Search Results (76)

Forest structure parameters are critical for understanding and managing forest ecosystems, yet sparse forests have received limited attention in previous studies. To address this research gap, this study systematically evaluates and compares the sensitivity of active Synthetic Aperture Radar (SAR) and passive optical remote sensing to key forest structure parameters in sparse forests, including Diameter at Breast Height (DBH), Tree Height (H), Crown Width (CW), and Leaf Area Index (LAI). Using the novel computer-graphics-based radiosity model applicable to porous individual thin objects, named Radiosity Applicable to Porous Individual Objects (RAPID), we simulated 38 distinct sparse forest scenarios to generate both SAR backscatter coefficients and optical reflectance across various wavelengths, polarization modes, and incidence/observation angles. Sensitivity was assessed using the coefficient of variation (CV). The results reveal that C-band SAR in HH polarization mode demonstrates the highest sensitivity to DBH (CV = −6.73%), H (CV = −52.68%), and LAI (CV = −63.39%), while optical data in the red band show the strongest response to CW (CV = 18.83%) variations. The study further identifies optimal acquisition configurations, with SAR data achieving maximum sensitivity at smaller incidence angles and optical reflectance performing best at forward observation angles. This study addresses a critical gap by presenting the first systematic comparison of the sensitivity of multi-band SAR and VIS/NIR data to key forest structural parameters across sparsity gradients, thereby clarifying their applicability for monitoring young and middle-aged sparse forests with high carbon sequestration potential. Full article

Water status is a key determinant of physiological performance and vineyard productivity. However, its assessment through field measurements is time-consuming and labour-intensive. Remote sensing offers a fast and reliable alternative to traditional in situ methods for the monitoring of the water status in vineyards. This study aimed to assess the potential of high-resolution multispectral imagery acquired by UAVs to estimate the vine water status. The research was conducted over two growing seasons (2021 and 2022) in a commercial Merlot vineyard in Yepes (Toledo, Central Spain), under five irrigation regimes designed to generate a range of water statuses. UAV flights were performed at two times of day (09:00 and 12:00 solar time), coinciding with in-field measurements of physiological parameters. Stem water potential (SWP), chlorophyll content, and photosynthesis data were collected. The SWP consistently showed the strongest and most stable associations with vegetation indices (VIs) and the red spectral band at 12:00. A simple linear regression model using the NDVI explained up to 58% of the SWP variability regardless of the time of day or year. Multiple linear regression models incorporating the red and NIR bands yielded even higher predictive power (R² = 0.62). Stronger correlations were observed at 12:00, especially when combining data from both years, highlighting the importance of midday measurements in capturing water stress effects. These findings demonstrate the potential of UAV-based multispectral imagery as a non-destructive and scalable tool for the monitoring of the vine water status under field conditions. Full article

Milk jujube has become an increasingly popular tropical fruit. The sugar content, which is commonly represented by the soluble solid content (SSC), is a key indicator of the flavor, internal quality, and market value of milk jujubes. Traditional methods for assessing SSC are time-consuming, labor-intensive, and destructive. These methods fail to meet the practical demands of the fruit market. A rapid, stable, and effective non-destructive detection method based on visible/near-infrared (Vis/NIR) spectroscopy is proposed here. A Vis/NIR reflectance spectroscopy system covering 340–1031 nm was constructed to detect SSC in milk jujubes. A structured spectral modeling framework was adopted, consisting of outlier elimination, dataset partitioning, spectral preprocessing, feature selection, and model construction. Comparative experiments were conducted at each step of the framework. Special emphasis was placed on the impact of outlier detection and dataset partitioning strategies on modeling accuracy. A data-augmentation-based unsupervised anomaly sample elimination (DAUASE) strategy was proposed to enhance the data validity. Multiple data partitioning strategies were evaluated, including random selection (RS), Kennard–Stone (KS), and SPXY methods. The KS method achieved the best preservation of the original data distribution, improving the model generalization. Several spectral preprocessing and feature selection methods were used to enhance the modeling performance. Regression models, including support vector regression (SVR), partial least squares regression (PLSR), multiple linear regression (MLR), and backpropagation neural network (BP), were compared. Based on a comprehensive analysis of the above results, the DAUASE + KS + SG + SNV + CARS + SVR model exhibited the highest prediction performance. Specifically, it achieved an average precision (AP_p) of 99.042% on the prediction set, a high coefficient of determination (R_P²) of 0.976, and a low root-mean-square error of prediction (RMSEP) of 0.153. These results indicate that Vis/NIR spectroscopy is highly effective and reliable for the rapid and non-destructive detection of SSC in milk jujubes, and it may also provide a theoretical basis for the practical application of rapid and non-destructive detection in milk jujubes and other jujube varieties. Full article

