Hyperspectral Reflectance Data Research Articles

Tracking Water Quality and Macrophyte Changes in Lake Trasimeno (Italy) from Spaceborne Hyperspectral Imagery

This work aims to show the potential of imaging spectroscopy in assessing water quality and aquatic vegetation in Lake Trasimeno, Italy. Hyperspectral reflectance data from the PRISMA, DESIS and EnMAP missions (2019–2022, summer periods) were compared with in situ measurements from WISPStation and used as inputs for water quality product generation algorithms. The bio-optical model BOMBER was run to simultaneously retrieve water quality parameters (Chlorophyll-a (Chl-a) and Total Suspended Matter, (TSM)) and the coverage of submerged and emergent macrophytes (SM, EM); value-added products, such as Phycocyanin concentration maps, were generated through a machine learning approach. The results showed radiometric agreement between satellite and in situ data, with R2 > 0.9, a Spectral Angle < 10° and water quality mapping errors < 30%. Both SM and EM coverage varied significantly from 2019 (135 ha, 0 ha, respectively) to 2022 (2672 ha, 343 ha), likely influenced by changes in rainfall and lake levels. The areas of greatest variability in Chl-a and TSM were identified in the littoral zones in the western side of the lake, while the highest variation in the fractional cover of SM and density of EM were observed in the south-eastern region; this information could support the water authorities’ monitoring activities. To this end, further developments to improve the reference field data for the validation of water quality products are recommended.

Early Detection of Rubber Tree Powdery Mildew by Combining Spectral and Physicochemical Parameter Features

Powdery mildew significantly impacts the yield of natural rubber by being one of the predominant diseases that affect rubber trees. Accurate, non-destructive recognition of powdery mildew in the early stage is essential for the cultivation management of rubber trees. The objective of this study is to establish a technique for the early detection of powdery mildew in rubber trees by combining spectral and physicochemical parameter features. At three field experiment sites and in the laboratory, a spectroradiometer and a hand-held optical leaf-clip meter were utilized, respectively, to measure the hyperspectral reflectance data (350–2500 nm) and physicochemical parameter data of both healthy and early-stage powdery-mildew-infected leaves. Initially, vegetation indices were extracted from hyperspectral reflectance data, and wavelet energy coefficients were obtained through continuous wavelet transform (CWT). Subsequently, significant vegetation indices (VIs) were selected using the ReliefF algorithm, and the optimal wavelengths (OWs) were chosen via competitive adaptive reweighted sampling. Principal component analysis was used for the dimensionality reduction of significant wavelet energy coefficients, resulting in wavelet features (WFs). To evaluate the detection capability of the aforementioned features, the three spectral features extracted above, along with their combinations with physicochemical parameter features (PFs) (VIs + PFs, OWs + PFs, WFs + PFs), were used to construct six classes of features. In turn, these features were input into support vector machine (SVM), random forest (RF), and logistic regression (LR), respectively, to build early detection models for powdery mildew in rubber trees. The results revealed that models based on WFs perform well, markedly outperforming those constructed using VIs and OWs as inputs. Moreover, models incorporating combined features surpass those relying on single features, with an overall accuracy (OA) improvement of over 1.9% and an increase in F1-Score of over 0.012. The model that combines WFs and PFs shows superior performance over all the other models, achieving OAs of 94.3%, 90.6%, and 93.4%, and F1-Scores of 0.952, 0.917, and 0.941 on SVM, RF, and LR, respectively. Compared to using WFs alone, the OAs improved by 1.9%, 2.8%, and 1.9%, and the F1-Scores increased by 0.017, 0.017, and 0.016, respectively. This study showcases the viability of early detection of powdery mildew in rubber trees.

