Remote Sensing Laboratory Research Articles

EXPERIENCE OF USING PLANET EARTH OBSERVATION DATA IN RETRAINING COURSES FOR EDUCATORS IN THE JUNIOR ACADEMY OF SCIENCES OF UKRAINE

This paper discusses the background and presents the results of the first in Ukraine advanced training course for educators in Remote Sensing, “Fundamentals of Remote Sensing: History and Practice”, organized by the Geographic Information Systems and Remote Sensing Laboratory of the National Center, “Junior Academy of Sciences of Ukraine”. The Junior Academy of Sciences of Ukraine is a system of extracurricular education committed to the development and implementation of science education techniques. Currently, the Academy has more than 250,000 students working in 64 scientific areas. In 2018, the Junior Academy of Sciences of Ukraine received the status of Category 2 Science Education Center under the auspices of UNESCO and joined the network of Copernicus Academies. The Junior Academy of Sciences of Ukraine has been promoting the application of satellite imagery in education for over ten years. One of the outputs of this activity is the advanced training course for educators. This paper describes the online training course that was held in spring 2021 and 2022. The course was open to all interested teachers. A total of 41 educators from 13 regions of Ukraine have completed the training course in the span of two years. The paper describes the stages of special course organization, topics, and tools used in the lectures and case studies, including an overview of the data sources, such as EO-Browser, ArcGIS Online, Google my Maps, NASA Worldview and Google Earth, etc. The paper presents a detailed description of one of the hands-on case studies of water bodies in EO-Browser. It was established that the conducted special course promoted the educators’ interest in bringing Earth observation to classrooms. Increased number of GIS and Remote Sensing after-school clubs in the territorial branches of the Junior Academy of Sciences (from 1 in Kyiv as of 2019 to 9 in the regions as of 2022, namely in Poltava, Sumy, Volyn, Kyiv, Kirovohrad, Zakarpattia, Lviv, Kharkiv and Chernivtsi) is indicative of the course’s effectiveness.

Radar and ground-level measurements of clouds and precipitation collected during the POPE 2020 campaign at Princess Elisabeth Antarctica

Abstract. The datasets presented in this article were collected during a 4-month measurement campaign at the Belgian research base Princess Elisabeth Antarctica (PEA). The campaign, named PEA Orographic Precipitation Experiment (POPE), was conducted by the Environmental Remote Sensing Laboratory of the École Polytechnique Fédérale de Lausanne, with the logistical support of the International Polar Foundation, between the end of November 2019 and the beginning of February 2020. The datasets have been collected at five different sites. A W-band Doppler cloud profiler and a multi-angle snowflake camera (MASC) have been deployed in the immediate proximity of the main building of the station. An X-band dual-polarization Doppler scanning weather radar was installed 1.9 km southeast of PEA. Information on the various hydrometeor types have been derived from its measurements and from the images collected by the MASC. The remaining three sites were located in a transect across the mountain chain south of the base, between 7 and 17 km apart from each other. At each site, a K-band Doppler profiler and an automated weather station have been deployed. A pyrgeometer and a pyranometer accompanied the instruments at the site in the middle of the transect. The radar variables and the measurements collected by the weather stations and radiometers are available at https://doi.org/10.5281/zenodo.7428690 (Ferrone and Berne, 2023c). The Doppler spectra are available at https://doi.org/10.5281/zenodo.7507087 (Ferrone and Berne, 2023a) and https://doi.org/10.5075/epfl-lte-299685 (Ferrone and Berne, 2023b). A case study, covering the precipitation event recorded on 23 December 2019, is presented to illustrate the various datasets. Overall, the availability of radar measurements over a complex terrain, relatively far from a scientific base, is extremely rare in the Antarctic context and opens a wide range of possibilities for precipitation studies over the region.

