they have not been widely used to monitor ecological restoration. This study assessed the potential of airborne hyperspectral remote sensing to monitor water mass characteristics of experimental temporary ponds in the Mediterranean region. These ponds were created during marsh restoration in Doñana National Park (south-west Spain). We used hyperspectral images acquired by the CASI-1500 hyperspectral airborne sensor to estimate and map water depth, turbidity and chlorophyll a in a subset of the 96 new ponds. The high spatial and spectral resolution of the CASI sensor allowed us to detect differences between ponds in water depth, turbidity and chlorophyll a, providing accurate mapping of these three variables, and a useful method to assess restoration success. High levels of spatial variation were recorded between different ponds, which likely generates high diversity in the animal and plant species that they contain. These results highlight the great potential of hyperspectral sensors for the long-term monitoring of wetland complexes in the Mediterranean region and elsewhere.

Customised Octacopter Drone-based Magnetic Survey for Exploration of Mn-Fe ores in Sidhan Area, Jabalpur District, Madhya Pradesh, India

Encouraged by Digital Initiatives of the Government of India and NMDC’s Version 2.0 plans, we have carried out Magnetic Survey and Hyperspectral Remote Sensing using Unmanned Aerial System (UAS) for the first time in India for Mineral Exploration. This paper briefly presents the findings of the Unmanned Aerial Vehicle (UAV; Customised Octacopter Drone) magnetic survey to explore Mn-Fe ores in Sidhan Block, Jabalpur District, Madhya Pradesh, India. The specifications of the UAV, flight parameters and magnetometer used for mineral exploration are also discussed in this paper.

Special Issue Introduction: Ushering a New Era of Hyperspectral Remote Sensing to Advance Remote Sensing Science in the Twenty-first Century

Special Issue Introduction: Ushering a New Era of Hyperspectral Remote Sensing to Advance Remote Sensing Science in the Twenty-first Century

Predicting Grapevine Physiological Parameters Using Hyperspectral Remote Sensing Integrated with Hybrid Convolutional Neural Network and Ensemble Stacked Regression

Grapevine rootstocks are gaining importance in viticulture as a strategy to combat abiotic challenges, as well as enhance scion physiology. Direct leaf-level physiological parameters like net assimilation rate, stomatal conductance to water vapor, quantum yield of PSII, and transpiration can illuminate the rootstock effect on scion physiology. However, these measures are time-consuming and limited to leaf-level analysis. This study used different rootstocks to investigate the potential application of aerial hyperspectral imagery in the estimation of canopy level measurements. A statistical framework was developed as an ensemble stacked regression (REGST) that aggregated five different individual machine learning algorithms: Least absolute shrinkage and selection operator (Lasso), Partial least squares regression (PLSR), Ridge regression (RR), Elastic net (ENET), and Principal component regression (PCR) to optimize high-throughput assessment of vine physiology. In addition, a Convolutional Neural Network (CNN) algorithm was integrated into an existing REGST, forming a hybrid CNN-REGST model with the aim of capturing patterns from the hyperspectral signal. Based on the findings, the performance of individual base models exhibited variable prediction accuracies. In most cases, Ridge Regression (RR) demonstrated the lowest test Root Mean Squared Error (RMSE). The ensemble stacked regression model (REGST) outperformed the individual machine learning algorithms with an increase in R2 by (0.03 to 0.1). The performances of CNN-REGST and REGST were similar in estimating the four different traits. Overall, these models were able to explain approximately 55–67% of the variation in the actual ground-truth data. This study suggests that hyperspectral features integrated with powerful AI approaches show great potential in tracing functional traits in grapevines.

JointNet: Multitask Learning Framework for Denoising and Detecting Anomalies in Hyperspectral Remote Sensing