Utilising LANDHYPERNET data products over a deciduous broadleaf forest to validate Sentinel-2 and Landsat surface reflectance products

This paper demonstrates the use of a novel, autonomous hyperspectral surface reflectance data collected at Wytham Woods, United Kingdom (WWUK) as part of the LANDHYPERNET network for the validation of multispectral data from Sentinel-2, Landsat 8 and Landsat 9. The deployment of the HYPSTAR instrument at the site and the corresponding quality control of the data products is described. In addition, a methodology based upon metrological principles is outlined showing the propagation of uncertainties from the LANDHYPERNET and satellite data products to enable conformity testing of the satellite products using the satellite mission requirements. A total of 9 matchups are found for satellite validation at the site, where there is a cloud-free satellite scene and a corresponding LANDHYPERNET sequence, which has passed all quality checks, within two hours of the overpass. An analysis of the impact of the spatial variability of the site is presented and can account for up to 40% of the uncertainty associated with the in-situ surface reflectance data. There is no systematic bias in the Bottom-Of-Atmosphere reflectance data obtained from the LANDHYPERNET data in comparison to the satellite data. In the best case, differences of less than 2% are found for certain spectral bands. However, in the worst cases, relatively large differences are found which exceed 100%, this is affected by the relatively low reflectance values found in the visible bands. These differences could be caused by the spatial and temporal mismatch between the in-situ and satellite measurement, or due to shadowing caused by the flux tower. Further data quality control and assurance is needed to best choose data sets suitable for satellite validation. Incorporating a Bidirectional Reflectance Distribution Function model into the processing chain for the forest canopy is recommended. Overall, although there are areas to further characterise, the site provides a useful benchmark for which to develop techniques for validation of satellite surface reflectance products over a challenging environment.

Spectrometer-Less Remote Sensing Image Classification Based on Gate-Tunable van der Waals Heterostructures.

Remote sensing technology, which conventionally employs spectrometers to capture hyperspectral images, allowing for the classification and unmixing based on the reflectance spectrum, has been extensively applied in diverse fields, including environmental monitoring, land resource management, and agriculture. However, miniaturization of remote sensing systems remains a challenge due to the complicated and dispersive optical components of spectrometers. Here, m-phase GaTe0.5Se0.5 with wide-spectral photoresponses (250-1064nm) and stack it with WSe2 are utilizes to construct a two-dimensional van der Waals heterojunction (2D-vdWH), enabling the design of a gate-tunable wide-spectral photodetector. By utilizing the multi-photoresponses under varying gate voltages, high accuracy recognition can be achieved aided by deep learning algorithms without the original hyperspectral reflectance data. The proof-of-concept device, featuring dozens of tunable gate voltages, achieves an average classification accuracy of 87.00% on 6 prevalent hyperspectral datasets, which is competitive with the accuracy of 250-1000nm hyperspectral data (88.72%) and far superior to the accuracy of non-tunable photoresponse (71.17%). Artificially designed gate-tunable wide-spectral 2D-vdWHs GaTe0.5Se0.5/WSe2-based photodetector present a promising pathway for the development of miniaturized and cost-effective remote sensing classification technology.

Fully connected-convolutional (FC-CNN) neural network based on hyperspectral images for rapid identification of P. ginseng growth years

P. ginseng is a precious traditional Chinese functional food, which is used for both medicinal and food purposes, and has various effects such as immunomodulation, anti-tumor and anti-oxidation. The growth year of P. ginseng has an important impact on its medicinal and economic values. Fast and nondestructive identification of the growth year of P. ginseng is crucial for its quality evaluation. In this paper, we propose a FC-CNN network that incorporates spectral and spatial features of hyperspectral images to characterize P. ginseng from different growth years. The importance ranking of the spectra was obtained using the random forest method for optimal band selection. Based on the hyperspectral reflectance data of P. ginseng after radiometric calibration and the images of the best five VNIR bands and five SWIR bands selected, the year-by-year identification of P. ginseng age and its identification experiments for food and medicinal purposes were conducted, and the FC-CNN network and its FCNN and CNN branch networks were tested and compared in terms of their effectiveness in the identification of P. ginseng growth years. It has been experimentally verified that the best year-by-year recognition was achieved by utilizing images from five visible and near-infrared important bands and all spectral curves, and the recognition accuracy of food and medicinal use reached 100%. The FC-CNN network is significantly better than its branching model in the effect of edible and medicinal identification. The results show that for P. ginseng growth year identification, VNIR images have much more useful information than SWIR images. Meanwhile, the FC-CNN network utilizing the spectral and spatial features of hyperspectral images is an effective method for the identification of P. ginseng growth year.

