Search Results (390)

The Pushbroom Imager for Cloud and Aerosol Research and Development (PICARD) visible through shortwave infrared imaging spectrometer was developed to carry a calibration laboratory environment to high altitudes, while also providing high-dynamic-range bright cloud-top radiance measurements across a field of view just under 50 degrees. The in-flight performance of this new spectroradiometer was validated in comparison to multiple reference data sources and targets using imagery collected aboard NASA’s ER-2 high-altitude aircraft during the Western Diversity Time Series (WDTS) airborne science campaign in April 2023 and the September 2024 Plankton, Aerosol, Cloud, and ocean Ecosystem (PACE) Postlaunch Airborne eXperiment (PACE-PAX), both operating out of southern California. PICARD measurements from flights over Railroad Valley Playa, Nevada, USA, were compared to high-resolution radiance spectra of the dry lakebed provided by the Radiometric Calibration Network (RadCalNet) Working Group. Direct comparison to satellite cloud radiometry was enabled by the ER-2 flying in coordination with simultaneous overpasses of the Terra, Aqua, and NOAA-20 Earth-observing satellites during WDTS and with the PACE observatory during PACE-PAX. To account for large spectral differences between incandescent laboratory sources and solar illumination, PICARD calibration relies on measurements using the Goddard Laser for Absolute Measurements of Radiance (GLAMR) to characterize and minimize spectral stray light from the instrument’s twin Offner grating spectrometers. Good agreement in comparison to reference measurements demonstrates PICARD’s ability to provide imagery for environmental science or for testing new sensor designs and retrieval algorithms for cloud and aerosol research with verified laboratory calibrations at high altitudes. Full article

Unmanned aerial vehicles (UAVs) provide a flexible platform for surface reflectance measurement at spatial scales between ground observations and satellite remote sensing. This study develops a UAV-based spectroradiometric system for surface reflectance retrieval under natural illumination conditions using non-imaging hyperspectral sensors. The system integrates two stabilized spectroradiometers mounted on a UAV to simultaneously measure hemispherical downwelling irradiance and upwelling surface radiance at flight altitude, enabling reflectance retrieval through a radiance–irradiance ratio framework without relying on ground calibration targets or radiative transfer model inversion. Field experiments were conducted over agricultural plots, and the UAV-derived reflectance was quantitatively validated against ground-based dual-spectroradiometer measurements. The results demonstrate stable irradiance measurements during flight and good agreement between UAV- and ground-derived reflectance across the 400–900 nm spectral range. The proposed system offers a practical and reliable solution for hyperspectral reflectance retrieval using UAV platforms. Full article

Dragon fruit (Hylocereus spp.) is an emerging crop in the tropics and subtropics, but its production is increasingly threatened by diseases that reduce yield and profitability. Early diagnosis of these diseases is crucial for timely intervention, yet visual symptoms often appear only after significant infection has occurred. The study aims to evaluate how optical spectral reflectance can detect dragon fruit diseases and identify the most responsive spectral regions. In this study, six major dragon fruit stem diseases: Neoscytalidium stem canker, stem sunburn, anthracnose, Botryosphaeria stem canker, Bipolaris stem rot, and bacterial soft rot were characterized by the goal of identifying unique spectral signatures for early detection and differentiation of each disease. Seventy-two potted dragon fruit plants of three distinct species were grown under four organic vermicompost treatments (0, 5, 10, 20 tons/acre) in both open-field and high-tunnel conditions together, in a randomized complete block design. A handheld spectroradiometer (350–2500 nm) was used to collect reflectance from the diseased and healthy cladodes (stem segment). Various spectral vegetative indices were computed to identify disease-specific features. The results revealed distinct spectral features for each disease. Infected cladodes consistently exhibited higher reflectance especially in the visible region (400–700 nm) and the near-infrared region (900–2500 nm) of the spectrum than healthy cladodes. The Normalized Difference Vegetative Index (NDVI), Green Normalized Difference Vegetative Index (GNDVI), and Spectral Ratio (SR) spectral indices were significantly higher in healthy plants than in diseased ones, reflecting higher chlorophyll concentration and plant biomass. Conversely, the 1110/810 ratio was lower in healthy plants than in diseased plants, suggesting a more compact internal plant structure. Statistical analysis revealed highly significant differences (p < 0.00001) between healthy and diseased spectra in the Red, Green and NIR regions. Linear Discriminant Analysis(LDA) achieved the highest classification accuracy (OA = 0.642, κ = 0.488), though performance was limited for minority classes. These findings demonstrate that targeted spectral sensing can identify dragon fruit diseases before obvious symptoms emerge. By pinpointing disease-specific spectral indices, our study paves the way for early-warning tools such as targeted multispectral sensors or drone-based imaging that would enable growers to intervene sooner and limit losses. These results highlight the potential for development of UAV-based or portable spectral sensors for large-scale, near real-time disease monitoring in dragon fruit production. Full article

