POBI interpolation algorithm for CYGNSS near real time flood detection research: A case study of extreme precipitation events in Henan,China in 2021

In late 2016, NASA launched the first constellation of the global navigation satellite system reflectometry (GNSS-R) small satellites called the Cyclone Global Navigation Satellite System (CYGNSS). The stable data quality and continuous free availability of CYGNSS scientific data provided a new method for flood monitoring. However, owing to the pseudorandom distribution of CYGNSS data, researchers must always choose between high temporal resolution and high spatial resolution during the performance of flood monitoring based on CYGNSS data. For floods caused by extreme precipitation with sudden and short durations, the current flood mapping based on CYGNSS data cannot be updated in near real time. However, the near real time update of the flood distribution range is meaningful for postdisaster emergency response and rapid rescue. This study aimed to address this problem using a newly proposed spatial interpolation method based on previously observed behaviour (POBI). First, a method for calculating the surface reflectivity of the CYGNSS was introduced, followed by the principle of the POBI spatial interpolation method. The applicability of the POBI method in Henan Province, China, was then analysed, and by using the flood in Henan Province, China, in July 2021 as an example, the feasibility of CYGNSS near real time flood mapping based on the POBI method was evaluated. Based on the results, near real time and 3 km flood distribution monitoring results can be obtained using the proposed new method. The results were evaluated using MODIS (Moderate Resolution Imaging Spectroradiometer) images and compared with the observations of SMAP (Soil Moisture Active Passive) and GPM (Global Precipitation Measurement) in the same period. The results show that the flooded areas obtained by CYGNSS correspond to the inundated areas in MODIS images and are also in high agreement with the SMAP. In addition, CYGNSS allows for finer mapping and quantification of inundation areas and flood duration. Moreover, we also discussed the potential of CYGNSS to detect floods in shorter periods of time (a few hours) and did a preliminary evaluation using precipitation data from meteorological stations. The results are also highly consistent.     

Spatial and temporal monitoring of drought conditions using the satellite rainfall estimates and remote sensing optical and thermal measurements

Drought is an important natural disaster that causes devastating impacts on the ecosystem, livestock, environment, and society. So far, various remote-sensing methods have been developed to estimate drought conditions, each of which has advantages and restrictions. This study aims to monitor the real-time drought indices at the field scales via the integration of various earth observations. Our proposed method consists of two steps. In the first step, the relationships between long-term standardized precipitation indices (SPI) derived from PERSIANN-CDR rainfall data and two drought-dependent parameters derived from MODIS products, including normalized NDVI and soil-air temperature gradient, are obtained at the spatial resolution of PERSIANN-CDR grid (approximately 25 km). As the next step, the corresponding relationships are applied to estimate the drought index maps at the spatial resolution of MODIS products (1 km). Numerous analyses are carried out to evaluate the proposed method. The results revealed that, from various drought indices, including SPIs of different timescales (1, 3, 6, and 12-months), SPI-3 and SPI-6 are more appropriate to the proposed method in terms of correlation with temperature and vegetation parameters. The findings also demonstrate the competency of the proposed method in estimating SPI indices with average RMSE 0.67 and the average correlation coefficient of 0.74.     

Validation of non-linear split window algorithm for land surface temperature estimation using Sentinel-3 satellite imagery: Case study; Tehran Province,Iran

