Search Results (119)

To evaluate the radiometric performance of top-of-atmosphere reflectance images, Deep Convective Clouds (DCCs) can be used as temporally, spatially and spectrally stable targets. The DCCs method has been developed more than 20 years ago and applied recently to Sentinel-2 and Sentinel-3 sensors. In this paper, among other developments, we built a new methodology upon those existing by using the bootstrap method and spectral band adjustment factors computed with the Hyper-Spectral Imager (HSI) from the Environmental Mapping and Analysis Program (EnMAP). This methodology is applied to the two Multi-Spectral Imager (MSI) instruments onboard Sentinel-2A and 2B, but also the two Operational Land Imager (OLI) instruments onboard Landsat 8 and 9, from visible wavelength at 442 nm to shortwave-infrared at 2200 nm, using images with a ground resolution spanning from 10 m to 60 m. The results demonstrate the good inter-calibration of MSI units A and B, which are within one percent of relative difference on average between January 2022 and June 2024 for all visible, near-infrared and shortwave-infrared bands, except for the band at 1375 nm for which saturation prevents the use of the method. Similarly, OLI and OLI-2 are found to have a relative difference on the same period lower than one percent for all 30 m resolution bands. Evaluation of the relative difference between the MSI sensors and the OLI sensors with the DCCs method gives values lower than three percent. Finally, these validation results are compared to those obtained with Pseudo-Invariant Calibration Sites (PICSs) over Libya-4: an agreement better than two percent is found between the DCCs and PICSs methods. Full article

Rice farming in Italy accounts for about 50% of the EU’s rice area and production. Precision agriculture has entered the scene to enhance sustainability, cut pollution, and ensure food security. Various studies have used remote sensing tools like satellites and drones for multispectral imaging. While Sentinel-2 is highly regarded for precision agriculture, it falls short for specific applications, like at the “Riserva San Massimo” (Gropello Cairoli, Lombardia, Northern Italy) rice farm, where irregularly shaped crops need higher resolution and frequent revisits to deal with cloud cover. A prior study that compared Sentinel-2 and the higher-resolution PlanetScope constellation for vegetative indices found a seasonal miscalibration in the Normalized Difference Vegetation Index (NDVI) and in the Normalized Difference Red Edge Index (NDRE). Dr. Agr. G.N. Rognoni, a seasoned agronomist working with this farm, stresses the importance of studying the radiometric intercalibration between the PlanetScope and Sentinel-2 vegetative indices to leverage the knowledge gained from Sentinel-2 for him to apply variable rate application (VRA). A high-resolution SkySat image, taken almost simultaneously with a pair of Sentinel-2 and PlanetScope images, offered a chance to examine if the irregular distribution of vegetation and barren land within rice fields might be a factor in the observed miscalibration. Using an unsupervised pixel-based image classification technique on SkySat imagery, it is feasible to split rice into two subclasses and intercalibrate them separately. The results indicated that combining histograms and agronomists’ expertise could confirm SkySat classification. Moreover, the uneven spatial distribution of rice does not affect the seasonal miscalibration object of past studies, which can be adjusted using the methods described here, even with images taken four days apart: the first method emphasizes accuracy using linear regression, histogram shifting, and histogram matching; whereas the second method is faster and utilizes only histogram matching. Full article

According to European Union guidelines, the assessment of the ecological status of Transitional Water Systems (TWSs) should be based on the monitoring of biological communities rather than physico-chemical parameters and pollutants. Macrophytes, including aquatic angiosperms and macroalgae, are organisms that respond more quickly to environmental changes by varying the structure and biomass of their assemblages. There are several ecological indices based on macrophytes, among them the Macrophyte Quality Index (MaQI), which has been intercalibrated with water and sediment parameters, nutrient concentrations, and pollutants and is used to determine the ecological status of Italian TWSs. In the Venice Lagoon, it was applied to 87 stations, showing a significant score increase over the last ten years of monitoring (2011–2021) due to progressive lagoon environmental recovery. The dominant taxa assemblages, previously dominated by Ulvaceae, were replaced by species of higher ecological value, with an increase in the number and distribution of sensitive species, as well as the spread and cover of aquatic angiosperms. The rise in the Ecological Quality Ratio (EQR) determined by the MaQI confirms the key role of macrophyte monitoring in detecting environmental changes in TWSs. In fact, a simple check of the presence or absence of aquatic angiosperms and sensitive species is sufficient for an initial rapid assessment of the ecological status of these environments. Full article