Determination of asbestos cement rooftop surface composition using regression analysis and hyper-spectral reflectance data in the visible and near-infrared ranges

The effects of asbestos on human health have spurred numerous studies examining its risks in urban environments. Recent works have shifted towards less-invasive techniques for remote detection and classification of asbestos-cement. In this context, this study combines visible (VIS) and near-infrared (NIR) reflectance data collected in-situ with reference signals from the USGS spectral library, utilizing optimized regression analysis to determine the surface composition of corrugated asbestos-cement rooftops. An outlier filter was successfully implemented to enhance the accuracy of regression calculations, achieving a high level of agreement with actual field observations. The regression analysis revealed varying proportions of weathered cement, hazardous asbestos fibers (specifically chrysotile and cummingtonite), and biological growth (such as lichens and moss). These results are consistent with previous research on the composition of asbestos-cement rooftops, including a comparable field study and XRD analysis conducted in 2019. This underscores the importance of using regression analysis, preceded by an outlier filtering step, on VIS and NIR reflectance data to ascertain the surface composition of asbestos-cement rooftops. This methodology holds potential for application to larger hyperspectral datasets across more extensive sample surfaces and areas.

Research on SPAD Estimation Model for Spring Wheat Booting Stage Based on Hyperspectral Analysis.

With the rapid progression of agricultural informatization technology, the methodologies of crop monitoring based on spectral technology are constantly upgraded. In order to carry out the efficient, precise and nondestructive detection of relative chlorophyll (SPAD) during the booting stage, we acquired hyperspectral reflectance data about spring wheat vertical distribution and adopted the fractional-order differential to transform the raw spectral data. After that, based on correlation analysis, fractional differential spectra and fractional differential spectral indices with strong correlation with SPAD were screened and fused. Then, the least-squares support vector machine (LSSSVM) and the least-squares support vector machine (SMA-LSSSVM) optimized on the slime mold algorithm were applied to construct the estimation models of SPAD, and the model accuracy was assessed to screen the optimal estimation models. The results showed that the 0.4 order fractional-order differential spectra had the highest correlation with SPAD, which was 9.3% higher than the maximum correlation coefficient of the original spectra; the constructed two-band differential spectral indices were more sensitive to SPAD than the single differential spectra, in which the correlation reached the highest level of 0.724. The SMA-LSSSVM model constructed based on the two-band fractional-order differential spectral indices was better than the single differential spectra and the integration of both, which realized the assessment of wheat SPAD.

Estimation of Soybean Internal Quality Based on Improved Support Vector Regression Based on the Sparrow Search Algorithm Applying Hyperspectral Reflectance and Chemometric Calibrations

The nutritional components of soybean, such as fat and protein, directly decide soybean quality. The fast and accurate detection of these components is significant to soybean industries and soybean crop breeding. This study developed an improved SSA-SVM (support vector regression based on the sparrow search algorithm) for the rapid and accurate detection of the fat and protein in soybean seeds using hyperspectral reflectance data. In this work, 85 soybean samples were selected. After their fat and protein contents were analyzed using chemical methods, a total of 85 groups of hyperspectral image data were collected using the hyperspectral imaging system. An effective data preprocessing method was applied to reduce the noise for enhancing the prediction models. Some popular models, including partial least-square regression (PLSR), random forest regression (RFR), and support vector regression based on the genetic algorithm (GA-SVR), were also established in this study. The experimental results showed that the improved SSA-SVM model could predict the nutrient contents of the soybean samples with accuracies of 0.9403 and 0.9215 and RMSEs of 0.2234 and 0.325 for the fat and protein, respectively. The convergence speed was improved significantly. Therefore, hyperspectral data combined with the SSA-SVM algorithm presented in this study were effective for evaluating the soybean quality.

Prediction of leaf nitrogen in sugarcane (Saccharum spp.) by Vis-NIR-SWIR spectroradiometry

