Search Results (396)

Radiometric consistency across satellite platforms is fundamental to producing high-quality Climate Data Records (CDRs). Because different cross-calibration methods have distinct advantages and limitations, comparative evaluation is necessary to ensure record accuracy. This study presents a comparative assessment of two widely applied calibration approaches—Simultaneous Nadir Overpass (SNO) and Double Difference (DD)—for the thermal infrared (TIR) bands of FY-3D MERSI. MODIS/Aqua serves as the reference sensor, while radiative transfer simulations driven by ERA5 inputs are generated with the Advanced Radiative Transfer Modeling System (ARMS) to support the analysis. The results show that SNO performs effectively when matchup samples are sufficiently large and globally representative but is less applicable under sparse temporal sampling or orbital drift. In contrast, the DD method consistently achieves higher calibration accuracy for MERSI Bands 24 and 25 under clear-sky conditions. It reduces mean biases from ~−0.5 K to within ±0.1 K and lowers RMSE from ~0.6 K to 0.3–0.4 K during 2021–2022. Under cloudy conditions, DD tends to overcorrect because coefficients derived from clear-sky simulations are not directly transferable to cloud-covered scenes, whereas SNO remains more stable though less precise. Overall, the results suggest that the two methods exhibit complementary strengths, with DD being preferable for high-accuracy calibration in clear-sky scenarios and SNO offering greater stability across variable atmospheric conditions. Future work will validate both methods under varied surface and atmospheric conditions and extend their use to additional sensors and spectral bands. Full article

Atmospheric correction (AC) over inland and coastal waters remains a key challenge in ocean color remote sensing, often limiting the effective use of satellite data for aquatic monitoring. AC algorithm performance is highly sensitive to water type and optical properties. To address this, we systematically evaluated six state-of-the-art AC algorithms—ACOLITE, C2RCC, iCOR, L2GEN, OC-SMART, and POLYMER—using Landsat-8/9 OLI data. This study leverages 440 high-quality in situ radiometric matchups spanning a wide range of aquatic environments, including inland lakes from China’s Satellite-Ground Synchronous Campaign and coastal waters from the globally distributed GLORIA dataset. These complementary datasets provide a robust benchmark for evaluating AC algorithm performance. A unified Optical Water Type (OWT) classification framework ensured consistency across environmental conditions. Results highlight significant variability in algorithm performance based on water type. In coastal waters, L2GEN demonstrated the lowest errors in visible bands, whereas OC-SMART achieved superior overall accuracy in inland waters. Notably, ACOLITE exhibited better performance than other algorithms in the blue spectral region (443 and 482 nm) for inland waters. OWT-specific analysis showed that OC-SMART maintained robust accuracy across the turbidity gradient, while ACOLITE and iCOR excelled in highly turbid waters (OWTs 5–6). In contrast, L2GEN, C2RCC, and POLYMER were more effective in clearer waters (OWTs 3–4). The study further discusses the applicability of each algorithm and offers recommendations for mitigating adjacency effects (AE) to improve AC accuracy. These findings provide valuable guidance for selecting and optimizing AC strategies for inland and coastal water monitoring. Full article

Accurate computation of external heat flux is critical for spacecraft thermal analysis and thermal control system design. The traditional method, which adopted the uniform planetary infrared radiation model (UPIRM), is inadequate for lunar orbital missions due to the extreme planetary surface temperature variations. This study proposes an external heat flux calculation method for lunar orbits by integrating a non-uniform lunar surface temperature model derived from Lunar Reconnaissance Orbiter (LRO) Diviner radiometric data. Specifically, the lunar surface temperature data were first fitted as functions of latitude ($\psi$) and position angles ($\zeta$) through data regression analysis. Then, a comprehensive mathematical framework is established to analyze solar radiation, lunar albedo, and lunar infrared radiation components, incorporating orbital parameters such as beta angle ($\beta$), orbital inclination ($i$) and so on. Coordinate transformations and numerical integration techniques are employed to evaluate heat flux distributions across cuboidal orbiter surfaces. It is found that the lunar infrared radiation heat flux manifests pronounced fluctuation, peaking at 1023 W/m² near the lunar noon region while plummeting to 20 W/m² near the midnight region under the orbital parameters investigated in this study. This study demonstrates the essential role of the non-uniform planetary infrared radiation model (NUPIRM) in enhancing prediction accuracy by contrast, offering foundational references for thermal management in future lunar and deep-space exploration spacecraft. Full article