One of the significant challenges with traditional single-task learning-based anomaly detection using noisy hyperspectral images (HSIs) is the loss of anomaly targets during denoising, especially when the noise and anomaly targets are similar. This issue significantly affects the detection accuracy. To address this problem, this paper proposes a multitask learning (MTL)-based method for detecting anomalies in noisy HSIs. Firstly, a preliminary detection approach based on the JointNet model, which decomposes the noisy HSI into a pure background and a noise–anomaly target mixing component, is introduced. This approach integrates the minimum noise fraction rotation (MNF) algorithm into an autoencoder (AE), effectively isolating the noise while retaining critical features for anomaly detection. Building upon this, the JointNet model is further optimized to ensure that the noise information is shared between the denoising and anomaly detection subtasks, preserving the integrity of the training data during the anomaly detection process and resolving the issue of losing anomaly targets during denoising. A novel loss function is designed to enable the joint learning of both subtasks under the multitask learning model. In addition, a noise score evaluation metric is introduced to calculate the probability of a pixel being an anomaly target, allowing for a clear distinction between noise and anomaly targets, thus providing the final anomaly detection results. The effectiveness of the proposed model and method is validated via testing on the HYDICE and San Diego datasets. The denoising metric results of the PSNR, SSIM, and SAM are 41.79, 0.91, and 4.350 and 42.83, 0.93, and 3.558 on the HYDICE and San Diego datasets, respectively. The anomaly detection ACU is 0.943 and 0.959, respectively. The proposed method outperforms the other algorithms, demonstrating that the reconstructed images using this method exhibited lower noise levels and more complete image information, and the JointNet model outperforms the mainstream HSI anomaly detection algorithms in both the quantitative evaluation and visual effect, showcasing its improved detection capabilities.

Spectrum Analysis and Detection of Ships Based on Aerial Hyperspectral Remote Sensing Experiments

Spectrum Analysis and Detection of Ships Based on Aerial Hyperspectral Remote Sensing Experiments

Optical Design of a Hyperspectral Remote-Sensing System Based on an Image-Slicer Integral Field Unit in the Short-Wave Infrared Band.

Grating-type spectral imaging systems are frequently employed in scenes for high-resolution remote-sensing observations of the Earth. However, the entrance of the grating-type spectral imaging system is a slit or a pinhole. This structure relies on the push broom method, which presents a challenge in capturing spectral information of transiently changing targets. To address this issue, the IFU is used to slice the focal plane of the telescope system, thereby expanding the instantaneous field of view (IFOV) of the grating-type spectral imaging system. The aberrations introduced by the expansion of the single-slice field of view (FOV) of the IFU are corrected, and the conversion of the IFU's FOV from arcseconds to degrees is achieved. The design of a spectral imaging system based on an image-slicer IFU for remote sensing is finally completed. The system has a wavelength range of 1400 nm to 2000 nm, and a spectral resolution of better than 3 nm. Compared with the traditional grating-type spectral imaging system, its IFOV is expanded by a factor of four. And it allows for the capture of complete spectral information of transiently changing targets through a single exposure. The simulation results demonstrate that the system has good performance at each sub-slit, thereby validating the effectiveness and advantages of the proposed system for dynamic target capture in remote sensing.

Design and Application of Bionic Camouflage Materials Simulating Spectral Reflection Characteristics of Plants: A Review

Hyperspectral remote sensing (RS) has rapidly developed in recent years and has been widely used in the military field. This technology not only brings huge opportunities for military reconnaissance but also poses unprecedented challenges to military camouflage, severely complicating the development of plant hyperspectral camouflage materials and technology. In this review, the spectral reflectance characteristics of plants and the application of hyperspectral RS in plant RS and military operations are reviewed. The development status of bionic camouflage materials that simulate the spectral reflection characteristics of plants is analyzed. With the existing hyperspectral camouflage materials and technology, bionic camouflage technology is limited by the inability of bionic materials to accurately imitate the characteristic absorption peaks of green vegetation, low stability and durability, and the large overall material thickness, which complicate actual large-scale application. On this basis, a future development direction and a trend of plant hyperspectral bionic camouflage materials and technology are proposed.

Improving the Spatiotemporal Transferability of Hyperspectral Remote Sensing for Estimating Soil Organic Matter by Minimizing the Coupling Effect of Soil Physical Properties on the Spectrum: A Case Study in Northeast China