Spinach, leafy vegetables with growing demand and high nutritional value, has a heightened focus on nitrate content. An open-field experiment evaluated the potential of vis-NIR-SWIR hyperspectral data for classifying spinach nitrate content. Shallow artificial neural networks (ANN) and ensemble techniques—majority voting (MV) and stacked generalization (stacked)—were applied. The competitive adaptive reweighted sampling (CARS), its stability version (SCARS), Elastic Net, and modified boosted versions of each (CARSplus, SCARSplus, and ENplus) were used as feature selection methods. ANNs were optimized for hidden layer size. The resulting models were further used in ensemble techniques by grouping them into two sets: one with all models and another with models trained using the three boosted feature selection subsets (fifty-three wavelengths). The best-performing ANNs were based on the SCARS, SCARSplus, and full datasets, achieving an accuracy (Acc) of 0.83. While the majority voting approach did not improve performance (Acc 0.82), the stacked ensemble models reached Acc 0.88. Notably, stacked performed well also with models trained on 53 wavelengths, demonstrating strong potential for transferability as the required sensors would be less complex than those used in this study. Furthermore, a simulation of the practical application was conducted using Italian Ministry of Health official data with the scope of showing a potential use case in improving nitrate management and for advancing efficient farming practices in agriculture. The stacked models demonstrated their utility in doubling the monitoring capacity for internal quality assurance in spinach farming within a regulated framework. Full article

The emergence of new-generation hyperspectral satellites offers more potential for mapping soil properties. This study presents the first assessment of EnMAP (Environmental Mapping and Analysis Program) hyperspectral imagery for soil organic matter (SOM) prediction and mapping using actual spectral data from 282 soil samples. Different spectral preprocessing techniques, including Savitzky–Golay (SG) smoothing, the second derivative of SG, and Standard Normal Variate (SNV) transformation, were evaluated in combination with embedded feature selection to identify the most relevant wavelengths for SOM prediction. Partial Least Squares Regression (PLSR) models were developed under different pre-treatment scenarios. The best performance was obtained using SNV preprocessing with the top 30 EnMAP bands (wavelengths) selected, giving R² = 0.68, RMSE = 0.34%, and RPIQ = 1.75. The combination of SNV with feature selection successfully identified significant wavelengths for SOM prediction, particularly around 550 nm in the Vis–NIR region, 1570–1630 nm, and 1600 nm and 2200 nm in the SWIR region. The resulting SOM predictions exhibited spatially consistent patterns that corresponded with known soil–landscape relationships, highlighting the potential of EnMAP hyperspectral data for mapping soil properties despite its limited geographical availability. While these results are promising, this study identified limitations in the ability of PLSR to extrapolate predictions beyond the sampled areas, suggesting the need to explore non-linear modeling approaches. Future research should focus on evaluating EnMAP’s performance using advanced machine learning techniques and comparing it to other available hyperspectral products to establish robust protocols for satellite-based soil monitoring. Full article