Mining Joint Intraimage and Interimage Context for Remote Sensing Change Detection

Recent deep learning methods for change detection focus on excavating more discriminative context within individual images. However, due to seasonal change, noise, and so on, the appearance of objects tends to be more heterogeneous among various scenes. Consequently, the above intra-image context is inadequate to represent specific-category objects and pseudo changes would be inevitable in detection results. To deal with this issue, we propose a context aggregation network (CANet) to mine inter-image context over all training images for further enhancing intra-image context. Specifically, a Siamese network attached with temporal attention modules is served as a feature encoder to extract multi-scale temporal features from bitemporal images. Then, a context extraction module is devised to capture long-range spatial-channel context within individual images. Meanwhile, context representations of underlying categories in the scene are inferred using all training images in an unsupervised manner. Finally, these two kinds of contextual information are aggregated to one which is subsequently fed into a multi-scale fusion module to produce the detection map. CANet is compared with several state-of-the-art methods on three benchmark datasets, including the season-varying change detection (SVCD) dataset, the Sun Yat-sen University change detection (SYSU-CD) dataset, and the Learning Vision and Remote Sensing Laboratory building change detection (LEVIR-CD) dataset. It is demonstrated that our method outperforms all comparison methods in terms of F1, overall accuracy (OA), and Intersection-of-Union (IoU). The results of CANet on three datasets are available at https://github.com/NuistZF/CANet-for-change-detection and codes will be public soon.

Agricultural Remote Sensing Laboratory, Faculty of Food and Agricultural Sciences, Fukushima University

Agricultural Remote Sensing Laboratory, Faculty of Food and Agricultural Sciences, Fukushima University

A Densely Attentive Refinement Network for Change Detection Based on Very-High-Resolution Bitemporal Remote Sensing Images

Detecting changes using bitemporal remote sensing imagery is vital to understand the dynamics of the land surface. Existing change detection models based on deep learning suffer from the problem of scale variation and pseudochange due to their insufficient multilevel aggregation and inadequate capability of feature representation, which limits the accuracy. This study proposes a densely attentive refinement network (DARNet) to improve change detection on bitemporal very-high-resolution remote sensing images. DARNet is based on the U-shape encoder–decoder architecture with the Siamese network as a feature extractor. The dense skip connection module (DSCM) is employed between the decoder and the encoder to aggregate multilevel feature maps. The hybrid attention module (HAM) is integrated to exploit contextual information and generate discriminative features. The recurrent refinement module (RRM) is exploited to progressively refine the predicted change maps during the decoding process. Experiments on testing the model performance were conducted on three benchmark datasets: the season-varying change detection (SVCD) dataset, the Sun Yat-sen University change detection (SYSU-CD) dataset, and the Learning Vision and Remote Sensing Laboratory building change detection (LEVIR-CD) dataset. The experimental results demonstrate that DARNet outperforms state-of-the-art models with kappa of 96.58%, 75.35%, and 90.69% for the SVCD, SYSU-CD, and LEVIR-CD datasets, respectively.

Radar and ground-level measurements of precipitation collected by the École Polytechnique Fédérale de Lausanne during the International Collaborative Experiments for PyeongChang 2018 Olympic and Paralympic winter games

Abstract. This article describes a 4-month dataset of precipitation and cloud measurements collected during the International Collaborative Experiments for PyeongChang 2018 Olympic and Paralympic winter games (ICE-POP 2018). This paper aims to describe the data collected by the Environmental Remote Sensing Laboratory of the École Polytechnique Fédérale de Lausanne. The dataset includes observations from an X-band dual-polarisation Doppler radar, a W-band Doppler cloud profiler, a multi-angle snowflake camera and a two-dimensional video disdrometer (https://doi.org/10.1594/PANGAEA.918315, Gehring et al., 2020a). Classifications of hydrometeor types derived from dual-polarisation measurements and snowflake photographs are presented. The dataset covers the period from 15 November 2017 to 18 March 2018 and features nine precipitation events with a total accumulation of 195 mm of equivalent liquid precipitation. This represents 85 % of the climatological accumulation over this period. To illustrate the available data, measurements corresponding to the four precipitation events with the largest accumulation are presented. The synoptic situations of these events were contrasted and influenced the precipitation type and accumulation. The hydrometeor classifications reveal that aggregate snowflakes were dominant and that some events featured significant riming. The combination of dual-polarisation variables and high-resolution Doppler spectra with ground-level snowflake images makes this dataset particularly suited to study snowfall microphysics in a region where such measurements were not available before.