Soil organic matter (SOM) is important for the global carbon cycle, and hyperspectral remote sensing has proven to be a promising method for fast SOM content estimation. However, because of the neglect of the spectral response of soil physical properties, the accuracy and spatiotemporal transferability of the SOM prediction model are poor. This study aims to improve the spatiotemporal transferability of the SOM prediction model by alleviating the coupling effect of soil physical properties on spectra. Based on satellite hyperspectral images and soil physical variables, including soil moisture (SM), soil surface roughness (root-mean-square height, RMSH), and soil bulk weight (SBW), a soil spectral correction model was established based on the information unmixing method. Two important grain-producing areas in Northeast China were selected as study areas to verify the performance and transferability of the spectral correction model and SOM content prediction model. The results showed that soil spectral corrections based on fourth-order polynomials and the XG-Boost algorithm had excellent accuracy and generalization ability, with residual predictive deviations (RPDs) exceeding 1.4 in almost all the bands. In addition, when the soil spectral correction strategy was adopted, the accuracy of the SOM prediction model and the generalization ability after the model migration were significantly improved. The SOM prediction accuracy based on the XG-Boost-corrected spectrum was the highest, with a coefficient of determination (R2) of 0.76, a root-mean-square error (RMSE) of 5.74 g/kg, and an RPD of 1.68. The prediction accuracy, R2 value, RMSE, and RPD of the model after the migration were 0.72, 6.71 g/kg, and 1.53, respectively. Compared with the direct migration prediction of the model, adopting the soil spectral correction model based on fourth-order polynomials and XG-Boost reduced the RMSE of the SOM prediction results by 57.90% and 60.27%, respectively. This performance comparison highlighted the advantages for considering soil physical properties in regional-scale SOM predictions.

3U CubeSat-Based Hyperspectral Remote Sensing by Offner Imaging Hyperspectrometer with Radially-Fastened Primary Elements.

This paper presents findings from a spaceborne Earth observation experiment utilizing a novel, ultra-compact hyperspectral imaging camera aboard a 3U CubeSat. Leveraging the Offner optical scheme, the camera's hyperspectrometer captures hyperspectral images of terrestrial regions with a 200 m spatial resolution and 12 nanometer spectral resolution across a 400 to 1000 nanometer wavelength range, covering 150 channels in the visible and near-infrared spectrums. The hyperspectrometer is specifically designed for deployment on a 3U CubeSat nanosatellite platform, featuring a robust all-metal cylindrical body of the hyperspectrometer, and a coaxial arrangement of the optical elements ensures optimal compactness and vibration stability. The performance of the imaging hyperspectrometer was rigorously evaluated through numerical simulations prior to construction. Analysis of hyperspectral data acquired over a year-long orbital operation demonstrates the 3U CubeSat's ability to produce various vegetation indices, including the normalized difference vegetation index (NDVI). A comparative study with the European Space Agency's Sentinel-2 L2A data shows a strong agreement at critical points, confirming the 3U CubeSat's suitability for hyperspectral imaging in the visible and near-infrared spectrums. Notably, the ISOI 3U CubeSat can generate unique index images beyond the reach of Sentinel-2 L2A, underscoring its potential for advancing remote sensing applications.

Seasonal Monitoring Method for TN and TP Based on Airborne Hyperspectral Remote Sensing Images

Airborne sensing images harness the combined advantages of hyperspectral and high spatial resolution, offering precise monitoring methods for local-scale water quality parameters in small water bodies. This study employs airborne hyperspectral remote sensing image data to explore remote sensing estimation methods for total nitrogen (TN) and total phosphorus (TP) concentrations in Lake Dianshan, Yuandang, as well as its main inflow and outflow rivers. Our findings reveal the following: (1) Spectral bands between 700 and 750 nm show the highest correlation with TN and TP concentrations during the summer and autumn seasons. Spectral reflectance bands exhibit greater sensitivity to TN and TP concentrations compared to the winter and spring seasons. (2) Seasonal models developed using the Catboost method demonstrate significantly higher accuracy than other machine learning (ML) models. On the test set, the root mean square errors (RMSEs) are 0.6 mg/L for TN and 0.05 mg/L for TP concentrations, with average absolute percentage errors (MAPEs) of 23.77% and 25.14%, respectively. (3) Spatial distribution maps of the retrieved TN and TP concentrations indicate their dependence on exogenous inputs and close association with algal blooms. Higher TN and TP concentrations are observed near the inlet (Jishui Port), with reductions near the outlet (Lanlu Port), particularly for the TP concentration. Areas with intense algal blooms near shorelines generally exhibit higher TN and TP concentrations. This study offers valuable insights for processing small water bodies using airborne hyperspectral remote sensing images and provides reliable remote sensing techniques for lake water quality monitoring and management.