Live Fuel Moisture Content (LFMC) is a key determinant of vegetation flammability and fire behaviour, yet LFMC products have traditionally relied on coarse-resolution sensors such as the Moderate Resolution Imaging Spectroradiometer (MODIS, 500 m), limiting their utility for fine-scale fire management. This study introduces the first continental-scale operational LFMC product for Australia derived from Sentinel-2 imagery at 20 m resolution. We developed a Random Forest regression model trained on approximately 680,000 paired Sentinel-2 reflectance and MODIS-LFMC samples (2015–2022) to emulate outputs from the Australian Flammability Monitoring System (AFMS), a MODIS-based pre-operational LFMC product. Model evaluation against AFMS showed strong agreement for grasslands (R² = 0.83, RMSE = 32.45%) and moderate performance for forests (R² = 0.43, RMSE = 20.84%) and shrublands (R² = 0.21, RMSE = 10.28%). Validation using 2279 in situ LFMC measurements from Globe-LFMC 2.0 indicated improved accuracy at homogeneous sites (NDVI CV ≤ 20th percentile: R² = 0.42, RMSE = 31.39%). Additionally, when validating with a dedicated field campaign specifically designed for Sentinel-2 LFMC assessment, the model achieved its highest accuracy (R² = 0.53, RMSE = 32.14%), highlighting the importance of tailored ground protocols for satellite product validation. Predicted LFMC also reproduced observed seasonal dynamics at sites with frequent field monitoring. Despite variability across vegetation types, the Sentinel-2 LFMC product effectively captured spatial patterns and seasonal dynamics, providing a step change in monitoring vegetation moisture at landscape scales. This high-resolution dataset offers actionable intelligence for prescribed burning, fuel treatment planning, and fire behaviour modelling in fire-prone environments. Full article

This study employs a hybrid methodology that integrates a physical process-based radiative transfer (RT) model and machine learning regression to assess three key wheat crop traits: leaf area index (LAI), leaf chlorophyll content (LCC), and canopy chlorophyll content (CCC). The non-imaging hyperspectral data collected proximally using the ASD FieldSpec Spectroradiometer were spectrally resampled to 269 spectral bands ranging from 400 to 1000 nm for the retrieval of these crop traits. Upon validating against in situ measurements, good accuracies in terms of NRMSE values, 10.65%, 11.63%, and 13.85%, were achieved for LAI, LCC, and CCC, respectively. Full article