THE COURSE OF FUNDAMENTALS OF REMOTE SENSING FOR EDUCATORS OF JUNIOR ACADEMY OF SCIENCES OF UKRAINE

This paper discusses the background and presents the results of the first in Ukraine advanced training course for educators in Remote Sensing, “Fundamentals of Remote Sensing: History and Practice”, organized by the Geographic Information Systems and Remote Sensing Laboratory of the National Center, “Junior Academy of Sciences of Ukraine”. The Junior Academy of Sciences of Ukraine is a system of extracurricular education committed to the development and implementation of science education techniques. Currently, the Academy has more than 250,000 students working in 64 scientific areas. In 2018, the Junior Academy of Sciences of Ukraine received the status of Category 2 Science Education Center under the auspices of UNESCO and joined the network of Copernicus Academies. The Junior Academy of Sciences of Ukraine has been promoting the application of satellite imagery in education for over ten years. One of the outputs of this activity is the advanced training course for educators. This paper describes the online training course that was held in spring 2021. The course was open to all interested teachers. 21 educators from 11 Oblasts of Ukraine have completed the training course. The majority of the course participants were teachers of Geography. Environmental science, biology, chemistry, physics, and computer science experts have also participated in the training course. In addition, the course participants included four associate professors and two professors from higher educational institutions, and one PhD in Geography. From a gender perspective, 66.6 % of the registered course participants were female and 33.3 % — male. The effectiveness of the course was evaluated based on the participants’ responses collected in two surveys and a concluding interview. The paper describes the stages of special course organization, topics, and tools used in the lectures and case studies, including an overview of the data sources, such as EO Browser, ArcGIS Online, and Google Earth, etc. The paper presents a detailed description of one of the hands-on case studies of water bodies in EO Browser.

Comparison of the Sentinel-1B Synthetic Aperture Radar With Airborne Microwave Sensors in an Extra-Tropical Cyclone

In Winter 2017, the University of Massachusetts Amherst's Imaging Wind and Rain Airborne Profiler (IWRAP) was flown on a National Oceanic and Atmospheric Administration (NOAA) WP-3D Hurricane Hunter aircraft under the direction of scientists from Center for Satellite Applications and Research (STAR) at NOAA/National Environmental Satellite, Data, and Information Service (NESDIS) over the North Atlantic ocean out of Shannon, Ireland. IWRAP is a dual-frequency, conically scanning, profiling Doppler radar initially developed by Microwave Remote Sensing Laboratory (MIRSL) at the University of Massachusetts Amherst that is routinely installed on the NOAA WP-3D research aircraft. The flight on February 6, 2017, targeted a region of high winds (greater than 30 m/s) that was also observed by the Sentinel-1B satellite's synthetic aperture radar. Sentinel-1B was configured to observe in extended wide swath mode in both VV- and VH-polarizations, whereas the IWRAP C-band radar was configured to measure all of VV-, VH-, and HH-polarizations. IWRAP and Sentinel-1B VV and VH normalized radar cross section (NRCS) at the same Earthincidence angle along the flight path match reasonably well during the entire flight, but some additional trends between aircraft and satellite can be observed. IWRAP VV-polarized NRCS generally match the CMOD5.h geophysical model function (GMF), suggesting errors in the Sentinel-1B processing chain.

Variability of the Boundary Layer Over an Urban Continental Site Based on 10 Years of Active Remote Sensing Observations in Warsaw