Microwave sensors possess the capacity to effectively penetrate through clouds and fog and are widely used in obtaining soil moisture, atmospheric water vapor, and surface temperature measurements. Long time-series datasets play a pivotal role in climate change studies. Unfortunately, the lifespan of operational satellites often falls short of the needs of these extensive datasets. Hence, comparing and cross-calibrating sensors with similar configurations is paramount. The Microwave Radiation Imager (MWRI) onboard Fengyun-3D (FY-3D) is the latest generation of satellite-based microwave remote sensing instruments in China, and its data quality and application prospects have attracted widespread attention. To comprehensively assess the data quality of MWRI, a comparison of the orbital brightness temperature (TB) data between FY-3D/MWRI and Global Change Observation Mission 1st-Water (GCOM-W1)/Advanced Microwave Scanning Radiometer 2 (AMSR2) is conducted, and then a calibration model is established. The results indicate a strong correlation between the two sensors, with a correlation coefficient exceeding 0.9 across all channels. The mean bias ranges from −1.5 K to 0.15 K. Notably, the bias of vertical polarization is more pronounced than that of horizontal polarization. The TB distribution patterns and temporal evolutions are highly consistent for both sensors, particularly under snow and ice. The small intercepts and close-to-1 slopes obtained during calibration further demonstrate the minor data differences between the two sensors. However, the calibration process effectively reduces the existing errors, and the calibrated FY-3D/MWRI TB data are closer to GCOM-W1/AMSR2, with a mean bias approximately equal to 0 K and a correlation coefficient exceeding 0.99. The excellent consistency of the TB data between the two sensors provides a vital data basis for retrieving surface parameters and establishing long time-series datasets. Full article

The European Fish Index EFI+ is the only fish-based multimetric index for the assessment of the ecological status of running waters that is validated and thus applicable across most countries of the European Union. Metrics of the index rely on several attributes of the species present in the fish assemblage, irrespective of their native/alien status. The abundance of alien fish, together with other anthropogenic impacts, is one of the most important threats to the conservation of native fish and ecosystem health and is also an indicator of degraded stream conditions. Therefore, to improve the performance of the EFI+ in regions with high incidence of alien species, the EFI+ was adapted to include alien fish pressure as a new metric that reflects the number of alien species as well as the proportional abundance of alien individuals. The application of the adapted index (A-EFI+) is illustrated with data from several Iberian Mediterranean basins and showed similar or stronger correlations than the original EFI+ with anthropogenic pressure (land-use variables and alterations in hydrology and river morphology) and with other regional fish indices. EFI+ has been invaluable to intercalibrate fish indices across Europe, and A-EFI+ is similar but explicitly includes alien pressure, thus helping to provide a more comprehensive assessment of ecosystem health and to communicate it to society. Full article

The intercalibration of age readings represents a crucial step in the ageing procedure; the use of different sampling methods, structures, preparation techniques, and ageing criteria can significantly affect age and growth data. This study evaluated the precision and accuracy of ageing for the most important North Atlantic (NA) and Mediterranean (M) ray species, Raja clavata, Raja brachyura, Torpedo marmorata, and Dipturus oxyrinchus, through exchange exercises carried out by readers from different laboratories. In addition, growth parameters were estimated from the obtained data. A total of 663 individual batoids were analysed. R. clavata and R. brachyura samples were obtained from both the NA and the M, while vertebral centra of T. marmorata and D. oxyrinchus were only available for the M. High reading variability was observed for all four evaluated species in terms of CV, APE, and PA. D. oxyrinchus and T. marmorata showed relatively slow growth and the von Bertalanffy model with fixed t₀ and Gompertz’s model were, respectively, the most precise models for each of these species. In R. brachyura, females had a faster growth rate compared to combined sexes. The vbt0p proved the most precise model for describing growth in this species, and no statistical differences were found between the NO and the M. For R. clavata, the best-fitting model was the vbt0p for females and males in the NO and for females from the M, while the best-fitting model for males from the M and sexes combined for both areas was log.p. Distinct growth patterns were observed between the two study areas. Full article