Rapid, standardised discrimination of soil taxonomic units remains challenging when relying solely on conventional field descriptions and laboratory analyses, particularly at high sampling densities. This study evaluated whether proximal spectroscopy and hyperspectral imaging can support the classification of Brazilian Soil Classification System (SiBCS) suborders and pedogenetic horizons when surface and subsurface spectra are treated separately. Six intact soil monoliths (0.12 × 1.60 m) were collected in Paraná State, southern Brazil, representing one Organossolo (Ooy), three Latossolos (LVd, LVd1, and LVd2) and two Argissolos (PVAd and PVd). For each monolith, 800 spectra were acquired per sensor with a non-imaging VIS–NIR–SWIR spectroradiometer (350–2500 nm), and 800 spectra per sensor per monolith were extracted from the SWIR hyperspectral images (1200–2450 nm). Principal component analysis (PCA) was used to summarise spectral variability, and supervised classification was performed via k-nearest neighbours, random forest, decision tree and gradient boosting for suborders (10-fold cross-validation), and a neural network was used for within-profile horizon classification. PCA indicated that most of the spectral variance was captured by a dominant axis, with clearer separation among suborders in the SWIR space than in the full VIS–NIR–SWIR range. With respect to suborder classification, subsurface spectra outperformed surface spectra, and SWIR outperformed VIS–NIR–SWIR: the best accuracies were 0.96 for subsurface SWIR (gradient boosting; AUC = 0.99; MCC = 0.95) and 0.89 for surface SWIR (k-nearest neighbours; AUC = 0.98; MCC = 0.87). Within-profile horizon classification via VIS–NIR–SWIR achieved accuracies of 0.84–0.97 with the Neural Network, with most misclassifications occurring between adjacent horizons. Overall, subsurface SWIR information provided the most reliable basis for taxonomic discrimination, whereas horizon classification was feasible but reflected gradual spectral transitions along the profile. Full article

Aerosol dry deposition is an important sink for particulate matter and a source of uncertainty in air quality modeling. Using the Weather Research and Forecasting model coupled with CUACE (WRF-CUACE), we quantified how three aerosol dry deposition schemes and satellite-based leaf area index (LAI) information affected PM_2.5 dry removal and near-surface PM_2.5 over central and eastern China in January 2022. The schemes were abbreviated as Z01, E20, and PZ10, respectively. A fourth simulation (PZ10_MLAI) used PZ10 but replaced the baseline LAI dataset with a Moderate Resolution Imaging Spectroradiometer (MODIS) constrained LAI field. Hourly PM_2.5 was evaluated with the China National Environmental Monitoring Center network. The schemes produced pronounced, size-dependent differences in deposition velocities, with a pronounced spread in the 0 to 2.5 µm average and more than one order of magnitude spread in the accumulation mode diagnostic, leading to distinct regional mean PM_2.5 dry deposition fluxes. The mean PM_2.5 flux increased by 5.9% in E20 relative to Z01 and decreased by 54.4% in PZ10. The MODIS LAI adjustment changed the PZ10 mean flux by 0.42%. The flux contrasts yielded coherent PM_2.5 responses, with E20 reducing near-surface concentrations by about 10 to 30% and PZ10 increasing them by about 20 to 60%, reaching about 80 to 100% in parts of southern China. Domain mean correlations ranged from 0.61 to 0.65 and PZ10-based simulations exhibited near-zero mean bias. Although MODIS LAI effects were modest for this winter month, local PM_2.5 differences commonly remained within about 4% and approached 6 to 10%, indicating that satellite LAI constraints can be important for multi-year and decadal applications. Full article

Italy is estimated to host thousands of abandoned mines, many of which contain large volumes of mine residues that negatively affect land and aquatic ecosystems, also posing a risk to human health. This study evaluates the effectiveness of spaceborne imaging spectroscopy combined with laboratory spectroscopy for characterizing the mineralogy and geochemistry of residues from the abandoned Montevecchio sulfide mine in southwestern Sardinia, a site recognized as a significant source of environmental pollution. Mine tailings and their downstream dispersion along the Rio Irvi River were systematically studied and sampled in the field. Collected samples were analyzed in the lab using an Analytical Spectral Device (ASD) spectroradiometer, complemented by powder X-ray Diffraction (XRD) for mineralogical characterization. Affected zones were subsequently mapped using the Environmental Mapping and Analysis Program (EnMAP) hyperspectral satellite data at a 30 m spatial resolution, by applying a polynomial fitting technique to the image spectra. The results reveal the presence of Fe- and Zn-bearing sulfates and oxy/hydroxides, indicative of acidic-to-circum-neutral drainage conditions in the mine tailings and along affected streams. Specifically, EnMAP was able to detect jarosite and subtle chemical and physical variations in Fe-hydroxides. This integrated approach enabled the delineation of environmental conditions and zones with varying acidity based on the spectral characteristics of secondary minerals. Overall, the study demonstrates the potential of EnMAP data for mapping acid mine drainage and assessing environmental impacts in legacy mining areas. Full article