Nitrogen is one of the essential nutrients for the production of agricultural crops, participating in a complex interaction among soil, plant and the atmosphere. Therefore, its monitoring is important both economically and environmentally. The aim of this work was to estimate the leaf nitrogen contents in sugarcane from hyperspectral reflectance data during different vegetative stages of the plant. The assessments were performed from an experiment designed in completely randomized blocks, with increasing nitrogen doses (0, 60, 120 and 180 kg ha−1). The acquisition of the spectral data occurred at different stages of crop development (67, 99, 144, 164, 200, 228, 255 and 313 days after cutting; DAC). In the laboratory, the hyperspectral responses of the leaves and the Leaf Nitrogen Contents (LNC) were obtained. The hyperspectral data and the LNC values were used to generate spectral models employing the technique of Partial Least Squares Regression (PLSR) Analysis, also with the calculation of the spectral bands of greatest relevance, by the Variable Importance in Projection (VIP). In general, the increase in LNC promoted a smaller reflectance in all wavelengths in the visible (400–680 nm). Acceptable models were obtained (R2 > 0.70 and RMSE <1.41 g kg−1), the most robust of which were those generated from spectra in the visible (400–680 nm) and red-edge (680–750 nm), with values of R2 > 0.81 and RMSE <1.24 g kg−1. An independent validation, leave-one-date-out cross validation (LOOCV), was performed using data from other collections, which confirmed the robustness and the possibility of LNC prediction in new data sets, derived, for instance, from samplings subsequent to the period of study.

Methodology for Regional Soil Organic Matter Prediction with Spectroscopy: Optimal Sample Grouping, Input Variables, and Prediction Model

Soil organic matter (SOM) is an essential component of soil and is crucial for increasing agricultural production and soil fertility. The combination of hyperspectral remote sensing and deep learning can be used to predict the SOM content efficiently, rapidly, and cost-effectively on various scales. However, determining the optimal groups, inputs, and models for reducing the spatial heterogeneity of soil nutrients in large regions and to improve the accuracy of SOM prediction remains a challenge. Hyperspectral reflectance data from 1477 surface soil samples in Northeast China were utilized to evaluate three grouping methods (no groups (NG), traditional grouping (TG), and spectral grouping (SG)) and four inputs (raw reflectance (RR), continuum removal (CR), fractional-order differentiation (FOD), and spectral characteristic parameters (SCPs)). The SOM prediction accuracies of random forest (RF), convolutional neural network (CNN), and long short-term memory (LSTM) models were assessed. The results were as follows: (1) The highest accuracy was achieved using SG, SCPs, and the LSTM model, with a coefficient of determination (R2) of 0.82 and a root mean squared error (RMSE) of 0.69%. (2) The LSTM model exhibited the highest accuracy in SOM prediction (R2 = 0.82, RMSE = 0.89%), followed by the CNN model (R2 = 0.72, RMSE = 0.85%) and the RF model (R2 = 0.69, RMSE = 0.91%). (3) The SG provided higher SOM prediction accuracy than TG and NG. (4) The SCP-based prediction results were significantly better than those of the other inputs. The R2 of the SCP-based model was 0.27 higher and the RMSE was 0.40% lower than that of the RR-based model with NG. In addition, the LSTM model had higher prediction errors at low (0–2%) and high (8–10%) SOM contents, whereas the error was minimal at intermediate SOM contents (2–8%). The study results provide guidance for selecting grouping methods and approaches to improve the prediction accuracy of the SOM content and reduce the spatial heterogeneity of the SOM content in large regions.

Estimation of Soil Moisture Content Based on Fractional Differential and Optimal Spectral Index

Applying hyperspectral remote sensing technology to the prediction of soil moisture content (SMC) during the growth stage of soybean emerges as an effective approach, imperative for advancing the development of modern precision agriculture. This investigation focuses on SMC during the flowering stage under varying nitrogen application levels and film mulching treatments. The soybean canopy’s original hyperspectral data, acquired at the flowering stage, underwent 0–2-order differential transformation (with a step size of 0.5). Five spectral indices exhibiting the highest correlation with SMC were identified as optimal inputs. Three machine learning methods, namely support vector machine (SVM), random forest (RF), and back propagation neural network (BPNN), were employed to formulate the SMC prediction model. The results indicate the following: (1) The correlation between the optimal spectral index of each order, obtained after fractional differential transformation, and SMC significantly improved compared to the original hyperspectral reflectance data. The average correlation coefficient between each spectral index and SMC under the 1.5-order treatment was 0.380% higher than that of the original spectral index, with mNDI showing the highest correlation coefficient at 0.766. (2) In instances of utilizing the same modeling method with different input variables, the SMC prediction model’s accuracy follows the order: 1.5 order &gt; 2.0 order &gt; 1.0 order &gt; 0.5 order &gt; original order. Conversely, with consistent input variables and a change in the modeling method, the accuracy order becomes RF &gt; SVM &gt; BPNN. When comprehensively assessing model evaluation indicators, the 1.5-order differential method and RF method emerge as the preferred order differential method and model construction method, respectively. The R2 for the optimal SMC estimation model in the modeling set and validation set were 0.912 and 0.792, RMSEs were 0.005 and 0.004, and MREs were 2.390% and 2.380%, respectively. This study lays the groundwork for future applications of hyperspectral remote sensing technology in developing soil moisture content estimation models for various crop growth stages and sparks discussions on enhancing the accuracy of these different soil moisture content estimation models.