Nitrogen fertilization is a challenging task that usually requires intensive use of resources, such as fertilizers, management and water. This study explored the potential of VIS-NIR-SWIR remote sensing for quantifying leaf nitrogen content (LNC) in sugarcane from different regions and vegetative stages. Conducted in three regions of São Paulo, Brazil (Jaú, Piracicaba and Santa Maria), the research involved three experiments, one per location. The spectral data were obtained at 140, 170, 200, 230 and 260 days after cutting (DAC). From the hyperspectral data, clustering analysis was performed to identify the patterns between the spectral bands for each region where the spectral readings were made, using the Partitioning Around Medoids (PAM) algorithm. Then, the LNC values were used to generate spectral models using Partial Least Squares Regression (PLSR). Subsequently, the generalization of the models was tested with the leave-one-date-out cross-validation (LOOCV) technique. The results showed that although the variation in leaf N was small, the sensor demonstrated the ability to detect these variations. Furthermore, it was possible to determine the influence of N concentrations on the leaf spectra and how this impacted cluster formation. It was observed that the greater the average variation in N content in each cluster, the better defined and denser the groups formed were. The best time to quantify N concentrations was at 140 DAC (R² = 0.90 and RMSE = 0.74 g kg⁻¹). From LOOCV, the areas with sandier soil texture presented a lower model performance compared to areas with clayey soil, with R² < 0.54. The spatial generalization of the models recorded the best performance at 140 DAC (R² = 0.69, RMSE = 1.18 g kg⁻¹ and dr = 0.61), decreasing in accuracy at the crop-maturation stage (260 DAC), R² of 0.05, RMSE of 1.73 g kg⁻¹ and dr of 0.38. Although the technique needs further studies to be improved, our results demonstrated potential, which tends to provide support and benefits for the quantification of nutrients in sugarcane in the long term. Full article

Traditional techniques for measuring leaf nitrogen content (LNC) involve slow and laborious processes, and radiometric data have been used to assist in the nutritional analysis of plants. Therefore, this study aimed to evaluate the performance of LNC predictions in corn plants based on laboratory hyperspectral Vis-NIR-SWIR data. The treatments corresponded to 60, 120, 180, and 240 kg ha⁻¹ of nitrogen, in addition to the control (0 kg ha⁻¹), and they were distributed using a randomized complete block design. At the laboratory, hyperspectral data of the leaves and LNC were obtained. The hyperspectral data were used in the calculation of different vegetation indices (VIs), which were applied in a predictive model—partial least squares regression (PLSR)—and the capacity of the prediction was assessed. The combination of bands and VIs generated a better prediction (0.74 < R² < 0.87; 1.00 < RMSE < 1.50 kg ha⁻¹) in comparison with the individual prediction by band (0.69 < R² < 0.85; 1.00 < RMSE < 1.77 kg ha⁻¹) and by VI (0.55 < R² < 0.68; 1.00 < RMSE < 1.78 kg ha⁻¹). Hyperspectral data offer a new opportunity to monitor the LNC in corn plants, especially in the region comprising the bands from 450 to 750 nm, since these were the bands that were most sensitive to the LNC. Full article

This study introduces a novel method for detecting and measuring transparent glass sheets using hyperspectral imaging (HSI). The main goal of this study is to create a conversion technique that can accurately display spectral information from collected images, particularly in the visible light spectrum (VIS) and near-infrared (NIR) areas. This technique enables the capture of relevant spectral data when used with images provided by industrial cameras. The next step in this investigation is using principal component analysis to examine the obtained hyperspectral images derived from different treated glass samples. This analytical procedure standardizes the magnitude of light wavelengths that are inherent in the HSI images. The simulated spectral profiles are obtained using the generalized inverse matrix technique on the normalized HSI images. These profiles are then matched with spectroscopic data obtained from microscopic imaging, resulting in the observation of distinct dispersion patterns. The novel use of images coloring methods effectively displays the thickness of the glass processing sheet in a visually noticeable way. Based on empirical research, changes in the thickness of the glass coating in the NIR-HSI range cause significant changes in the transmission of infrared light at different wavelengths within the NIR spectrum. This phenomenon serves as the foundation for the study of film thickness. The root mean square error inside the NIR area is impressively low, calculated to be just 0.02. This highlights the high level of accuracy achieved by the technique stated above. Potential areas of investigation that arise from this study are incorporating the proposed approach into the design of a real-time, wide-scale automated optical inspection system. Full article