High-resolution TIR missions require sustained and well-characterized radiometric accuracy to support applications such as land surface temperature retrieval, drought monitoring, and surface energy budget analysis. To address this need, we develop an operational and automated ground-based vicarious radiometric calibration framework for TIR sensors and demonstrate its performance using the Wide-swath Thermal Infrared Imager (WTI) onboard Gaofen-5 01A (GF-5A). Three arid Gobi calibration sites were selected by integrating Moderate Resolution Imaging Spectroradiometer (MODIS) cloud products, Shuttle Radar Topography Mission (SRTM)-derived topography, and WTI-based radiometric uniformity metrics to ensure low cloud cover, flat terrain, and high spatial homogeneity. Automated ground stations deployed at Golmud, Dachaidan, and Dunhuang have continuously recorded 1 min contact surface temperature since October 2023. Field-measured emissivity spectra, Integrated Global Radiosonde Archive (IGRA) radiosonde profiles, and MODTRAN (MODerate resolution atmospheric TRANsmission) v5.2 simulations were combined to compute top-of-atmosphere (TOA) radiances, which were subsequently collocated with WTI imagery. After data screening and gain-stratified regression, linear calibration coefficients were derived for each TIR band. Based on 189 scenes from February–July 2024, all four bands exhibit strong linearity (R-squared greater than 0.979). Validation using 45 independent scenes yields a mean brightness–temperature root-mean-square error (RMSE) of 0.67 K. A full radiometric-chain uncertainty budget—including contact temperature, emissivity, atmospheric profiles, and radiative transfer modeling—results in a combined standard uncertainty of 1.41 K. The proposed framework provides a low-maintenance, traceable, and high-frequency solution for the long-term on-orbit radiometric calibration of GF-5A WTI and establishes a reproducible pathway for future TIR missions requiring sustained calibration stability. Full article

Water is vital for producing summer maize (SM) and winter wheat (WW); therefore, its proper management is crucial for sustainable farming. This study aimed to develop new tri-band spectral vegetation indices that enhance the accuracy of monitoring plant moisture content (PMC) in SM and WW. We conducted irrigation treatments, including W0, W1, W2, W3, and W4, in SM–WW rotations to address this issue. Canopy reflectance was measured with a field spectroradiometer. Tri-band hyperspectral vegetation indices were constructed: Normalised Water Stress Index (NWSI), Normalised Difference Index (NDI), and Exponential Water Stress Index (EWSI), for assessing the PMC of SM and WW. Results indicate that NWSI outperformed other indices. In the maize trials, the correlation reached R = −0.8369, while in wheat, it reached R = −0.9313, surpassing traditional indices. Four mainstream machine learning models (Random Forest, Partial Least Squares Regression, Support Vector Machine, and Artificial Neural Network) were employed for modelling. NWSI-PLSR exhibited the best index-type performance with an R² of 0.7878. When the new indices were combined with traditional indices as input data, the NWSI-Published indices-SVM model achieved superior performance with an R² of 0.8203, outperforming other models. The RF model produced the most consistent performance and achieved the highest average R² across all input types. The NDI-Published indices models also outperformed those of the published indices alone. This indicates that these new indices improve the accuracy of moisture content monitoring in SM and WW fields. It provides a technical basis and support for precision irrigation, holding significant potential for application. Full article