In recent years, land surface temperature (LST) has become critical in environmental studies and earth science. Remote sensing technology enables spatiotemporal monitoring of this parameter on large scales. This parameter can be estimated by satellite images with at least one thermal band. Sentinel-3 SLSTR data provide LST products with a spatial resolution of 1 km. In this research, direct and indirect validation procedures were employed to evaluate the Sentinel-3 SLSTR LST products over the study area in different seasons from 2018 to 2019. The validation method was based on the absolute (direct) evaluation of this product with field data and comparison (indirect) evaluation with the MODIS LST product and the estimated LST using the non-linear split-window (NSW) algorithm. Also, two emissivity estimation methods, (1) NDVI thresholding method (NDVI-THM) and (2) classification-based emissivity method (CBEM), were used to estimate the LST using the NSW method according to the two thermal bands of Sentinel-3 images. Then, the accuracy of these methods in estimating LST was evaluated using field data and temporal changes of vegetation, which the NDVI-THM method generated better results. For indirect evaluation between the Sentinel-3 LST product, MODIS LST product, and LST estimated using NSW, four filters based on spatial and temporal separates between pairs of pixels and pixel quality were used to ensure the accuracy and consistency of the compared pairs of a pixel. In general, the accuracy results of the LST products of MODIS and Sentinel-3, and LST estimated using NSW showed a similar trend for LST changes during the seasons. With respect to the two absolute and comparative validations for the Sentinel-3 LST products, summer with the highest values of bias (?1.24 K), standard deviation (StDv = 2.66 K), and RMSE (2.43 K), and winter with the lowest ones (bias of 0.14 K, StDv of 1.13 K, and RMSE of 1.12 K) provided the worst and best results for the seasons in the period of 2018–2019, respectively. According to both absolute and comparative evaluation results, the Sentinel-3 SLSTR LST products provided reliable results for all seasons on a large temporal and spatial scale over our studied area.     

Evaluation of MODIS GPP over a complex ecosystem in East Asia: A case study at Gwangneung flux tower in Korea

Moderate Resolution Imaging Radiometer (MODIS) gross primary productivity (GPP) has been used widely to study the global carbon cycle associated with terrestrial ecosystems. The retrieval of the current MODIS productivity with a 1 × 1 km² resolution has limitations when presenting subgrid scale processes in terrestrial ecosystems, specifically when forests are located in mountainous areas, and shows heterogeneity in vegetation type due to intensive land use. Here, we evaluate MODIS GPP (MOD17) at Gwangneung deciduous forest KoFlux tower (deciduous forest; GDK) for 2006–2010 in Korea, where the forests comprise heterogeneous vegetation cover over complex terrain. The monthly MODIS GPP data overestimated the GDK measurements in a range of +15% to +34% and was fairly well correlated (R = 0.88) with the monthly variability at GDK during the growing season. In addition, the MODIS data partly represented the sharp GPP reduction during the Asian summer monsoon (June–September) when intensive precipitation considerably reduces solar radiation and disturbs the forest ecosystem. To examine the influence of subgrid scale heterogeneity on GPP estimates over the MODIS scale, the individual vegetation type and its area within a corresponding MODIS pixel were identified using a national forest type map (∼71-m spatial resolution), and the annual GPP in the same area as the MODIS pixel was estimated. This resulted in a slight reduction in the positive MODIS bias by ∼10%, with a high degree of uncertainty in the estimation. The MODIS discrepancy for GDK suggests further investigation is necessary to determine the MODIS errors associated with the site-specific aerodynamic and hydrological characteristics that are closely related to the mountainous topography. The accuracy of meteorological variables and the impact of the very cloudy conditions in East Asia also need to be assessed.     

Monitoring spatio-temporal variations in aerosols and aerosol–cloud interactions over Pakistan using MODIS data

Clouds are important elements in climatic processes and interactions between aerosols and clouds are therefore a hot topic for scientific research. Aerosols show both spatial and temporal variations, which can lead to variations in the microphysics of clouds. In this research, we have examined the spatial and temporal variations in aerosol particles over Pakistan and the impact of these variations on various optical properties of clouds, using Moderate Resolution Imaging Spectroradiometer (MODIS) data from the Terra satellite. We used the Hybrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT) model for trajectory analysis to reveal the origins of air masses, with the aim of understanding these spatial and temporal variabilities in aerosol concentrations. We also documented seasonal variations in patterns of aerosol optical depth (AOD) over Pakistan, for which the highest values occur during the monsoon season (June–August). We then analyzed the relationships between AOD and four other cloud parameters, namely water vapour (WV), cloud fraction (CF), cloud top temperature (CTT) and cloud top pressure (CTP). Regional correlation maps and time series plots for aerosol (AOD) and cloud parameters were produced to provide a better understanding of aerosol–cloud interaction. The analyses showed strong positive correlations between AOD and WV for all of the eight cities investigated. The correlation between AOD and CF was positive for those cities where the air masses were predominantly humid, but negative for those cities where the air masses were relatively dry and carried a low aerosol abundance. These correlations were clearly dependent on the meteorological conditions for all of the eight cities investigated. Because of the observed AOD–CF relationship, the co-variation of AOD with CTP and CTT may be attributable to large-scale meteorological variations: AOD showed a positive correlation with CTP and CTT in northern regions of Pakistan and a negative correlation in southern regions.     