With the advancement of precision agriculture, Unmanned Aerial Vehicle (UAV)-based remote sensing has been increasingly employed for monitoring crop water and nutrient status due to its high flexibility, fine spatial resolution, and rapid data acquisition capabilities. This review systematically examines recent research progress and key technological pathways in UAV-based remote sensing for crop water and nutrient monitoring. It provides an in-depth analysis of UAV platforms, sensor configurations, and their suitability across diverse agricultural applications. The review also highlights critical data processing steps—including radiometric correction, image stitching, segmentation, and data fusion—and compares three major modeling approaches for parameter inversion: vegetation index-based, data-driven, and physically based methods. Representative application cases across various crops and spatiotemporal scales are summarized. Furthermore, the review explores factors affecting monitoring performance, such as crop growth stages, spatial resolution, illumination and meteorological conditions, and model generalization. Despite significant advancements, current limitations include insufficient sensor versatility, labor-intensive data processing chains, and limited model scalability. Finally, the review outlines future directions, including the integration of edge intelligence, hybrid physical–data modeling, and multi-source, three-dimensional collaborative sensing. This work aims to provide theoretical insights and technical support for advancing UAV-based remote sensing in precision agriculture. Full article

The Geostationary Interferometric Infrared Sounder (GIIRS) onboard the Fengyun-4B satellite plays a critical role in numerical weather prediction and extreme weather monitoring. To meet the requirements of quantitative remote sensing and high-precision operational applications for radiometric calibration accuracy, this study, based on pre-launch calibration experiments, conducts a novel modeling analysis of the coupling between stray radiation at the input side and the system’s nonlinearity, and proposes a correction method for nonlinear coupling errors. This method explicitly models and physically traces the calibration residuals caused by stray radiation introduced via non-nominal optical paths under the effect of system nonlinearity, which are related to the radiance of the observed target. Experimental results show that, within the brightness temperature range of 200–320 K, the calibration bias is reduced from approximately 0.7 to 0.3–0.4 K, with good consistency and stability observed across channels and pixels. Full article

Chlorophyll-a (Chla) and suspended particulate matter (SPM) are key indicators of water quality, playing critical roles in understanding marine biogeochemical processes and ecosystem health. Although satellite data from the Chinese Ocean Color and Temperature Scanner (COCTS) onboard the Haiyang-1C/D satellites is freely available, there has been limited validation of its standard Chla and SPM products. This study is a first step to address this gap by evaluating COCTS-derived Chla and SPM products against in situ measurements in French coastal waters. The matchup analysis showed robust performance for the Chla product, with a median symmetric accuracy (MSA) of 50.46% over a dynamic range of 0.13–4.31 mg·m⁻³ (n = 24, Bias = 41.11%, Slope = 0.93). In contrast, the SPM product showed significant limitations, particularly in turbid waters, despite a reasonable performance in the matchup exercise, with an MSA of 45.86% within a range of 0.18–10.52 g·m⁻³ (n = 23, Bias = −14.59%, Slope = 2.29). A comparison with another SPM model and Moderate Resolution Imaging Spectroradiometer (MODIS) products showed that the COCTS standard algorithm tends to overestimate SPM and suggests that the issue does not originate from the input radiometric data. This study provides the first regional assessment of COCTS Chla and SPM products in European coastal waters. The findings highlight the need for algorithm refinement to improve the reliability of COCTS SPM products, while the Chla product demonstrates suitability for water quality monitoring in low to moderate Chla concentrations. Future studies should focus on the validation of COCTS ocean color products in more diverse waters. Full article