The absolute radiometric calibration of a satellite sensor is an essential process that determines the coefficients required to convert the radiometric quantities of satellite images. This procedure is crucial for ensuring the applicability and enhancing the reliability of optical sensors onboard satellites. This study performs the absolute radiometric calibration of the Compact Advanced Satellite 500-1 (CAS500-1) Advanced Earth Imaging Sensor System-C (AEISS-C), a low Earth orbit satellite developed independently by Republic of Korea for precise ground observation. Field campaign using a tarp, an Analytical Spectral Devices FieldSpecIII spectroradiometer, and a MicrotopsII sunphotometer was conducted. Additionally, reflectance-based vicarious calibration was performed using observational data and the MODerate resolution atmospheric TRANsmission model (version 6) radiative transfer model (RTM). Cross-calibration was also performed using data from the Sentinel-2 MultiSpectral Instrument, RadCalNet observations, and MODIS Bidirectional nReflectance Distribution Function (BRDF) products (MCD43A1) to account for differences in spectral response functions, viewing/solar geometry, and atmospheric conditions between the two satellites. From these datasets, two correction factors were derived: the Spectral Band Adjustment Factor and the BRDF Correction Factor. CAS500-1/AEISS-C acquires satellite imagery using two Time Delay Integration (TDI) modes, and the absolute radiometric calibration coefficients were derived considering these TDI modes. The coefficient of determination (R²) ranged from 0.70 to 0.97 for the reflectance-based vicarious calibration and from 0.90 to 0.99 for the cross-calibration. For reflectance-based vicarious calibration, aerosol optical depth was identified as the primary source of uncertainty among atmospheric factors. For cross-calibration, the reference satellite and RTMs were the primary sources of uncertainty. The results of this study will support the monitoring of CAS500-1/AEISS-C, which produces high-resolution imagery with a spatial resolution of 2 m, and can serve as foundational material for absolute radiometric calibration procedures for other CAS500 satellites. Full article

The accurate monitoring of water vapor is essential for understanding the hydrological cycle and improving weather forecasting. Although the Moderate-resolution Imaging Spectroradiometer (MODIS) provides spatially continuous precipitable water vapor (PWV), validation in Hunan Province reveals a systematic underestimation, with correlations to radiosonde (RS-PWV) around 0.40 and average RMSE and MAE reaching 23.80 and 18.04 mm. To address this issue, high-accuracy PWV derived from the BeiDou Navigation Satellite System (BDS-PWV), which show high consistency with RS-PWV, were incorporated. A random forest daily-scale water vapor fusion model was developed based on the differential characteristics of dry and wet season residuals. By employing day of year (DOY), latitude, longitude, and elevation as auxiliary factors, the model establishes a seasonal fusion framework that dynamically transitions between dry and wet seasons. Validation shows that the fusion PWV aligns closely with RS-PWV, reducing average RMSE and MAE to 4.71 and 3.81 mm, corresponding to improvements of 80.21% and 78.88% over MODIS, with accuracy increases exceeding 75% at all stations. The fusion model effectively mitigates MODIS’s underestimation and weather sensitivity, producing high-accuracy, spatially continuous daily PWV fields and offering strong potential for improving precipitation and weather forecasting in complex regions such as Hunan Province. Full article