Combining images from multiple Earth Observing (EO) satellites increases the temporal resolution of the data, overcoming the limitations imposed by low revisit time and cloud coverage. However, this requires an intercalibration process to ensure that there is no radiometric difference in top-of-atmosphere (TOA) observations or to quantify any offset in the respective instruments. In addition, combining vicarious calibration processes to the intercalibration of instruments can provide a useful mechanism to validate and compare data from multiple sensors. The Radiometric Calibration Network (RadCalNet) provides automated surface and top-of-atmosphere reflectance data from multiple participating ground sites that can be used for instrument vicarious calibration. We present a comparative analysis of the Landsat 8 and Landsat 9 Operational Land Imagers (OLI) sensors and validate the data by comparing them to measurements from RadCalNet sites as a quantitative intercalibration approach. RadCalNet serves as a common reference for instrument radiometric calibration, providing SI-traceable TOA reflectance with its associated absolute uncertainties. This paper discusses the method of combining data from multiple sites and calculating the weighted average by comparing the TOA reflectance of the instruments and their associated uncertainties. The presented process provides a SI-traceable intercalibration methodology and quantifies the offset and uncertainty in the Landsat 8 and 9 OLI instruments, demonstrating that the two instruments are in good agreement with each other and the data can be reliably cross-correlated and used by the scientific community. Full article

(1) The Environmental Trace Gases Monitoring Instrument-2(EMI-2) is a high-quality spaceborne imaging spectrometer that launched in September 2021. To evaluate its radiometric calibration performance in-flight, the UV2 and VIS1 bands of EMI-2 were cross-calibrated by the corresponding bands (band3 and band4) of TROPOMI over the pseudo-invariant calibration site Dome C. (2) After angle limitation and cloud filtering of the Earth radiance data measured by EMI-2 and TROPOMI over Dome C, the top of atmosphere (TOA) reflectance time series were calculated. The spectral adjustment factors (SAF) were derived from the solar spectrum measured by the sensor to minimize the uncertainties caused by the different spectral response functions (SRF) of sensors. In addition, a correction method based on the radiative transfer model (RTM) SCIATRAN was used to suppress unaccounted angular dependence of atmospheric scattering. The radiation performance of EMI-2 is evaluated using the TOA reflectance ratio of EMI-2 and TROPOMI, combining the SAF correction and RTM-based correction methods. (3) It was shown that the time series trending of the TOA reflectance ratio between EMI-2 measurements and TROPOMI demonstrate flat characteristics and strong correlation. The mean reflectance ratios range from 0.998 to 1.09. The standard deviation of the reflection ratio is less than 3%. For 328 nm, 335 nm, 340 nm, 460 nm, and 490 nm, the mean values are close to one, and the relative radiometric bias estimated through EMI-2 and TROPOMI intercalibration is less than 3%, and for other wavelengths, the biases are less than 6%, except for 416 nm, which behaves higher than 7%. The cross-calibration results show that the radiometric calibration of EMI-2 is within the relative accuracy requirement. Full article

The Advanced Baseline Imager (ABI) is the primary instrument onboard the NOAA Geostationary Operational Environmental Satellite-R Series (GOES-R) satellites, providing continuous weather imagery over the vast area in the Western Hemisphere. It is imperative to ensure consistent calibration accuracy within the instrument’s field of regard (FOR). This paper characterized the spatial uniformity in the east–west (EW) direction for the six ABI visible and near-infrared (VNIR) bands of the first two GOES-R satellites, GOES-16 (G16) and GOES-17 (G17), using a special collection of lunar chasing images during their post-launch testing and post-launch product testing (PLT/PLPT) periods. The EW response versus scan-angle (RVS) is examined with the normalized lunar irradiance ratios at varying scan angles combined from multiple lunar-chasing events. The impacts of straylight from the Earth were found in some of the B01–B03 lunar images. The straylight, including those scattered into the spacelook scenes near the polar regions and those leaked into space near the Moon, can cause RVS variation up to 1% for B01 and to a lesser magnitude for the other two bands. Straylight correction algorithms are applied for the accurate ABI lunar image irradiance calculation. After the corrections, the RVS variation is reduced to less than 0.3% for all the VNIR bands of both G16/17 in full-disk (FD) images. Results of this study also confirm that the Global Space-based Inter-Calibration System (GSICS) Implementation of the ROLO (GIRO) model has high relative accuracy for the ABI VNIR bands when the lunar images are collected within a relatively short time. The method described in this paper can be applied to validate the EW spatial uniformity for imagers on other geostationary satellites, including the recently launched GOES-18 and the future GOES-U satellites. Full article