Atmospheric boundary layer height (ABLH) was observed by the CHM15k ceilometer (January 2008 to October 2013) and the PollyXT lidar (July 2013 to December 2018) over the European Aerosol Research LIdar NETwork to Establish an Aerosol Climatology (EARLINET) site at the Remote Sensing Laboratory (RS-Lab) in Warsaw, Poland. Out of a maximum number of 4017 observational days within this period, a subset of quasi-continuous measurements conducted with these instruments at the same wavelength (1064 nm) was carefully chosen. This provided a data sample of 1841 diurnal cycle ABLH observations. The ABLHs were derived from ceilometer and lidar signals using the wavelet covariance transform method (WCT), gradient method (GDT), and standard deviation method (STD). For comparisons, the rawinsondes of the World Meteorological Organization (WMO 12374 site in Legionowo, 25 km distance to the RS-Lab) were used. The ABLHs derived from rawinsondes by the skew-T-log-p method and the bulk Richardson (bulk-Ri) method had a linear correlation coefficient (R2) of 0.9 and standard deviation (SD) of 0.32 km. A comparison of the ABLHs obtained for different methods and instruments indicated a relatively good agreement. The ABLHs estimated from the rawinsondes with the bulk-Ri method had the highest correlations, R2 of 0.80 and 0.70 with the ABLHs determined using the WCT method on ceilometer and lidar signals, respectively. The three methods applied to the simultaneous, collocated lidar, and ceilometer observations (July to October 2013) showed good agreement, especially for the WCT method (R2 of 0.94, SD of 0.19 km). A scaling threshold-based algorithm was proposed to homogenize ceilometer and lidar datasets, which were applied on the lidar data, and significantly improved the coherence of the results (R2 of 0.98, SD of 0.11 km). The difference of ABLH between clear-sky and cloudy conditions was on average below 230 m for the ceilometer and below 70 m for the lidar retrievals. The statistical analysis of the long-term observations indicated that the monthly mean ABLHs varied throughout the year between 0.6 and 1.8 km. The seasonal mean ABLH was of 1.16 ± 0.16 km in spring, 1.34 ± 0.15 km in summer, 0.99 ± 0.11 km in autumn, and 0.73 ± 0.08 km in winter. In spring and summer, the daytime and nighttime ABLHs appeared mainly in a frequency distribution range of 0.6 to 1.0 km. In winter, the distribution was common between 0.2 and 0.6 km. In autumn, it was relatively balanced between 0.2 and 1.2 km. The annual mean ABLHs maintained between 0.77 and 1.16 km, whereby the mean heights of the well-mixed, residual, and nocturnal layer were 1.14 ± 0.11, 1.27 ± 0.09, and 0.71 ± 0.06 km, respectively (for clear-sky conditions). For the whole observation period, the ABLHs below 1 km constituted more than 60% of the retrievals. A strong seasonal change of the monthly mean ABLH diurnal cycle was evident; a mild weakly defined autumn diurnal cycle, followed by a somewhat flat winter diurnal cycle, then a sharp transition to a spring diurnal cycle, and a high bell-like summer diurnal cycle. A prolonged summertime was manifested by the September cycle being more similar to the summer than autumn cycles.

Introducing an Index in Determination of Soil Total Nitrogen Content in an Agricultural Soil Using Laboratory Spectrometry

ABSTRACTNitrogen is an important nutrient in the process of crop growth. The aim of this research was to develop several models to estimate the Soil Total Nitrogen Content (STNC) using soil reflectance values. For this, 138 samples for three different types of soil: loam, sandy loam, and silty loam with different nitrogen contents were prepared. After pre-processing of data, the most effective spectral bands for the STNC assessment were determined using two different approaches. These bands were 1415, 1465, 1475, 1895, 1925, 1955, 1985, 2025, 2215, and 2305 nm. Subsequently, several statistical models were developed using the selected bands and multivariate linear regression analysis. The root mean square error (RMSE) of the most accurate models for loam, sandy loam, and silty loam was 0.35, 0.38, and 0.57g/kg, respectively. Additionally, three models were developed regardless of soil types. These models were independent of the soil type and can be applied to any agricultural soil. The most accurate independent model had an RMSE of 0.54 g/kg and a correlation coefficient (R) of 0.97. Although these models are valid for laboratory measurements, but the extension of these models to satellite application is the objective of future studies in the Khavaran Remote Sensing Laboratory (KRSLab).

Interrelations between surface, boundary layer, and columnar aerosol properties derived in summer and early autumn over a continental urban site in Warsaw, Poland