A comparison of different regression models for downscaling Landsat and MODIS land surface temperature images over heterogeneous landscape

Remotely sensed high spatial resolution thermal images are required for various applications in natural resource management. At present, availability of high spatial resolution (<200 m) thermal images are limited. The temporal resolution of such images is also low. Whereas, coarser spatial resolution (∼1000 m) thermal images with high revisiting capability (∼1 day) are freely available. To bridge this gap, present study attempts to downscale coarser spatial resolution thermal image to finer spatial resolution using relationships between land surface temperature (LST) and vegetation indices over a heterogeneous landscape of India. Five regression based models namely (i) Disaggregation of Radiometric Temperature (DisTrad), (ii) Temperature Sharpening (TsHARP), (iii) TsHARP with local variant, (iv) Least median square regression downscaling (LMS_DS) and (v) Pace regression downscaling (PR_DS) are applied to downscale LST of Landsat Thematic Mapper (TM) and Terra MODIS (Moderate Resolution Imaging Spectroradiometer) images. All the five models are first evaluated on Landsat image aggregated to 960 m resolution and downscaled to 480 m and 240 m resolution. The downscale accuracy is achieved using LMS_DS and PR_DS models at 240 m resolution at 0.61 °C and 0.75 °C respectively. MODIS data downscaled from 1000 m to 250 m spatial resolution results root mean square error (RMSE) of 1.43 °C and 1.62 °C for LMS_DS and PR_DS models, respectively. The LMS_DS model is less sensitive to outliers in heterogeneous landscape and provides higher accuracy when compared to other models. Downscaling model is found to be suitable for agricultural and vegetated landscapes up to a spatial resolution of 250 m but not applicable to water bodies, dry river bed sand sandy open areas.     

Spatial and temporal scales of the Brazil–Malvinas Current confluence documented by simultaneous MODIS Aqua 1.1-km resolution SST and color images

The moderate resolution imaging spectroradiometer (MODIS) on board the Aqua satellite measures visible and infrared radiation in 36 wavebands, providing simultaneous images of sea-surface temperature (SST) and chlorophyll-a (chl-a) concentration in the upper meters of the sea. For the first time, truly synoptic SST and chl-a- concentration images are available. These images are daily and of 1.1-km resolution.The strong contrasts in sea-surface temperature and surface chlorophyll-a concentration over the southwest Atlantic make satellite infrared and color images particularly appropriate tools for studying the Brazil–Malvinas (B/M) Current confluence. We examine two years (July, 2002–June, 2004) of Aqua/MODIS infrared and color images to document the precise structure of the B/M confluence simultaneously in SST and chl-a.We first compared MODIS weekly data with simultaneous independent satellite data. Spatial and temporal distributions are similar for both SST and color. Differences between MODIS and SeaWiFS (sea-viewing wide field-of-view sensor) are large in pigment-rich regions along the coast and shelf. Here, we focused on the offshore region where differences are small.For each season, exceptionally cloud-free 1.1-km resolution MODIS images showed two thermal fronts, one corresponding to the Brazil Current’s southernmost limit, the other, to the Malvinas Current’s northernmost limit. These two fronts remained quite close to each other (within 50 km) and were separated by water with an SST and chl-a concentration typical of the continental shelf waters. In spring, the water rich in chl-a from the platform is squeezed between the two currents and entrained away from the coast in between the two thermal fronts. In the frontal region, SST gradient maxima trace the contour of the chl-a-rich water.Enlargements of the frontal region and of the turbulent region downstream of the frontal collision are presented and analyzed. MODIS documents in an unprecedented way the SST and chl-a filaments as they are distorted and mixed by meso- and sub-mesoscale structures in the strain-dominated region of the B/M confluence. It is suggested that a substantial part of the chl-a local maximum in the Malvinas return flow is of continental-shelf origin.     