Monitoring of Nitrogen Concentration in Soybean Leaves at Multiple Spatial Vertical Scales Based on Spectral Parameters.

Nitrogen is a fundamental component for building amino acids and proteins, playing a crucial role in the growth and development of plants. Leaf nitrogen concentration (LNC) serves as a key indicator for assessing plant growth and development. Monitoring LNC provides insights into the absorption and utilization of nitrogen from the soil, offering valuable information for rational nutrient management. This, in turn, contributes to optimizing nutrient supply, enhancing crop yields, and minimizing adverse environmental impacts. Efficient and non-destructive estimation of crop LNC is of paramount importance for on-field crop management. Spectral technology, with its advantages of repeatability and high-throughput observations, provides a feasible method for obtaining LNC data. This study explores the responsiveness of spectral parameters to soybean LNC at different vertical scales, aiming to refine nitrogen management in soybeans. This research collected hyperspectral reflectance data and LNC data from different leaf layers of soybeans. Three types of spectral parameters, nitrogen-sensitive empirical spectral indices, randomly combined dual-band spectral indices, and "three-edge" parameters, were calculated. Four optimal spectral index selection strategies were constructed based on the correlation coefficients between the spectral parameters and LNC for each leaf layer. These strategies included empirical spectral index combinations (Combination 1), randomly combined dual-band spectral index combinations (Combination 2), "three-edge" parameter combinations (Combination 3), and a mixed combination (Combination 4). Subsequently, these four combinations were used as input variables to build LNC estimation models for soybeans at different vertical scales using partial least squares regression (PLSR), random forest (RF), and a backpropagation neural network (BPNN). The results demonstrated that the correlation coefficients between the LNC and spectral parameters reached the highest values in the upper soybean leaves, with most parameters showing significant correlations with the LNC (p < 0.05). Notably, the reciprocal difference index (VI6) exhibited the highest correlation with the upper-layer LNC at 0.732, with a wavelength combination of 841 nm and 842 nm. In constructing the LNC estimation models for soybeans at different leaf layers, the accuracy of the models gradually improved with the increasing height of the soybean plants. The upper layer exhibited the best estimation performance, with a validation set coefficient of determination (R2) that was higher by 9.9% to 16.0% compared to other layers. RF demonstrated the highest accuracy in estimating the upper-layer LNC, with a validation set R2 higher by 6.2% to 8.8% compared to other models. The RMSE was lower by 2.1% to 7.0%, and the MRE was lower by 4.7% to 5.6% compared to other models. Among different input combinations, Combination 4 achieved the highest accuracy, with a validation set R2 higher by 2.3% to 13.7%. In conclusion, by employing Combination 4 as the input, the RF model achieved the optimal estimation results for the upper-layer LNC, with a validation set R2 of 0.856, RMSE of 0.551, and MRE of 10.405%. The findings of this study provide technical support for remote sensing monitoring of soybean LNCs at different spatial scales.

Detection of Rubber Tree Powdery Mildew from Leaf Level Hyperspectral Data Using Continuous Wavelet Transform and Machine Learning