Abstract. PollyXT Raman polarization lidar observations were performed at the Remote Sensing Laboratory (RS-Lab) in Warsaw (52.2109∘ N, 20.9826∘ E), Poland, in the framework of the European Aerosol Research Lidar Network (EARLINET) and the Aerosol, Clouds, and Trace gases Research Infrastructure (ACTRIS) projects. Data collected in July, August, and September of 2013, 2015, and 2016 were analysed using the classical Raman approach. In total, 246 sets of intact profiles, each set comprising particle extinction (α) and backscatter coefficients (β) as well as linear particle depolarization ratios (δ) at 355 nm and 532 nm, were derived for statistical investigations and stored in the EARLINET/ACTRIS database. The main analysis was focused on intensive optical properties obtained within the atmospheric boundary layer (ABL). Their interrelations were discussed for different periods: the entire day; nighttime, with respect to the nocturnal boundary layer (NL) and the residual boundary layer (RL); at sunrise, with respect to the morning transition boundary layer (MTL); and from late afternoon until sunset, with respect to the well-mixed boundary layer (WML). Within the boundary layer, the lidar-derived optical properties (entire day, 246 sets) revealed a mean aerosol optical depth (AODABL) of 0.20±0.10 at 355 nm and 0.11±0.06 at 532 nm; a mean Ångström exponent (ÅEABL) of 1.54±0.37; a mean lidar ratio (LRABL) of 48±17 sr at 355 nm and 41±15 sr at 532 nm; a mean linear particle depolarization ratio (δABL) of 0.02±0.01 at 355 nm and 0.05±0.01 at 532 nm; and a mean water vapour mixing ratio (WVABL) of 8.28±2.46 g kg−1. In addition, the lidar-derived daytime boundary layer optical properties (for the MTL and WML) were compared with the corresponding daytime columnar aerosol properties derived from the multi-filter rotating shadowband radiometer (MFR-7) measuring within the National Aerosol Research Network (PolandAOD-NET) and the CE318 sun photometer of the Aerosol Robotic NETwork (AERONET). A high linear correlation of the columnar aerosol optical depth values from the two latter instruments was obtained in Warsaw (a correlation coefficient of 0.98 with a standard deviation of 0.02). The contribution of the aerosol load in the summer and early-autumn free troposphere can result in an AODCL value that is twice as high as the AODABL over Warsaw. The occurrence of a turbulence-driven aerosol burst from the boundary layer into the free troposphere can further increase this difference. Aerosol within the ABL and in the free troposphere was interpreted based on comparisons of the properties derived at different altitudes with values reported in the literature, which were characteristic for different aerosol types, in combination with backward trajectory calculations, satellite data, and model outputs. Within the boundary layer, the aerosol consisted of either urban anthropogenic pollution (∼ 61 %) or mixtures of anthropogenic aerosol with biomass-burning aerosol (< 14 %), local pollen (< 7 %), or Arctic marine particles (< 5 %). No significant contribution of mineral dust was found in the boundary layer. The lidar-derived atmospheric boundary layer height (ABLH) and the AODABL exhibited a positive correlation (R of 0.76), associated with the local anthropogenic pollution (most pronounced for the RL and WML). A positive correlation of the AODABL and LRABL and a negative correlation of the ÅEABL and LRABL, as well as the expected negative trends for the WVABL (and surface relative humidity, RH) and δABL, were observed. Relations of the lidar-derived aerosol properties within the ABL and the surface in situ measurements of particulate matter with an aerodynamic diameter less than 10 µm (PM10) and less than 2.5 µm (PM2.5) measured by the Warsaw Regional Inspectorate for Environmental Protection (WIOS) network, and the fine-to-coarse mass ratio (FCMR) were investigated. The FCMR and surface RH showed a positive correlation even at nighttime (R of 0.71 for the MTL, 0.63 for the WML, and 0.6 for the NL), which generally lacked statistically significant relations. A weak negative correlation of the FCMR and δABL (more pronounced at 532 nm at nighttime) and no casual relation between the FCMR and ÅEABL were found. Most interestingly, distinct differences were observed for the morning transition layer (MTL) and the well-mixed layer (WML). The MTL ranged up to 0.6–1 km, and was characterized by a lower AODABL(<0.12), wetter conditions (RH 50–80 %), smaller particles (ÅEABL of 1–2.2; FCMR from 0.5 to 3), and a low LRABL of between 20 and 40 sr. The WML ranged up to 1–2.5 km and exhibited a higher AODABL (reaching up to 0.45), drier conditions (RH 25–60 %), larger particles (ÅEABL of 0.8–1.7; FCMR of 0.2–1.5), and a higher LRABL of up to 90 sr.

RANGE MIGRATION ALGORITHM IN THE PROCESSING CHAIN OF SIGNALS OF A GROUND-BASED SAR SENSOR

Abstract. Synthetic aperture radar (SAR) system based on frequency modulated continuous wave (FM-CW) transmission is a viable option for producing high-resolution ground-based imaging radars. Compared with pulsed SAR systems, the combination of FM-CW technology and SAR processing techniques have the advantages of small cubage, lightweight, cost-effectiveness, and high resolution in the SAR image. These characteristics make FM-CW SAR suitable to be deployed as payload on ground Based SARs (GB-SARs) for environmental and civilian applications. In this paper, the Range Migration Algorithm (RMA) is used in the processing chain of a Ground-Based SAR (GB-SAR) sensor. The mentioned sensor has been developed in Microwave Remote Sensing Laboratory (MReSL) at the School of Surveying and Geospatial Engineering, the University of Tehran for the generation of a complex image from the raw signal. The raw signal is acquired with that sensor working at S-band, frequency modulating from 2.26 GHz to 2.59 GHz.