High temporal resolution global PWV dataset of 2005–2016 by using a neural network approach to determine the mean temperature of the atmosphere

Atmospheric water vapour plays an important role in phenomena related to the global hydrologic cycle and climate change. However, the rapid temporal–spatial variation in global tropospheric water vapour has not been well investigated due to a lack of long-term, high-temporal-resolution precipitable water vapour (PWV). Accordingly, this study generates an hourly PWV dataset for 272 ground-based International Global Navigation Satellite System (GNSS) Service (IGS) stations over the period of 2005–2016 using the zenith troposphere delay (ZTD) derived from global-scale GNSS observation. The root mean square (RMS) of the hourly ZTD obtained from the IGS tropospheric product is approximately 4 mm. A fifth-generation reanalysis dataset of the European Centre for Medium-range Weather Forecasting (ECMWF ERA5) is used to obtain hourly surface temperature (T) and pressure (P), which are first validated with GNSS synoptic station data and radiosonde data, respectively. Then, T and P are used to calculate the water vapour-weighted atmospheric mean temperature (Tm) and zenith hydrostatic delay (ZHD), respectively. T and P at the GNSS stations are obtained via an interpolation in the horizontal and vertical directions using the grid-based ERA5 reanalysis dataset. Here, Tm is calculated using a neural network model, whereas ZHD is obtained using an empirical Saastamoinen model. The RMS values of T and P at the collocated 693 radiosonde stations are 1.6 K and 3.1 hPa, respectively. Therefore, the theoretical error of PWV caused by the errors in ZTD, T and P is on the order of approximately 2.1 mm. A practical comparison experiment is performed using 97 collocated radiosonde stations and 23 GNSS stations equipped with meteorological sensors. The RMS and bias of the hourly PWV dataset are 2.87/?0.16 and 2.45/0.55 mm, respectively, when compared with radiosonde and GNSS stations equipped with meteorological sensors. Additionally, preliminary analysis of the hourly PWV dataset during the EI Niño event of 2014–2016 further indicates the capability of monitoring the daily changes in atmospheric water vapour. This finding is interesting and significant for further climate research.     

Estimation of vertically integrated water vapor in Hungary using MODIS imagery

Information about the amount and spatial structure of atmospheric water vapor is essential in understanding meteorology and the Earth environment. Space-borne remote sensing offers a relatively inexpensive method to estimate atmospheric water vapor in the form of integrated water vapor (IWV). The research activity reported in the present paper is based on the data acquired by the HRPT/MODIS (High Resolution Picture Transmission, MODerate resolution Imaging Spectroradiometer) receiving station established in Budapest (Hungary) by the Space Research Group of the Eötvös Loránd University. Integrated water vapor is estimated by the remotely sensed data of the MODIS instrument with different methods and also by the operational numerical weather prediction model of the European Centre for Medium-Range Weather Forecasts (ECMWF). Radiosonde data are used to evaluate the accuracy of the different IWV fields though it has been pointed out that the in situ data also suffers from uncertainties. It was found that both the MODIS and the ECMWF based fields are of good accuracy. The satellite data represent finer scale spatial structures while the ECMWF data have a relatively poor spatial resolution. The high quality IWV fields have proved to be useful for radiative transfer studies such as the atmospheric correction of other satellite data from times different than the overpass times of satellites Terra/Aqua and the forecast times of the model data. For this purpose the temporal variability of IWV is scrutinized both using ECMWF and MODIS data. Taking advantage of Terra and Aqua overpasses, the mean rate of change of IWV estimated by the near infrared method was found to be 0.47 ± 0.45 kg m⁻² h⁻¹, while it was 0.13 ± 0.65 kg m⁻² h⁻¹ based on the infrared method. The numerical weather prediction model’s analysis data estimated −0.01 ± 0.13 kg m⁻² h⁻¹ for the mean growth rate, while using forecast data it was 0.24 ± 0.18 kg m⁻² h⁻¹. MODIS data should be used when available for the estimation of the IWV in other studies. If no satellite data are available, or available data are only from one overpass, ECMWF based IWV can be used. In this case the analysis fields (or the satellite field) should be used for temporal extrapolation but the rate of change should be calculated from the forecast data due to its higher temporal resolution.     