Powdery mildew is one of the most significant rubber tree diseases, with a substantial impact on the yield of natural rubber. This study aims to establish a detection approach that coupled continuous wavelet transform (CWT) and machine learning for the accurate assessment of powdery mildew severity in rubber trees. In this study, hyperspectral reflectance data (350–2500 nm) of healthy and powdery mildew-infected leaves were measured with a spectroradiometer in a laboratory. Subsequently, three types of wavelet features (WFs) were extracted using CWT. They were as follows: WFs dimensionally reduced by the principal component analysis (PCA) of significant wavelet energy coefficients (PCA-WFs); WFs extracted from the top 1% of the determination coefficient between wavelet energy coefficients and the powdery mildew disease class (1%R2-WFs); and all WFs at a single decomposition scale (SS-WFs). To assess the detection capability of the WFs, the three types of WFs were input into the random forest (RF), support vector machine (SVM), and back propagation neural network (BPNN), respectively. As a control, 13 optimal traditional spectral features (SFs) were extracted and combined with the same classification methods. The results revealed that the WF-based models all performed well and outperformed those based on SFs. The models constructed based on PCA-WFs had a higher accuracy and more stable performance than other models. The model combined PCA-WFs with RF exhibited the optimal performance among all models, with an overall accuracy (OA) of 92.0% and a kappa coefficient of 0.90. This study demonstrates the feasibility of combining CWT with machine learning in rubber tree powdery mildew detection.

Comparison of leaf chlorophyll content retrieval performance of citrus using FOD and CWT methods with field-based full-spectrum hyperspectral reflectance data

Citrus is one of the most economically valuable fruit trees in the world, for which leaf chlorophyll content (LCC) serves as a crucial indicator for evaluating its growth and health status. However, the quantitative estimation of LCC using remote-sensing techniques is still challenging owing to unclear sensitive spectral ranges, baseline drift and overlapping spectrum peaks. To resolve these issues, we clarified the spectral response characteristics of citrus LCC using fractional-order derivatives (FOD) and continuous wavelet transform (CWT) methods to determine its sensitive spectral range with in situ full-spectrum leaf hyperspectral data. We proposed a novel method for estimating the LCC of citrus by combining an ensemble learning regression model based on Hyperopt optimization (H-ELR) with partial least squares regression (PLSR) using the ultra-high dimensional feature variables produced by dual- and tri-band combination strategies. We evaluated the retrieval performance of LCC between the FOD- and CWT-based optimal spectral feature variables. Besides, we further examined the feasibility of improving the estimation accuracy of LCC by the combination of their optimal feature variables. Finally, we evaluated the effect of the spectral curve trend changes and different dimensional spectral features on LCC estimation. The results showed that: (1) The FOD- and CWT-based methods improved the correlation between original spectral reflectance and LCC, with the correlation coefficient increasing by 0.046 and 0.054, respectively. We confirmed that 425–740 nm is the optimal spectral range for LCC estimation. (2) We found that the 0.9 order derivative Tri-band index (TBI2 (R571, R1697, R740)) constructed by combining the sensitive spectral bands of leaf water content achieved a high-precision LCC estimation (R2 = 0.876). In addition, the MERIS terrestrial chlorophyll index (MTCI) constructed based on the scale6 of CWT also gained good retrieval accuracy (R2 = 0.806). (3) We demonstrated that a combination of FOD-based and CWT-based sensitive spectral features improves estimation accuracies (R2 = 0.891) of LCC and revealed that the reflectance peaks and slope peaks at 550 and 750 nm are essential variables for predicting the citrus LCC. (4) The combination of PLSR and H-ELR model provided a good retrieval performance of citrus LCC with the kurtosis (γ = 3.2) and skewness (Sk = 0.066) of the residual prediction values. Our proposed method can provide a scientific basis for estimating LCC and other physiological parameters of citrus and other crop types, which is also important to optimize agricultural management practices and improve crop yield.

Predictions of Spartina alterniflora leaf functional traits based on hyperspectral data and machine learning models

ABSTRACT Investigating the functional traits of Spartina alterniflora can provide insights towards understanding its invasion mechanism, and developing a method leaves can improve its management in coastal wetlands. Here, we examined the relationship between 11 leaf functional traits of S. alterniflora and hyperspectral data and investigated the feature bands through importance score analysis. Using original spectral and first-order differential conversion data of feature bands, we established four prediction models: random forest (RF), support vector machine (SVM), eXtreme Gradient Boosting (XGBoost), and back propagation neural network (BPNN). The study results showed that: (1) the SVM model based on Random Forest Importance Score is well-suited for S. alterniflora leaf functional trait inversion; (2) the importance score of leaf functional traits differed, and first-order differential spectral data produced more bands with high scores compared with the original hyperspectral reflectance data; (3) first-order differential data modelling effects were slightly better than those of the original spectral data. However, the first-order differential treatment did not show a significant improvement in the validation accuracy compared with the original data, and the accuracy of some traits decreased. Our study provides a new methodological approach for improving the monitoring and management of S. alterniflora in coastal wetlands.