Modeling spectral reflectance data using machine learning algorithms presents a promising approach for estimating soil attributes. Nevertheless, a comprehensive investigation of the most effective models, parameters, wavelengths, and data acquisition techniques is essential to ensure optimal predictive accuracy. This work aimed to (a) explore the potential of the soil spectral signature obtained in different spectral bands (VIS-NIR, SWIR, and VIS-NIR-SWIR) and, by using hyperspectral imaging and non-imaging sensors, in the predictive modeling of soil attributes; and (b) analyze the accuracy of different ML models in predicting particle size and soil organic carbon (SOC) applied to the spectral signature of different spectral bands. Six soil monoliths, located in the central north region of Parana, Brazil, were collected and scanned via hyperspectral cameras (VIS-NIR camera and SWIR camera) and spectroradiometer (VIS-NIR-SWIR) in the laboratory. The spectral signature of the soils was analyzed and subsequently applied to ML models to predict particle size and SOC. Each set of data obtained by the different sensors was evaluated separately. The algorithms used were k-nearest neighbors (KNN), support vector machine (SVM), random forest (RF), linear regression (LR), artificial neural network (NN), and partial least square regression (PLSR). The most promising predictive performance was observed for the complete VIS-NIR-SWIR spectrum, followed by SWIR and VIS-NIR. Meanwhile, KNN, RF, and NN models were the most promising algorithms in estimating soil attributes for the dataset obtained from both sensors. The general mean R² (determination coefficient) values obtained using these models, considering the different spectral bands evaluated, were around 0.99, 0.98, and 0.97 for sand prediction, and around 0.99, 0.98, and 0.96 for clay prediction. The lower performances, obtained for the datasets from both sensors, were observed for silt and SOC, with R² results between 0.40 and 0.59 for these models. KNN demonstrated the best predictive performance. Integrating effective ML models with robust sample databases, obtained by advanced hyperspectral imaging and spectroradiometers, can enhance the accuracy and efficiency of soil attribute prediction. Full article

Pomelo fruit pulp mainly is consumed fresh and with very little processing, and its peels are discarded as biological waste, which can cause the environmental problems. The peels contain several bioactive chemical compounds, especially essential oils (EOs). The content of a specific EO is important for the extraction process in industry and in research units such as breeding research. The explanation of the biosynthesis pathway for EO generation and change was included. The chemical bond vibration affected the prediction of EO constituents was comprehensively explained by regression coefficient plots and x-loading plots. Visible and near-infrared spectroscopy (VIS/NIRS) is a prominent rapid technique used for fruit quality assessment. This research work was focused on evaluating the use of VIS/NIRS to predict the composition of EOs found in the peel of the pomelo fruit (Citrus maxima (J. Burm.) Merr. cv Kao Nam Pueng) following storage. The composition of the peel oil was analyzed by gas chromatography–mass spectrometry (GC-MS) at storage durations of 0, 15, 30, 45, 60, 75, 90, 105 and 120 days (at 10 °C and 70% relative humidity). The relationship between the NIR spectral data and the major EO components found in the peel, including nootkatone, geranial, β-phellandrene and limonene, were established using the raw spectral data in conjunction with partial least squares (PLS) regression. Preprocessing of the raw spectra was performed using multiplicative scatter correction (MSC) or second derivative preprocessing. The PLS model of nootkatone with full MSC had the highest correlation coefficient between the predicted and reference values (r = 0.82), with a standard error of prediction (SEP) of 0.11% and bias of 0.01%, while the models of geranial, β-phellandrene and limonene provided too low r values of 0.75, 0.75 and 0.67, respectively. The nootkatone model is only appropriate for use in screening and some other approximate calibrations, though this is the first report of the use of NIR spectroscopy on intact fruit measurement for its peel EO constituents during cold storage. Full article