Long-term time-varying characteristics of UPD products generated by a global and regional network and their interoperable application in PPP

The quality and availability of Uncalibrated Phase Delay (UPD) solutions are crucial to the Precise Point Positioning (PPP) service, and the long-term temporal variability and its contributing factors should be better understood. In this paper, we comprehensively investigate the long-term time-varying characteristics of each UPD product respectively generated by a global and regional network and their interoperable application in PPP-AR (ambiguity resolution), the sampling of the WL and NL UPDs are daily and 30 s, respectively. Firstly, in terms of our 30 day Wide-Lane (WL) UPD products of 31 satellites, the Standard Deviation (STD) of each satellite WL UPDs ranges from 0.04 to 0.06 cycles, indicating that the long-term prediction accuracy of satellite WL UPD is sufficient for fixing Wide-Lane ambiguities. Secondly, when a satellite in eclipsing the discontinulity may corrupt the determination of Narrow-Lane (NL) UPD in form of offset, as a result of lacking or poor satellite attitude dynamic modeling. When the influence of discontinuity is removed, the STD of our estimated satellite NL UPDs is less than 0.05 cycles. Thirdly, the STD of our estimated receiver WL UPDs is mainly below 0.2 cycles, which implies that its stability is one order poorer that of the satellite. In addition, if they are used for stations in and around the network covered region, the stability of the UPD products from the CMONOC (Crustal Movement Observation Network of China) is better than that from a global network, benefit from the fact that all the CMONOC stations are equipped with the same receiver type. Finally, the PPP-AR results show that a rate of 82.9% for stations with a WL-ambiguity-fixed rate of over 90% while 69.5% for stations with an NL-ambiguity-fixed rate of over 80% can be achieved when using UPD from the global network, which is worse than that of using UPD from the CMONOC (85.7% for stations with a WL-ambiguity-fixed rate of over 90% while 75% for stations with an NL-ambiguity-fixed rate of over 80%). The results of the experiment on the UPD interoperable application in PPP show that the global network UPD products can provide a fast AR at any single station, and the convergence time is well below 25 min. Particularly, when the location of a station is in and around the regional network, our results show that the PPP results obtained using regional UPDs enable the consistent use of global UPDs. When the location of a station is far away from the regional network, using the regional UPDs can not achieve PPP-AR. Finally, the WL UPDs of the previous day is used for forecasting to estimate the NL UPDs, the stability analysis results of NL UPDs solution and positioning results are demonstrate the validity of forecasted UPD products.     

Photon counting detector for space debris laser tracking and lunar laser ranging

We are reporting on a design, construction and performance of solid state photon counting detector package which has been designed for laser tracking of space debris. The detector has been optimized for top photon detection efficiency and detection delay stability. The active area of the commercially available avalanche photodiode manufactured on Si (SAP500 supplied by Laser Components, Inc.) is circular with a diameter of 500 μm. The newly designed control circuit enables to operate the detection sensor at a broad range of biases 5–50 V above its breakdown voltage of 125 V. This permits to select a right trade-off between photon detection efficiency, timing resolution and dark count rate. The photon detection efficiency exceeds 70% at the wavelength of 532 nm. This is the highest photon detection efficiency ever reported for such a device. The timing properties of the detector have been investigated in detail. The timing resolution is better than 80 ps r.m.s, the detection delay is stable within units of picoseconds over several hours of operation. The detection delay stability in a sense of time deviation of 800 fs has been achieved. The temperature change of the detection delay is 0.5 ps/K. The detector has been tested as an echo signal detector in laser tracking of space debris at the satellite laser station in Graz, Austria. Its application in lunar laser ranging is under consideration by several laser stations.     