SENTINEL FOR APPLICATIONS IN AGRICULTURE

Abstract. The European Union and the European Space Agency (EU/ESA) have promoted since 1998 (Baveno Manifesto*) the GMES Programme (Global Monitoring for Environment and Security), nowadays called Copernicus (www.copernicus.eu). In the agriculture domain, the use of Copernicus Sentinel imagery and its services are providing several new opportunities. The knowledge of fundamentals of Earth Observation/Geographic Information EO/GI, namely Geomatics, for the development of innovative strategies for professional skills adequacy and capacity building, supporting Copernicus user uptake, becomes mandatory (Gomarasca, 2009). The target is to help bridging gaps between supply and demand of education and training for geospatial sector (www.eo4geo.eu). The innovative and strategical novelties are the complete free access to Sentinel time series imagery and digital image processing software “Sentinel toolboxes” such as SNAP (Sentinel Application Platform) for different environments (Windows, Mac, Unix). The paper introduce topics as crop mapping and monitoring, biophysical parameters, phenology and yield estimations, through several concluded or ongoing international projects such as: ERMES -FP7 (http://www.ermes-fp7space.eu/it/homepage/, Busetto et al. 2017) and SATURNO (https://www.progettosaturno.it/, Nutini et al., 2018) devoted to the regional agricultural monitoring. As conclusion, SNAP software for image processing of Sentinel data was demonstrated and tested together with Earth Engine software for specific vertical agriculture applications. The topics reported in this paper have been part of the Summer School ‘Sentinel for Applications in Agriculture’ supported by the Copernicus programme, several scientific associations (AIT, ASITA, EARSeL - European Association of Remote Sensing Laboratories), the European Erasmus+ project EO4GEO, University Departments and Geo-Information Companies.

Vehicle-mounted validations of a 1.55-μm all-fiber continuous-wave coherent laser radar for measuring aircraft airspeed

The lidar velocimetry is an airspeed sensor for the application of the lidar remote sensing technology to airborne air data systems. This study discusses a 1.55-μm all-fiber continuous-wave coherent laser radar (CLR) system, with a balanced photodetector as receiving frequency discriminators, developed by the Ocean Remote Sensing Laboratory, Ocean University of China. The system exhibits advantages including a compact structure, light weight, and high precision. The coupling efficiency of light from free space to single-mode fiber is analyzed, and the optimal value of the relative aperture of the coupling lens is given (d / f = 0.2107). The expression of carrier-to-noise ratio is simulated based on the parameters of the components used in the system to obtain the optimum local oscillator power (0.724 mW). The presented result of the hard target experiment and vehicle-mounted experiment in August 2018 indicates that the correlation coefficient between the lidar measured value and actual speed value is better than 0.99, relative error is less than 2.6%, and standard deviation is less than 0.22 m / s. The aforementioned validation results indicate that the CLR system exhibits stable and reliable performance and the potential to measure aircraft airspeed.

Intercalibration of Cross-Track Scanners in GPM Constellation

This paper discusses the intersatellite radiometric calibration for microwave sounders in support of NASA’s Global Precipitation Measurement (GPM) mission. This paper is an extension of previous GPM Intersatellite Calibration Working Group work, which assesses the robustness of the Central Florida Remote Sensing Laboratory “double difference” technique for sounders intercalibration. In this paper, we will analyze the intercalibration between the Advanced Technology Microwave Sounder on board the National Polar-orbiting Operational Environmental Satellite System Preparatory Project, the Microwave Humidity Sounder on board the MetOp-A and MetOp-B and NOAA-18 and NOAA-19, and the Sondeur Atmospherique du Profil d’Humidite Intertropicale par Radiometrie sounder onboard the Megha-Tropiques satellite. The sensitivity of the calibration to the geophysical parameters and incidence angles is investigated using two years data. Here, it is observed that a reliable intercalibration can be made only when the path lengths of the two sensors are similar, so when matching the scan angles for the two sensors, the sensitivity of the biases upon the water vapor profiles is significantly reduced.