The VIIRS instrument on the JPSS-2 (renamed NOAA-21 upon reaching orbit) spacecraft was launched in November 2022, making it the third sensor in the VIIRS series, following those onboard the SNPP and NOAA-20 spacecrafts, which are operating nominally. As a multi-disciplinary instrument, the VIIRS provides the worldwide user community with high-quality imagery and radiometric measurements of the land, atmosphere, cryosphere, and oceans. This study provides an early assessment of the calibration stability and radiometric consistency of the NOAA-21 VIIRS RSBs using the latest NASA SIPS C2 L1B products. Vicarious approaches are employed, relying on reflectance data obtained from the Libya-4 desert and Dome C sites, deep convective clouds, and simultaneous nadir overpasses, as well as intercomparison with the first two VIIRS instruments using MODIS as a transfer radiometer. The impact of existing band spectral differences on sensor-to-sensor comparison is corrected using scene-specific a priori hyperspectral observations from the SCIAMACHY sensor onboard the ENVISAT platform. The results indicate that the overall radiometric performance of the newly launched NOAA-21 VIIRS is quantitatively comparable to the NOAA-20 for the VIS and NIR bands. For some SWIR bands, the measured reflectances are lower by more than 2%. An upward adjustment of 6.1% in the gain of band M11 (2.25 µm), based on lunar intercomparison results, generates more consistent results with the NOAA-20 VIIRS. Full article

More efficient resource utilization and increased crop utilization rate are needed to address the growing demand for food. The efficient quality testing of key agricultural products such as potatoes, especially the rapid testing of key nutritional indicators, has become an important strategy for ensuring their quality and safety. In this study, visible and near infrared (Vis/NIR) transmittance spectroscopy (600–900 nm) was used for the online analysis of multiple quality parameters in potatoes. The study concentrated on comparing three one-dimensional convolutional neural network (1D-CNN) models, specifically, the fine-tuned DeepSpectra, the fine-tuned 1D-AlexNet, and classic CNN, with UVE-PLS (uninformative variable elimination–partial least squares) models. These models utilized spectral data for the real-time detection of dry matter (DM) content in potatoes. To address the challenges posed by limited data from Vis/NIR, this study strategically implemented data augmentation techniques. This approach significantly enhanced the robustness and generalization capabilities of the models. The 1D-AlexNet and DeepSpectra models achieved 0.934 and 0.913 R²_P and 0.0603 and 0.0695 g/100 g RMSEP for DM, respectively. Compared to UVE-PLS, the R²_P value improved by 21.31% (0.770 to 0.934) for the 1D-AlexNet model and 18.64% (0.770 to 0.913) for the DeepSpectra model. The RMSEP value was reduced by 47.31% (0.114 to 0.0603) for 1D-AlexNet, and 39.30% (0.114 to 0.0695) for the DeepSpectra model. As a result, this study would be helpful for researching the online Vis/NIR transmission determination of potato DM using deep learning. These results highlighted the immense potential of employing specific spectral features in deep-learning models for a more precise and efficient online assessment of agricultural quality. This advancement provided some insight and reference for further contributing to the evolution of more targeted and efficient quality assessment methods in agricultural products. Full article

Camera traps, an invaluable tool for biodiversity monitoring, capture wildlife activities day and night. In low-light conditions, near-infrared (NIR) imaging is commonly employed to capture images without disturbing animals. However, the reflection properties of NIR light differ from those of visible light in terms of chrominance and luminance, creating a notable gap in human perception. Thus, the objective is to enrich near-infrared images with colors, thereby bridging this domain gap. Conventional colorization techniques are ineffective due to the difference between NIR and visible light. Moreover, regular supervised learning methods cannot be applied because paired training data are rare. Solutions to such unpaired image-to-image translation problems currently commonly involve generative adversarial networks (GANs), but recently, diffusion models gained attention for their superior performance in various tasks. In response to this, we present a novel framework utilizing diffusion models for the colorization of NIR images. This framework allows efficient implementation of various methods for colorizing NIR images. We show NIR colorization is primarily controlled by the translation of the near-infrared intensities to those of visible light. The experimental evaluation of three implementations with increasing complexity shows that even a simple implementation inspired by visible-near-infrared (VIS-NIR) fusion rivals GANs. Moreover, we show that the third implementation is capable of outperforming GANs. With our study, we introduce an intersection field joining the research areas of diffusion models, NIR colorization, and VIS-NIR fusion. Full article