The Flexible Combined Imager (FCI), developed as the next-generation imager for the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) Meteosat Third Generation (MTG) satellite series, represents a significant advancement over its predecessor, SEVIRI, on the Meteosat Second Generation (MSG) satellites. FCI offers more spectral bands, higher spatial resolution, and faster imaging capabilities, supporting a wide range of applications in weather forecasting, climate monitoring, and environmental analysis. On 13 January 2024, the FCI onboard MTG-I1 (renamed Meteosat-12 in December 2024) experienced a critical anomaly involving the failure of its onboard Calibration and Obturation Mechanism (COM). As a result, the use of the COM was discontinued to preserve operational safety, leaving the instrument dependent on alternative calibration methods. This loss of onboard calibration presents immediate challenges, particularly for the infrared channels, including image artifacts (e.g., striping), reduced radiometric accuracy, and diminished stability. To address these issues, EUMETSAT implemented an external calibration approach leveraging algorithms from the Global Space-based Inter-Calibration System (GSICS). The inter-calibration algorithm transfers stable and accurate calibration from the Infrared Atmospheric Sounding Interferometer (IASI) hyperspectral instrument aboard Metop-B and Metop-C satellites to FCI’s infrared channels daily, ensuring continued data quality. Comparisons with Cross-track Infrared Sounder (CrIS) data from NOAA-20 and NOAA-21 satellites using a similar algorithm is then used to validate the radiometric performance of the calibration. This confirms that the external calibration method effectively compensates for the absence of onboard blackbody calibration for the infrared channels. For the visible and near-infrared channels, slower degradation rates and pre-anomaly calibration ensure continued accuracy, with vicarious calibration expected to become the primary source. This adaptive calibration strategy introduces a novel paradigm for in-flight calibration of geostationary instruments and offers valuable insights for satellite missions lacking onboard calibration devices. This paper details the COM anomaly, the external calibration process, and the broader implications for future geostationary satellite missions. Full article

Stripe noise along the scanning direction significantly degrades the quality of high-resolution infrared line-scan images and impairs downstream tasks such as target detection and radiometric analysis. This paper presents a lightweight, single-frame, reference-free non-uniformity correction (NUC) method tailored for such images. The proposed approach enhances the directionality of stripe noise by projecting the 2D image into a 1D row-mean signal, followed by adaptive guided filtering driven by local median absolute deviation (MAD) to ensure spatial adaptivity and structure preservation. A spectral-entropy-constrained frequency-domain masking strategy is further introduced to suppress periodic and non-periodic interference. Extensive experiments on simulated and real datasets demonstrate that the method consistently outperforms six state-of-the-art algorithms across multiple metrics while maintaining the fastest runtime. The proposed method is highly suitable for real-time deployment in airborne, satellite-based, and embedded infrared imaging systems. It provides a robust and interpretable framework for future infrared enhancement tasks. Full article

This study was realized in the frame of an IAEA Coordinated Research Project for the evaluation of sediment dynamics, applying in-situ radiometric methods accompanied with a theoretical model. The in-situ methods were validated using lab-based high-resolution gamma-ray spectrometry. Sediment dynamics assessments were performed based on the measured and mapped activity concentrations of specific ²³⁸U progenies (²¹⁴Bi or ²¹⁴Pb), ²³²Th progenies (²⁰⁸Tl and ²²⁸Ac), and ⁴⁰K along the shoreline of the beach. The maps of the activity concentrations of natural radionuclides were produced rapidly using software tools (R language v4.5). The sediment dynamics of the studied area were also investigated through numerical simulations, applying an open source model considering land–sea interactions and meteorological conditions and the corresponding sediment processes. The assessments, which were conducted utilizing the detailed data from the natural radioactivity maps, were validated by the simulation results, since both were found to be in agreement. Generally, it was confirmed that the distribution of radionuclides reflects the selective transport processes of sediments, which are related to the corresponding processes that occur in the study area. Legrena Beach in Attica, Greece, served as a pilot area for the comparative analysis of methods and demonstration of their relevance and applicability for studying coastal processes. Full article

Sugarcane yield prediction plays a pivotal role in enabling farmers to monitor crop development and optimize cultivation practices, guiding harvesting operations for sugar mills. In this study, we established three experimental fields, which were planted with three main sugarcane cultivars in Guangxi, China, respectively, implementing a multi-gradient fertilization design with 39 plots and 810 sampling grids. Multispectral imagery was acquired by unmanned aerial vehicles (UAVs) during five critical growth stages: mid-tillering (T1), late-tillering (T2), mid-elongation (T3), late-elongation (T4), and maturation (T5). Following rigorous image preprocessing (including stitching, geometric correction, and radiometric correction), 16 VIs were extracted. To identify yield-sensitive vegetation indices (VIs), a spectral feature selection criterion combining gray relational analysis and correlation analysis (GRD-r) was proposed. Subsequently, three supervised learning algorithms—Gradient Boosting Decision Tree (GBDT), Random Forest (RF), and Support Vector Machine (SVM)—were employed to develop both single-stage and multi-stage yield prediction models. Results demonstrated that multi-stage models consistently outperformed their single-stage counterparts. Among the single-stage models, the RF model using T3-stage features achieved the highest accuracy (R² = 0.78, RMSE_V = 7.47 t/hm²). The best performance among multi-stage models was obtained using a GBDT model constructed from a combination of DVI (T1), NDVI (T2), TDVI (T3), NDVI (T4), and SRPI (T5), yielding R² = 0.83 and RMSE_V = 6.63 t/hm². This study highlights the advantages of integrating multi-temporal spectral features and advanced machine learning techniques for improving sugarcane yield prediction, providing a theoretical foundation and practical guidance for precision agriculture and harvest logistics. Full article