Comparison between Hyperspectral and Multispectral Retrievals of Suspended Sediment Concentration in Rivers

Remote sensing (RS) is often employed to estimate suspended sediment concentration (SSC) in rivers, and the availability of hyperspectral imagery enhances the effectiveness of RS-based water quality monitoring due to its high spectral resolution. Yet, the necessity of hyperspectral imagery for SSC estimation in rivers has not been fully validated. This study thus compares the performance of hyperspectral RS with that of multispectral RS by conducting field-scale experiments in shallow rivers. In the field experiments, we measured radiance from a water body mixed with suspended sediments using a drone-mounted hyperspectral sensor, with the sediment and riverbed types considered as controlling factors. We retrieved the SSC from UAV imagery using an optimal band ratio analysis, which successfully estimated SSC distributions in the sand bed conditions with both multispectral and hyperspectral data. In the vegetated bed conditions, meanwhile, the prediction accuracy decreased significantly due to the temporally varying bottom reflectance associated with the random movement of vegetation caused by near-bed turbulence. This is because temporally inhomogeneous bottom reflectance distorts the relationship between the SSC and total reflectance. Nevertheless, the hyperspectral imaging exhibited better prediction accuracy than the multispectral imaging, effectively extracting optimal spectral bands sensitive to back-scattered reflectance from sediments while constraining the bottom reflectance caused by the vegetation-covered bed.

Preflight Spectral Calibration of the Ozone Monitoring Suite-Nadir on FengYun 3F Satellite

The Ozone Monitoring Suite-Nadir (OMS-N) instrument is the first hyperspectral remote sensor in the ultraviolet band of China’s Fengyun series satellites. It can be used to detect several kinds of atmospheric constituents. This paper describes the prelaunch spectral calibration of the OMS-N onboard FengYun 3F. Several critical spectral parameters including the spectral resolution, spectral dispersion, and the instrument spectral response function were determined through laser-based measurements. A secondary peak of the instrument spectral response function from the short wavelength side of the ultraviolet band was found, and the possible influence on data applications was analyzed using a reference solar model and radiative transfer model. The results indicate that the spectral resolution and spectral accuracy of OMS-N meet the mission requirements. However, the asymmetries in the instrument spectral response function in the ultraviolet band were found near nadir rows, which are expressed as the “asymmetric central peak” and “secondary peak”. The analysis results show that if the influences of the instrument spectral response function “asymmetric central peak” and “secondary peak” in the ultraviolet band are ignored, they will bring an error as large as 5% at the center of the absorption line.

Detailed Land Use Classification in a Rare Earth Mining Area Using Hyperspectral Remote Sensing Data for Sustainable Agricultural Development

In China, ion-adsorbing rare earth minerals are mainly located in the southern hilly areas and are important strategic resources. Extensive long-term mining has severely damaged the land cover in mining areas, caused soil pollution and terrain fragmentation, disrupted the balance between mining and agriculture, severely restricted agricultural development, and affected ecological development. Precise and detailed classification of land use within mining areas is crucial for monitoring the sustainable development of agricultural ecology in these areas. In this study, we leverage the high spatial and high spectral resolution characteristics of the Zhuhai-1 (OHS) hyperspectral image datasets. We create four types of datasets based on spectral, vegetation, red edge, and texture characteristics. These datasets are optimized for multifaceted features, considering the complex land use scenario in rare earth mining areas. Additionally, we design seven optimal combination schemes for features. This is performed to examine the impact of different schemes on land use classification in rare earth mining areas and the accuracy of identifying agricultural land classes from broken blocks. The results show that (1) the inclusion of texture features has the most obvious effect on the overall classification accuracy; (2) the red edge feature has the worst effect on improving the overall accuracy of the surface classification; however, it has a prominent effect on the identification of agricultural lands such as farmland, orchards, and reclaimed vegetation; and (3), following the combination of various optimization features, the land use classification yielded the highest overall accuracy, at 88.16%. Furthermore, the comprehensive identification of various agricultural land classes, including farmland, orchards, and greenhouse vegetables, yielded the most desirable outcomes. The research results not only highlight the advantages of hyperspectral images for complex terrain classification and recognition but also address the previous limitations in the application of hyperspectral datasets over wide mining areas. Additionally, the results underscore the reliability of feature selection methods in reducing information redundancy and improving classification accuracy. The proposed feature selection combination, based on OHS hyperspectral datasets, offers technical support and guidance for the detailed classification of complex land use in mining areas and the accurate monitoring of agroecological environments.