Detection of Aphid-Infested Mustard Crop Using Ground Spectroscopy

Timely detection of pest infestation in agricultural crops plays a pivotal role in the planning and execution of pest management interventions. In this study, a ground measured electromagnetic spectrum through hyperspectral sensing (400–2500 nm) was conducted in healthy and aphid-infested mustard crops in different regions of the Bharatpur district of Rajasthan state, India. The ground measured hyperspectral reflectance and its derivatives during the mustard aphid infestation period were used to identify the sensitive spectral regions in the electromagnetic spectrum concerning Aphid Infestation Severity Grade (AISG) to discriminate Lipaphis-infested mustard crops from the healthy ones. Further Principal Component Analysis (PCA) and Partial Least Square Regression (PLSR) were utilized to identify specific spectral bands to differentiate the healthy from aphid-infested crops. The spectral regions of 493–497 nm (blue), 509–515 nm (green), 690–714 nm (red), 717–721 nm (red edge), and 752–756 nm (NIR) showed high correlation with AISG for reflectance, first and second order derivatives. Further analysis of the spectra using PCA and PLSR indicated that spectral bands of 679 nm, 746 nm, and 979 nm had high sensitivity for discriminating aphid-infested crops from the healthy ones. Average reflectance and various spectral indices such as ratio spectral index (RSI), difference spectral index (DSI), and normalized difference spectral index (NDSI) of identified spectral regions and absolute reflectance of identified specific spectral bands were used for predicting AISG. Several regression models, including PCR and PLSR, were examined to predict the AISG. PLSR was found to better predict infestation grade with RMSE of 0.66 and r2 0.71. Our outcomes counseled that hyperspectral reflectance data have the ability to detect aphid-infested severity in mustard.

Dual sampling linear regression ensemble to predict wheat yield across growing seasons with hyperspectral sensing

In the domain of crop yield assessment, the combination of remote sensing and advanced regression techniques has gained significant attention. This study focused on predicting wheat yield across growing seasons using hyperspectral reflectance data. The performance of two decision tree-based algorithms, deep forest (DF) and random forest (RF), as well as a novel ensemble method called dual sampling linear regression ensemble (DSLRE), was evaluated. Canopy hyperspectral reflectance data were collected at two stages of wheat growth: early grain filling (EGF) and mid-grain filling (MGF), in three distinct growing environments. Prediction accuracy was assessed using the Pearson correlation coefficient (PCC) and mean absolute error (MAE). The results showed that the DF algorithm outperformed the RF algorithm in most scenarios. In modeling framework A, DF achieved the highest accuracy at the MGF stage using truncated reflectance (PCC = 0.60, MAE = 0.82 t/ha). In modeling framework B, DF performed better than RF in three out of four conditions, while RF exhibited higher prediction accuracy during the EGF stage using raw reflectance (PCC = 0.59, MAE = 1.48 t/ha). Moreover, the DSLRE algorithm outperformed DF and RF in six of the eight modeling scenarios and demonstrated robustness in coping with noisy features. The heritability analysis indicated that DSLRE predictions effectively captured the effects of genetic factors on yield variation. These findings illustrate the significance of the proposed DSLRE algorithm in improving yield prediction, providing valuable guidance for effective crop yield management.

Hyperspectral estimation of chlorophyll content in jujube leaves: integration of derivative processing techniques and dimensionality reduction algorithms.