High-precision hyperspectral remote sensing reflectance measurement of water bodies serves as the fundamental technical basis for accurately retrieving spatiotemporal distribution characteristics of water quality parameters, providing critical data support for dynamic monitoring of aquatic ecosystems and pollution source tracing. To address the critical issue of water surface glint interference significantly affecting measurement accuracy in aquatic remote sensing, this study innovatively developed a novel sensor system based on multi-field-of-view Kohler-polarization technology. The system incorporates three Kohler illumination lenses with exceptional surface uniformity exceeding 98.2%, effectively eliminating measurement errors caused by water surface brightness inhomogeneity. By integrating three core technologies—multi-field polarization measurement, skylight blocking, and high-precision radiometric calibration—into a single spectral measurement unit, the system achieves radiation measurement accuracy better than 3%, overcoming the limitations of traditional single-method glint suppression approaches. A glint removal efficiency (GRE) calculation model was established based on a skylight-blocked approach (SBA) and dual-band power function fitting to systematically evaluate glint suppression performance. Experimental results show that the system achieves GRE values of 93.1%, 84.9%, and 78.1% at ±3°, ±7°, and ±12° field-of-view angles, respectively, demonstrating that the ±3° configuration provides a 9.2% performance improvement over the ±7° configuration. Comparative analysis with dual-band power-law fitting reveals a GRE difference of 2.1% (93.1% vs. 95.2%) at ±3° field-of-view, while maintaining excellent consistency (ΔGRE < 3.2%) and goodness-of-fit (R² > 0.96) across all configurations. Shipborne experiments verified the system’s advantages in glint suppression (9.2%~15% improvement) and data reliability. This research provides crucial technical support for developing an integrated water remote sensing reflectance monitoring system combining in situ measurements, UAV platforms, and satellite observations, significantly enhancing the accuracy and reliability of ocean color remote sensing data. Full article

Accurate and timely flood mapping is critical for informing emergency response and risk mitigation during extreme weather events. This study presents a synthetic aperture radar (SAR)-based approach for rapid flood extent mapping using Sentinel-1 imagery, demonstrated for Tropical Storm Imelda (17–21 September 2019) in southeastern Texas. Dual-polarization Sentinel-1 SAR data (VH and VV) were processed by computing the VH/VV backscatter ratio, and the resulting ratio image was classified using a supervised Random Forest classifier to delineate water and land. All Sentinel-1 images underwent radiometric calibration, speckle noise filtering, and terrain correction to ensure precision in flood delineation. The Random Forest classifier achieved an overall flood mapping accuracy exceeding 94%, with Cohen’s kappa coefficients of approximately 0.75–0.80, demonstrating the approach’s reliability in distinguishing transient floodwaters from permanent water bodies. The spatial distribution of flooding was strongly influenced by topography and land cover. Analysis of Shuttle Radar Topography Mission (SRTM) digital elevation data revealed that low-lying, flat terrain was most vulnerable to inundation; correspondingly, the land cover types most affected were hay/pasture, cultivated land, and emergent wetlands. Additionally, urban areas with low-intensity development experienced extensive flooding, attributed to impervious surfaces exacerbating runoff. A strong, statistically significant correlation (R² = 0.87, p < 0.01) was observed between precipitation and flood extent, indicating that heavier rainfall led to greater inundation; accordingly, the areas with the highest rainfall totals (e.g., Jefferson and Chambers counties) experienced the most extensive flooding, as confirmed by SAR-based change detection. The proposed approach eliminates the need for manual threshold selection, thereby reducing misclassification errors due to speckle noise and land cover heterogeneity. Harnessing globally available Sentinel-1 data with near-real-time processing and a robust classifier, this approach provides a scalable solution for rapid flood monitoring. These findings underscore the potential of SAR-based flood mapping under adverse weather conditions, thereby contributing to improved disaster preparedness and resilience in flood-prone regions. Full article