Retrieval and validation of aerosol optical depth using GF-1 WFV cameras data

Conventional AOD (Aerosol Optical Depth) retrieval is restricted to the global and regional scale due to the limited spatial resolution of satellites. This does not allow for aerosol monitoring at the city level. The Chinese GF-1 Wide Field of View (WFV) sensors have sufficiently fine resolution as a data source for AOD retrieval with fine spatial resolution and a 4-day revisit time. In this study, principles similar to those in the Deep Blue (DB) and Dark Target (DT) algorithms were used to retrieve AOD at 100 m spatial resolution from GF-1 WFV images supported by Moderate Resolution Imaging Spectraradiometer (MODIS) surface reflectance (SR) products (MOD09A1). The derived GF-1 WFV AOD were compared with a combination of MOD04_3K DT AOD and MOD04_L2 DB AOD (MODIS AOD) to find that they yield reasonable Spearman correlations (R_S > 0.82) over Taiwan and Beijing. The derived GF-1 WFV AOD were also validated against Aerosol Robotic Network (AERONET) AOD; the Spearman correlation values were R_S = 0.911 in Beijing and R_S = 0.858 in Taiwan.     

An approach to multi-temporal MODIS image analysis using image classification and segmentation

This paper discusses an approach for river mapping and flood evaluation based on multi-temporal time series analysis of satellite images utilizing pixel spectral information for image classification and region-based segmentation for extracting water-covered regions. Analysis of MODIS satellite images is applied in three stages: before flood, during flood and after flood. Water regions are extracted from the MODIS images using image classification (based on spectral information) and image segmentation (based on spatial information). Multi-temporal MODIS images from “normal” (non-flood) and flood time-periods are processed in two steps. In the first step, image classifiers such as Support Vector Machines (SVM) and Artificial Neural Networks (ANN) separate the image pixels into water and non-water groups based on their spectral features. The classified image is then segmented using spatial features of the water pixels to remove the misclassified water. From the results obtained, we evaluate the performance of the method and conclude that the use of image classification (SVM and ANN) and region-based image segmentation is an accurate and reliable approach for the extraction of water-covered regions.     

Detection of aerosol pollution sources during sandstorms in Northwestern China using remote sensed and model simulated data

The present paper has used a comprehensive approach to study atmosphere pollution sources including the study of vertical distribution characteristics, the epicenters of occurrence and transport of atmospheric aerosol in North-West China under intensive dust storm registered in all cities of the region in April 2014. To achieve this goal, the remote sensing data using Moderate Resolution Imaging Spectroradiometer satellite (MODIS) as well as model-simulated data, were used, which facilitate tracking the sources, routes, and spatial extent of dust storms. The results of the study have shown strong territory pollution with aerosol during sandstorm. According to ground-based air quality monitoring stations data, concentrations of PM₁₀ and PM_2.5 exceeded 400?μg/m³ and 150?μg/m³, respectively, the ratio PM_2.5/PM₁₀ being within the range of 0.123–0.661. According to MODIS/Terra Collection 6 Level-2 aerosol products data and the Deep Blue algorithm data, the aerosol optical depth (AOD) at 550?nm in the pollution epicenter was within 0.75–1. The vertical distribution of aerosols indicates that the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) 532?nm total attenuates backscatter coefficient ranges from 0.01 to 0.0001?km^?1?×?sr^?1 with the distribution of the main types of aerosols in the troposphere of the region within 0–12.5?km, where the most severe aerosol contamination is observed in the lower troposphere (at 3–6?km). According to satellite sounding and model-simulated data, the sources of pollution are the deserted regions of Northern and Northwestern China.     