Saudi Arabia is a prominent producer of dates, producing 1.6 million tons annually. There is a need to evaluate the physical properties and quality of fruits non-destructively and then modeled and predict them throughout the storage period. The aim of the current study was to generate a quality index (Q_i) and visible–near-infrared spectra (VIS-NIR) models non-destructively to predict properties of Barhi dates including objective and sensory evaluations. A total of 1000 Barhi fruits were sorted into three stages of maturation, ranging from 80 to 100% yellowish. The physical properties (hardness, color, TSS, pH, and sensory evaluations) of Barhi dates were measured and modeled with Q_i based on VIS-NIR of fresh Barhi fruits and during storage in ambient (25 °C), cold (1 °C), and CA (1 °C with 5%:5% O₂:CO₂, 85% RH) conditions for up to 3 months. The prediction of Q_i was non-destructively based on VIS-NIR utilizing PLSR and ANN data analysis. The results showed that the shelf-life of stored Barhi fruits were 20, 40, and 120 days corresponding to 25 °C, cold (1 °C), and CA, respectively. It was found that VIS-NIR spectroscopy was helpful in estimating the Q_i of Barhi fruits for PLSR and ANN data analysis, respectively, in calibration with an R² of 0.793 and 0.912 and RMSEC of 0.110 and 0.308 and cross-validation with an R² of 0.783 and 0.912 and RMSEC of 0.298 and 0.308. The VIS-NIR spectrum has proven to be an effective method for the evaluation of the Q_i of Barhi fruits and their physical properties throughout the supply chain in the handling, processing, transportation, storage and retail sectors. It was found that ANN is more suitable than PLSR analysis. Full article

The accuracy of vegetation indices (VIs) in estimating forest stand age is significantly inadequate due to insufficient consideration of the differences in the physiological functions of forest ecosystems, which limits the accuracy of carbon sink simulation. In this study, remote sensing inversion and mapping of forest stand age were carried out on the Loess Plateau under consideration of the remote sensing mechanism of VIs and the physiological function and canopy structure of the forest using multiple linear regression (MLR) and random forest (RF) models. The main conclusions are as follows: (1) The canopy reflectance of different forest stands has a significant change pattern, and the older the forest stands, the lower the NIR reflectance. The relationship between forest stands and red edge is the most significant, and r is 0.53, and the relationship between Simple Ratio Index (SR), near-infrared reflectance of vegetation (NIR_v), normalized difference vegetation index (NDVI), Global Vegetation Index and forest stands is more nonlinear than linear. (2) Principal component analysis (PCA) of canopy spectral information shows that SR, NDVI and red edge (B₅) could explain 98% of all spectral information. SR, NDVI and red edge (B₅) were used to construct a multiple linear regression model and random forest (RF) algorithm model, and RF has high estimation accuracy (R² = 0.63). (3) The accuracy of the model was evaluated using reference data, and it was found that the accuracy of the RF model (R² = 0.63) was higher than that of the linear regression model (R² = 0.61), but both models underestimated the forest stand age when the forest stand age was greater than 50a, which may be caused by the saturation of the reflectance of the old forest canopy. The RF model was used to generate the dataset of forest stand information in the Loess Plateau, and it was found that the forest is dominated by young forests (<20a), accounting for 38.26% of the forest area, and the average age of forests in the Loess Plateau is 56.1a. This study not only improves the method of forest stand age estimation, but also provides data support for vegetation construction in the Loess Plateau. Full article