Phosphate is a crucial non-renewable mineral resource, mainly utilized in producing fertilizers that support global agriculture. As phosphorus is an indispensable nutrient for plant growth, phosphate holds a key position in ensuring food security. While deposits are distributed worldwide, the largest reserves are concentrated in Morocco. The Benguerir phosphate mining in Morocco generates heterogeneous waste (i.e., including overburden, tailings, and phosphogypsum) that complicates management and valorization, which is the beneficial reuse or value recovery from waste materials (e.g., use in cover systems, buffering, or other engineered applications). Therefore, it is essential to characterize their mineralogical properties to evaluate their environmental impact and possibilities for reuse or site revegetation. To do so, we integrate VNIR–SWIR reflectance spectroscopy with HandHeld X-ray fluorescence (HHXRF) to characterize phosphate waste rock and assess its reuse potential. For this purpose, field samples (n = 104) were collected, and their spectral reflectance was measured using an ASD FieldSpec 4 spectroradiometer (350–2500 nm) under standardized laboratory conditions. Spectra were processed (Savitzky–Golay smoothing, convex-hull continuum removal) and matched to ECOSTRESS library references; across the dataset, library matching achieved mean RMSE = 0.15 ± 0.053 (median 0.145; 0.085–0.350), median SAM = 0.134 rad, median SID = 0.029, and mean R² = 0.748 ± 0.170, with 84% of spectra yielding R² > 0.70. In parallel, HHXRF major and trace elements were measured on all samples to corroborate spectral interpretations. Together, these analyses resolve carbonate–clay–phosphate assemblages (dolomite commonly dominant, with illite/smectite–kaolinite, quartz, and residual carbonate-fluorapatite varying across samples). Elemental ratios (e.g., Mg/Ca distinguishing dolomite from calcite; K/Al indicating illite) reinforce spectral trends, and phosphate indicators delineate localized enrichment (P₂O₅ up to 23.86 wt % in apatite-rich samples). Overall, the combined workflow is rapid, low-impact, and reproducible, yielding coherent mineralogical patterns that align across spectroscopic and geochemical lines of evidence and providing actionable inputs for selective screening, targeted material reuse, and more sustainable mine reclamation planning. Full article

The cereal leaf beetle (CLB; Oulema melanopus L., Coleoptera: Chrysomelidae) is a serious pest of wheat, capable of causing yield losses of up to 40% through photosynthetic impairment. Early detection and severity assessment are essential for effective and sustainable pest management. This study evaluates the potential of hyperspectral remote sensing (RS) combined with machine learning (ML) for non-invasive detection of CLB-induced stress in winter wheat. Spectral reflectance was measured using a full-range spectroradiometer (350–2500 nm) from flag leaves categorized into four damage levels (healthy, slightly, moderately, and severely damaged). Three input datasets were used for ML classification: full spectral reflectance, a set of 13 vegetation indices (VIs), and outputs of dimensionality reduction technique. CLB stress increased reflectance in the visible range (400–700 nm) and reduced it in the near-infrared (700–1400 nm), consistent with chlorophyll degradation and mesophyll damage. Several VIs, including RIGreen, NDVI750, GNDVI, and NDVI, correlated strongly with damage severity (τ = 0.78–0.81). Among the six ML models tested, Support Vector Machine (SVM) achieved the highest classification accuracy of 90.0% (precision = 0.90, recall = 0.90, F1 = 0.90) across the four severity classes, and achieved 91.9% accuracy at the early-detection threshold. As far as the currently available literature indicates, this study provides one of the earliest quantitative assessments of CLB damage severity based on full-spectrum leaf-level hyperspectral reflectance integrated with ML classification. These findings were obtained under controlled, leaf-level measurement conditions and therefore represent a proof-of-concept; future validation using UAV and satellite platforms is needed to assess performance under operational field variability. Overall, our findings highlight the potential of hyperspectral RS and ML for precision pest monitoring, supporting threshold-based decision-making and more sustainable insecticide use. Full article

Climate variability and extreme weather events, particularly flooding, pose growing threats to agricultural productivity worldwide, including in Pakistan. Traditional crop damage assessments during flood events have relied on field surveys, which are often time-intensive and spatially limited. Recent advancements in remote sensing technologies now allow for rapid and large-scale estimation of flood-induced agricultural damage. This study assesses agricultural damage from two recent extreme flood events in Pakistan, integrating crop condition and flood intensity metrics. We present remote sensing-based case studies that employ an interdisciplinary approach, using Moderate Resolution Imaging Spectroradiometer (MODIS), Sentinel-1, and Sentinel-2 imagery along with crop data. Our results show that flood timing, crop stage, and inundation duration were the most influential factors in determining crop loss. We determined that Northern Sindh province and areas along the Indus River and its tributaries are highly vulnerable to flooding, resulting in extensive damage to infrastructure, crops, and loss of lives during flood events in 2010 and 2022, followed by Punjab, Balochistan, and Khyber Pakhtunkhwa. Remote sensing-derived damage estimates were closely aligned with post-event ground reports, validating the approach. Full article