Initial accuracy and reliability of current BDS-3 precise positioning,velocity estimation,and time transfer (PVT)

The primary system of Chinese global BeiDou satellite system (BDS-3) was completed to provide global services on December 27, 2018; this was a key milestone in the development process for BDS in terms of its provision of global services. Therefore, this study analyzed the current performance of BDS-3, including its precise positioning, velocity estimation, and time transfer (PVT). The datasets were derived from international GNSS monitoring and assessment system (iGMAS) tracking networks and the two international time laboratories in collaboration with the International Bureau of Weights and Measures (BIPM). With respect to the positioning, the focus is on the real-time kinematic (RTK) positioning and precise point positioning (PPP) modes with static and kinematic scenarios. The results show that the mean available satellite number is 4.8 for current BDS-3 system at short baseline XIA1–XIA3. The RTK accuracy for three components is generally within cm level; the 3D mean accuracy is 8.9 mm for BDS-3 solutions. For the PPP scenarios, the convergence time is about 4 h for TP01 and BRCH stations in two scenarios. After the convergence, the horizontal positioning accuracy is better than cm level and the vertical accuracy nearly reaches the 1 dm level. With respect to kinematic scenarios, the accuracy stays at the cm level for horizontal components and dm level for the vertical component at two stations. In terms of velocity estimation, the horizontal accuracy stays at a sub-mm level, and the vertical accuracy is better than 2 mm/s in the BDS-3 scenario, even in the Arctic. In terms of time and frequency transfer, the noise level of BDS-3 time links can reach 0.096 ns for long-distances link NT01–TP02 and 0.016 ns for short-distance links TP01–TP02. Frequency stability reaches 5E–14 accuracy when the averaging time is within 10,000 s for NT01–TP02 and 1E–15 for TP01–TP02.     

Development and testing of a statistical texture model for land cover classification of the Black Sea region with MODIS imagery

A statistical model is proposed for analysis of the texture of land cover types for global and regional land cover classification by using texture features extracted by multiresolution image analysis techniques. It consists of four novel indices representing second-order texture, which are calculated after wavelet decomposition of an image and after texture extraction by a new approach that makes use of a four-pixel texture unit. The model was applied to four satellite images of the Black Sea region, obtained by Terra/MODIS and Aqua/MODIS at different spatial resolution. In single texture classification experiments, we used 15 subimages (50 × 50 pixels) of the selected classes of land covers that are present in the satellite images studied. These subimages were subjected to one-level and two-level decompositions by using orthonormal spline and Gabor-like spline wavelets. The texture indices were calculated and used as feature vectors in the supervised classification system with neural networks. The testing of the model was based on the use of two kinds of widely accepted statistical texture quantities: five texture features determined by the co-occurrence matrix (angular second moment, contrast, correlation, inverse difference moment, entropy), and four statistical texture features determined after the wavelet transformation (mean, standard deviation, energy, entropy). The supervised neural network classification was performed and the discrimination ability of the proposed texture indices was found comparable with that for the sets of five GLCM texture features and four wavelet-based texture features. The results obtained from the neural network classifier showed that the proposed texture model yielded an accuracy of 92.86% on average after orthonormal wavelet decomposition and 100% after Gabor-like wavelet decomposition for texture classification of the examined land cover types on satellite images.     

Simulation of the land surface temperature from moon-based Earth observations

The land surface temperature (LST) is a key parameter for the Earth’s energy balance. As a natural satellite of the Earth, the orbital of the moon differs from that of current Earth observation satellites. It is a new way to measure the land surface temperature from the moon and has many advantages compared with artificial satellites. In this paper, we present a new method for simulating the LST measured by moon-based Earth observations. Firstly, a modified land-surface diurnal temperature cycle (DTC) method is applied to obtain the global LST at the same coordinated universal time (UTC) using the Moderate Resolution Imaging Spectroradiometer (MODIS) LST products. The lunar elevation angles calculated using the ephemeris data (DE405) from the Jet Propulsion Laboratory (JPL) were then applied to simulate the Earth coverage observed from the moon. At the same time, the modified DTC model was validated using in situ data, MODIS LST products, and the FengYun-2F (FY-2F) LST, respectively. The results show that the fitting accuracy (root-mean-square error, RMSE) of the modified DTC model is not greater than 0.72?°C for eight in situ stations with different land cover types, and the maximum fitting RMSE of the modified model is smaller than that of current DTC models. By the comparison of the simulated LST with MODIS and FY-2F LST products, the errors of the results were feasible and accredited, and the simulated global LST has a reasonable spatiotemporal distribution and change trend. The simulated LST data can therefore be used as base datasets to simulate the thermal infrared imagery from moon-based Earth observations in future research.     