The leaf chlorophyll content (LCC) of vegetation is closely related to photosynthetic efficiency and biological activity. Jujube (Ziziphus jujuba Mill.) is a traditional economic forest tree species. Non-destructive monitoring of LCC of jujube is of great significance for guiding agroforestry production and promoting ecological environment protection in arid and semi-arid lands. Hyperspectral data is an important data source for LCC detection. However, hyperspectral data consists of a multitude of bands and contains extensive information. As a result, certain bands may exhibit high correlation, leading to redundant spectral information. This redundancy can distort LCC prediction results and reduce accuracy. Therefore, it is crucial to select appropriate preprocessing methods and employ effective data mining techniques when analyzing hyperspectral data. This study aims to evaluate the performance of hyperspectral data for estimating LCC of jujube trees by integrating different derivative processing techniques with different dimensionality reduction algorithms. Hyperspectral reflectance data were obtained through simulations using an invertible forest reflectance model (INFORM) and measurements from jujube tree canopies. The least absolute shrinkage and selection operator (LASSO) and elastic net (EN) were employed to identify the important bands in the original spectra (OS), first derivative spectra (FD), and second derivative spectra (SD). Support vector regression (SVR) was used to establish the estimation model. The results show that compared with full-spectrum modeling, LASSO and EN algorithms are effective methods for preventing overfitting in LCC machine learning estimation models for different spectral derivatives. The LASSO/EN-based estimation models constructed using FD and SD exhibited superior R2 compared to the OS. The important band of SD can best reveal the relevant information of jujube LCC, and SD-EN-SVR is the most ideal model in both the simulated dataset (R2=0.99, RMSE=0.61) and measured dataset (R2=0.89, RMSE=0.91). Our results provided a reference for rapid and non-destructive estimation of the LCC of agroforestry vegetation using canopy hyperspectral data.

Early detection of pine shoot beetle attack using vertical profile of plant traits through UAV-based hyperspectral, thermal, and lidar data fusion

Pine shoot beetle (PSB) is one of the most damaging forest insects of Yunnan pine plantations in southwest China. However, the subtle symptoms of heterogeneous tree crowns make it difficult to accurately detect at early stage of PSB attack. Here, we evaluated the potential of a combination of plant traits (PTs) and vegetation indices (VIs) to distinguish different levels of tree damage by integrating hyperspectral, thermal imagery, and light detection and ranging (lidar) data based on unmanned airborne vehicle (UAV) systems. A voxelization method was used to fuse hyperspectral reflectance, temperature, and lidar point cloud data. Subsequently, PTs such as pigments (i.e., chlorophyll, carotenoid, and anthocyanin contents) were retrieved from a radiative transfer model inversion, and structural, fluorescence, and thermal traits, as well as VIs, were derived from the fusion data. We developed a novel analytical approach using random forest (RF) algorithm with predictors from different spatial distributions (i.e., horizontal directions, vertical layers, and vertical clusters) to compare the performance of tree severity classification. The results showed that the difference of both PTs and VIs in vertical layers between different severity levels are more than that in horizontal directions. The performance of RF model with predictors of the vertical layers (OA = 74 %, kappa = 0.65) was better than that using the predictive variables in horizontal directions (OA = 69 % and kappa = 0.60) of tree crowns. Using the vertical clustering features RF model increased the accuracy (OA = 78 % and kappa = 0.70), especially for slightly and moderately damaged trees, with improvements of 10 % and 12 %, respectively. Among all variables analyzed, chlorophylls were the most important predictor, followed by photochemical reflectance index and structural traits. Our work demonstrates the effectiveness of using fused UAV-based multi-sensor data for early detection of PSB attack, and can be applied other potential forest diseases and insect monitoring.

Quantifying colors at micrometer scale by colorimetric microscopy (C-Microscopy) approach

The color is the primal property of the objects around us and is direct manifestation of light-matter interactions. The color information is used in many different fields of science, technology and industry to investigate material properties or for identification of concentrations of substances. Usually the color information is used as a global parameter in a macro scale. To quantitatively measure color information in micro scale one needs to use dedicated microscope spectrophotometers or specialized micro-reflectance setups. Here, the Colorimetric Microscopy (C-Microscopy) approach based on digital optical microscopy and a free software is presented. The C-Microscopy approach uses color calibrated image and colorimetric calculations to obtain physically meaningful quantities i.e., dominant wavelength and excitation purity maps at micro level scale. This allows for the discovery of the local color details of samples surfaces. Later, to fully characterize the optical properties, the hyperspectral reflectance data at micro scale (reflectance as a function of wavelength for a each point) are colorimetrically recovered. The C-Microscopy approach was successfully applied to various types of samples i.e., two metamorphic rocks unakite and lapis lazuli, which are mixtures of different minerals; and to the surface of gold 99.999 % pellet, which exhibits different types of surface features. The C-Microscopy approach could be used to quantify the local optical properties changes of various materials at microscale in an accessible way. The approach is freely available as a set of python jupyter notebooks.