The leaf area index (LAI) is a crucial indicator for quantifying forest productivity and community ecological processes. Satellite remote sensing can achieve large-scale LAI monitoring, but it needs to be calibrated and validated according to the in situ measurements on the ground. In this study, we attempted to use different indirect methods to measure LAI in a tropical secondary forest. These methods included the LAI-2200 plant canopy analyzer (LAI-2200), Digital Hemispherical Photography (DHP), Tracing Radiation and Architecture of Canopies (TRAC), and Terrestrial Laser Scanning (TLS) (using single-station and multi-station measurements, respectively). Additionally, we tried to correct the measured LAI by obtaining indicators of woody components and clumping effects. The results showed that the LAI of this forest was large, with estimated values of 5.27 ± 1.16, 3.69 ± 0.74, 5.86 ± 1.09, 4.93 ± 1.33, and 3.87 ± 0.89 for LAI-2200, DHP, TRAC, TLS multi-station, and TLS single-station, respectively. There was a significant correlation between the different methods. LAI-2200 was significantly correlated with all other methods (p < 0.01), with the strongest correlation with DHP (r = 0.684). TRAC was significantly correlated with TLS single-station (p < 0.01, r = 0.283). TLS multi-station was significantly correlated with TLS single-station (p < 0.05, r = 0.266). With the multi-station measurement method, TLS could maximize the compensation for measurement bias due to the shadowing effects. In general, the clumping index of this forest was 0.94 ± 0.05, the woody-to-total area ratio was 3.23 ± 2.22%, and the total correction coefficient was 1.03 ± 0.07. After correction, the LAI estimates for all methods were slightly higher than before, but there was no significant difference among them. Based on the performance assessment of existing ground-based methods, we hope to enhance the inter-calibration between methods to improve their estimation accuracy under complex forest conditions and advance the validation of remote sensing inversion of the LAI. Moreover, this study also provided a practical reference to promote the application of LiDAR technology in tropical forests. Full article

The densest network for measuring air pollutant concentrations in Colombia is in Medellin, where most sensors are located in the heavily polluted lower parts of the valley. Measuring stations in the higher elevations on the mountains surrounding the valley are not available, which limits our understanding of the valley’s pollutant dynamics and hinders the effectiveness of data assimilation studies using chemical transport models such as LOTOS-EUROS. To address this gap in measurements, we have designed a new network of low-cost sensors to be installed at altitudes above 2000 m.a.s.l. The network consists of custom-built, solar-powered, and remotely connected sensors. Locations were strategically selected using the LOTOS-EUROS model driven by diverse meteorology-simulated fields to explore the effects of the valley wind representation on the transport of pollutants. The sensors transmit collected data to internet gateways for posterior analysis. Various tests to verify the critical characteristics of the equipment, such as long-range transmission modeling and experiments with an R score of 0.96 for the best propagation model, energy power system autonomy, and sensor calibration procedures, besides case exposure to dust and water experiments, to ensure IP certifications. An inter-calibration procedure was performed to characterize the sensors against reference sensors and describe the observation error to provide acceptable ranges for the data assimilation algorithm (<10% nominal). The design, installation, testing, and implementation of this air quality network, oriented towards data assimilation over the Aburrá Valley, constitute an initial experience for the simulation capabilities toward the system’s operative capabilities. Our solution approach adds value by removing the disadvantages of low-cost devices and offers a viable solution from a developing country’s perspective, employing hardware explicitly designed for the situation. Full article

The reflected GNSS signals at the L-band is significantly advantageous in soil moisture monitoring as they are sensitive to the dielectric properties determined by the volumetric water content of topsoil, and they can penetrate vegetation, except in very dense forests. The Global Navigation satellite system Occultation Sounder (GNOS-II) with a reflectometry technique onboard the Fengyun-3E (FY-3E) satellite, launched on 5 July 2021, is the first mission that can receive reflected Global Navigation Satellite System (GNSS) signals from GPS, BeiDou and Galileo systems. This paper presents the soil moisture retrieval results from the FY-3E GNOS-II mission using 16 months of data. In this study, the reflectivity observations from different GNSS systems were firstly intercalibrated with some differences analyzed. Observations were also corrected by considering vegetation attenuation for 16 different land cover classifications. Finally, an empirical model was constructed for volumetric soil moisture (VSM) estimation, where the reflectivity of GNOS-II was linearly related to the SMAP reference soil moisture for each 36 km ease grid. The overall root-mean-square error of the retrieved soil moisture is 0.049 compared with the SMAP product, and 0.054 compared with the in situ data. The results of the three GNSS systems show similar levels of accuracy. This paper, for the first time, demonstrates the feasibility of global soil moisture retrieval using multiple GNSS signals. Full article