Effect of the troposphere on surface neutron counter measurements

Surface neutron counter data are often used as a proxy for atmospheric ionisation from cosmic rays in studies of extraterrestrial effects on climate. Neutron counter instrumentation was developed in the 1950s and relationships between neutron counts, ionisation and meteorological conditions were investigated thoroughly using the techniques available at the time; the analysis can now be extended using modern data. Whilst surface neutron counts are shown to be a good proxy for ionisation rate, the usual meteorological correction applied to surface neutron measurements, using surface atmospheric pressure, does not completely compensate for tropospheric effects on neutron data. Residual correlations remain between neutron counts, atmospheric pressure and geopotential height, obtained from meteorological reanalysis data. These correlations may be caused by variations in the height and temperature of the atmospheric layer at ∼100 hPa. This is where the primary cosmic rays interact with atmospheric air, producing a cascade of secondary ionising particles.     

Development and performance evaluation of tropospheric scintillation model on Ku-band satellite link over Akure,Nigeria

Existing amplitude scintillation prediction models often perform less satisfactorily when deployed outside the regions where they were formulated. This necessitates the need to evaluate the performance of scintillation models developed in one region using data data from other regions while documenting their relative errors. Due to its variation with elevation angle, frequency, other link parameters and meteorological factors, we employed three years (January 2016 to December 2018) of concurrently measured satellite radio beacons and tropospheric weather parameters to develop a location-based amplitude scintillation prediction model over the Earth-space path of Akure (7.17^oN, 5.18^oE), South-western Nigeria. The satellite beacon measurement used Tektronix Y400 NetTek Analyzer at 1 s integration time while meteorological parameters, namely; temperature, pressure and relative humidity were measured using Davis Vantage Vue weather station at 1 min integration time. Comparative study of the model’s performance with nine (9) existing scintillation prediction models indicates that the best and worst performing models, in terms of root mean square error (RMSE), are the Statistical Temperature and Refractivity (STN) and direct physical and statistical prediction (DPSP) models with values 11.48 and 51.03 respectively. Also, worst month analysis indicates that April, with respective enhancement and fade values of 0.88 and 0.90 dB for 0.01% exceedance, is the overall worst calendar month for amplitude scintillation.     

Time Transfer by Laser Link: Data analysis and validation to the ps level

The Time Transfer by Laser Link (T2L2) is a very high resolution time transfer technique based on the recording of arrival times of laser pulses at the satellite. T2L2 was designed to achieve time stability in the range of 1 ps over 1000 s and an accuracy better than 100 ps. The project is in operation onboard the Jason-2 satellite since June 2008. The principle is based on the Satellite Laser Ranging (SLR) technology; it uses the input of 20–25 SLR stations of the international laser network which participate in the tracking. This paper focuses on the data reduction process which was developed specifically to transform the raw information given by both space instrument and ground network: first to identify the triplets (ground and onboard epochs and time of flight of the laser pulse), second to estimate a usable product in terms of ground-to-space time transfer (including instrumental corrections), and thirdly to produce synchronization between any pair of remote ground clocks. In describing the validation of time synchronizations, the paper opens a way for monitoring the time difference between ultra-stable clocks thanks to a laser link at a few ps level for Common View passes. It highlights however that without accurately characterizing the onboard oscillator of Jason-2 and knowing the unavailability of time calibrations of SLR stations generally, time transfer over intercontinental distances remain difficult to be accurately estimated.