Marine steel structures face severe corrosion risks due to harsh environmental conditions, posing significant logistical, economic, and safety challenges for inspection and maintenance. Traditional corrosion assessment methods are costly, labor-intensive, and potentially hazardous. This study evaluated the capabilities of visible-to-near-infrared hyperspectral imaging (HSI) for automating corrosion detection and severity classification in steel samples subjected to accelerated corrosion conditions simulating marine exposure. Marine steel coupons were partially coated to simulate protective paint and immersed in natural brackish water from the Panama Canal, creating varying corrosion levels. Hyperspectral images were acquired in controlled illumination conditions, calibrated radiometrically, and reduced in dimensionality via principal component analysis (PCA). Four machine learning models, including k-nearest neighbors, support vector machine, random forest, and multilayer perceptron, were tested for classifying corrosion severity. The multilayer perceptron achieved the highest accuracy at 96.18%, clearly distinguishing among five defined corrosion stages. These findings demonstrate that hyperspectral imaging, coupled with machine learning techniques, provides a viable, accurate, non-destructive methodology for assessing marine steel corrosion, potentially reducing costs, improving safety, and streamlining maintenance procedures. Full article

Landsat 8 Level 2 Collection 2 (L2C2) surface reflectance (SR) products are widely used in various scientific applications by the remote sensing community, where their accuracy is vital for reliable analysis. However, discrepancies have been observed at shorter wavelength bands, which can affect certain applications. This study investigates the root cause of these differences by analyzing the assumptions made in the Land Surface Reflectance Code (LaSRC), the atmospheric correction algorithm of Landsat 8, as currently implemented at United States Geological Survey Earth Resources Observation and Science (USGS EROS), and proposes a correction method. To quantify these discrepancies, ground truth SR measurements from the Radiometric Calibration Network (RadCalNet) and Arable Mark 2 sensors were compared with the Landsat 8 SR. Additionally, the surface pressure measurements from RadCalNet and the National Centers for Environmental Information (NCEI) were evaluated against the LaSRC-calculated surface pressure values. The findings reveal that the discrepancies arose from using a single scene center surface pressure for the entire Landsat 8 scene pixels. The pressure-related discrepancies were most pronounced in the coastal aerosol and blue bands, with greater deviations observed in regions where the elevation of the study area differed substantially from the scene center, such as Railroad Valley Playa (RVUS) and Baotao Sand (BSCN). To address this issue, an exponential correction model was developed, reducing the mean error in the coastal aerosol band for RVUS from 0.0226 to 0.0029 (about two units of reflectance), which can be substantial for dark vegetative and water targets. In the blue band, there is a smaller improvement in the mean error, from 0.0095 to −0.0032 (about half a unit of reflectance). For the green band, the reduction in error was much less due to the significantly lesser impact of aerosol on this band. Overall, this study underscores the need for a more precise estimation of surface pressure in LaSRC to enhance the reliability of Landsat 8 SR products in remote sensing applications. Full article

Cloud detection is a crucial task in satellite remote sensing, influencing applications such as vegetation indices, land use analysis, and renewable energy estimation. This study evaluates the performance of cloud masks generated for KOMPSAT-3 and KOMPSAT-3A imagery using the DeepLabV3+ deep learning model with a ResNet-101 backbone. To overcome the limitations of digital number (DN) data, Top-of-Atmosphere (TOA) reflectance was computed and used for model training. Comparative analysis between the DN and TOA reflectance demonstrated significant improvements with the TOA correction applied. The TOA reflectance combined with the NDVI channel achieved the highest precision (69.33%) and F1-score (59.27%), along with a mean Intersection over Union (mIoU) of 46.5%, outperforming all the other configurations. In particular, this combination was highly effective in detecting dense clouds, achieving an mIoU of 48.12%, while the Near-Infrared, green, and red (NGR) combination performed best in identifying cloud shadows with an mIoU of 23.32%. These findings highlight the critical role of radiometric correction and optimal channel selection in enhancing deep learning-based cloud detection. This study demonstrates the crucial role of radiometric correction, optimal channel selection, and the integration of additional synthetic indices in enhancing deep learning-based cloud detection performance, providing a foundation for the development of more refined cloud masking techniques in the future. Full article