The recent advances in unmanned aerial vehicle (UAV)-based remote sensing systems have broadened the remote sensing applications for agriculture. Despite the great possibilities of using UAVs to monitor agricultural fields, specific problems related to missing parts in UAV orthomosaics due to drone flight restrictions are common in agricultural monitoring, especially in large areas. In this study, we propose a methodological framework to impute missing parts of UAV orthomosaics using PlanetScope (PS) and Sentinel-2 (S2) data and the random forest (RF) algorithm of an integrated crop–livestock system (ICLS) covered by grass at the time. We validated the proposed framework by simulating and imputing artificial missing parts in a UAV orthomosaic and then comparing the original data with the model predictions. Spectral bands and the normalized difference vegetation index (NDVI) derived from PS, as well as S2 images (separately and combined), were used as predictor variables of the UAV spectral bands and NDVI in developing the RF-based imputation models. The proposed framework produces highly accurate results (RMSE = 6.77–17.33%) with a computationally efficient and robust machine-learning algorithm that leverages the wealth of empirical information present in optical satellite imagery (PS and S2) to impute up to 50% of missing parts in a UAV orthomosaic. Full article

Satellite altimetry has been providing a continuous record of ocean measurements with numerous applications across the entire range of ocean sciences. A reference orbit has been used since 1992 with TOPEX/Poseidon, which was repeated in the Jason missions, and in the newly launched Sentinel-6 Michael Freilich (in November 2020) to continually monitor the trends of sea level rise and other properties of the sea surface. These multidecadal missions have evolved alongside major technological advances, whose measurements are unified into a single data record owing to continuous intercalibration and validation efforts. However, the new Sentinel-6 provides synthetic aperture radar (SAR) processing, which improves the along-track resolution of conventional altimeters from a few kilometres (e.g., for Jason-3) to about 300 m. This means a major leap in sampling towards higher frequencies of the ocean spectrum, which inevitably means reconciling the assumption of a uniform Brown surface between the footprints of the larger kilometre-scale conventional altimetry and those of the finer-scale SAR altimetry. To explore this issue, this study uses the vantage point of the Sentinel-6/Jason-3 tandem phase to compare simultaneous sea surface signatures of large-scale Internal Solitary Waves (ISWs) between SAR and conventional altimetry. These waves can modulate the sea surface into arrayed sections of increased and decreased roughness with horizontal scales up to 10 km, which inflict sharp transitions between increased and decreased backscatter in the radar altimeters. It is found that Sentinel-6 can provide more detailed structures of ISWs in standard level-2 products, when compared with those from the conventional Jason-3 (similarly to previous results reported from the SAR altimeter from Sentinel-3). However, a new and striking feature is found when comparing the radar backscatter between Sentinel-6 and Jason-3, which are in opposite phases in the ISWs. These intriguing results are discussed in light of the intrinsically different acquisition geometries of SAR and conventional altimeters as well as possible implications thereof. Full article

The main objective of this research was to evaluate the surface water system of Lake Vegoritida (Region of Central Macedonia, Greece). The Driver–Pressure–State–Impact–Response (DPSIR) methodological approach was used. The analysis includes data from three (3) stations monitoring point source pollution and recording the most critical water quality measurement parameters in a time series data analysis from 1983 to 1997. The data will contribute to the analysis and was used to investigate, identify, and evaluate possible sources of chemical and ecological changes recorded in the lake. The artificial neural network (ANN) is a valuable tool for making predictions based on the water quality data set. The findings highlighted the increased concentration of nutrients that contribute to the presence of eutrophic conditions, while their seasonal variability is mainly due to factors, such as water level fluctuations and biological processes in the lake. The above, combined with the critical biotic indicators and factors alongside the reduction in biodiversity, indicated that only the most resistant species survive, confirming the previous finding. In Greece, systematic monitoring and reporting programs have recently been implemented, such as the ECOFRAME scheme and the guidelines proposed by the “Intercalibration Group for Mediterranean Lakes”. The water quality status could be classified as “High”, “High to Good”, and “High to Poor”, respectively, while the overall ecological assessment tends to change to poor conditions. The actions required at an early stage concern the planning of programs and actions that contribute to the sustainable management of land uses and the reduction in point sources of pollution, as well as the reduction of the applied quantities of agrochemicals on the cultivated land in the study area. Full article