The importance of core habitat for a threatened species in changing landscapes

Abstract Habitat loss, fragmentation, and alteration of the landscape matrix are interdependent processes, collectively responsible for most recent species extinctions. Thus, determining the extent to which these landscape processes affect animals is critical for conservation. However, researchers have often assumed that interdependent effects are independently related to animals’ responses, underestimating the importance of one or several landscape processes in driving species declines. We demonstrate how to disentangle the interdependent effects of habitat amount, fragmentation, and edge context on population size by assessing abundance of a rapidly declining grassland songbird species (grasshopper sparrow Ammodramus savannarum) in eastern Kansas (USA). We conducted >7,000 point count surveys at >2,000 sites over two breeding seasons, then modelled the direct, interactive, and indirect effects of landscape factors on abundance within spatial scales (200‐, 400‐, 800‐, and 1,600‐m radii) relevant to our focal species’ dispersal behaviour. Sparrow abundance correlated most strongly with landscape structure within 400‐m radii, increasing nonlinearly with grassland area and decreasing with the proportion of grassland near cropland or woody edges. Sparrows’ negative response to cropland edges was mostly an added, indirect consequence of reduced grassland area, whereas sparrows’ stronger negative response to woody edges was not attributable to variation in grassland area. Fragmentation and edge context mattered most in landscapes comprising c. 50%–80% grassland. Synthesis and applications. In our research, abundance of a threatened grassland songbird was influenced more by core grassland area (a function of total grassland area, fragmentation, and edge context) than total grassland area per se. Moreover, a local extinction threshold of c. 50% grassland indicated that small amounts of habitat were unsuitable for our focal species regardless of habitat configuration or matrix type. Local extinction thresholds in response to habitat area provide clear baseline targets for land managers; above those thresholds, configuration and the matrix can be modified to increase abundance of edge‐sensitive animals. Conflicting evidence in the literature regarding the importance of fragmentation and matrix features could be partially explained by species‐level traits, or methodological issues such as defining landscapes at ecologically arbitrary spatial scales, assessing landscape quality using species richness, and ignoring interactive and indirect effects.

Validation of Sensing Ocean Surface Currents Using Multi-Frequency HF Radar Based on a Circular Receiving Array

To reduce the floor space of receiving antenna arrays, the Radio Ocean Remote SEnsing (RORSE) laboratory of Wuhan University developed a circular receiving array for a multi-frequency high frequency (MHF) radar system in 2014, consisting of seven uniformly spaced antenna elements positioned on a circle with a diameter of 5 m. The new system, which is abbreviated MHF-C radar, adopts frequency modulated interrupted continuous wave (FMICW) chirps and is capable of simultaneously operating at a maximum of four frequencies in the band of 7.5–25 MHz, and providing current, wave and wind maps every ten minutes. The phase direction-finding method is utilized to estimate the directions of the current signals, and array phase uncertainties are also taken into consideration in the signal model. This paper introduces the system in detail and investigates the performance of current measurements using MHF-C radars installed at Shengshan and Zhujiajian along the coast of the East China Sea. Radial current measurements derived from 8.27 MHz and 19.20 MHz at the same range are compared. Observations and comparisons between MHF-C radars and acoustic Doppler current profilers (ADCPs) are also presented in this paper. The results preliminarily demonstrate that the MHF-C radar system is capable of maintaining the same performance for current measurements whenever it steers to any other azimuth in the coverage and has a good ability to measure currents.

Novel lidar algorithms for atmospheric slantrange visibility, planetary boundary layer height, meteorogical phenomena and atmospheric layering measurements

In this paper we present a development of novel algorithms and techniques implemented within the Laser Remote Sensing Laboratory (LRSL) of the National Technical University of Athens (NTUA), in collaboration with Raymetrics S.A., in order to incorporate them into a 3-Dimensional (3D) lidar. The lidar is transmitting at 355 nm in the eye safe region and the measurements then are transposed to the visual range at 550 nm, according to the World Meteorological Organization (WMO) and the International Civil Aviation Organization (ICAO) rules of daytime visibility. These algorithms are able to provide horizontal, slant and vertical visibility for tower aircraft controllers, meteorologists, but also from pilot’s point of view. Other algorithms are also provided for detection of atmospheric layering in any given direction and vertical angle, along with the detection of the Planetary Boundary Layer Height (PBLH).

RapidScat Cross-Calibration Using the Double Difference Technique

RapidScat is a National Aeronautics and Space Administration (NASA) Ku-Band scatterometer that was operated onboard the International Space Station between September 2014 and August 2016 when the mission effectively ended after an irrecoverable instrument failure. A unique non-Sun-synchronous orbit facilitated global contiguous geographical sampling between the ±56° latitude. For the first time, such an orbit enabled an overlap with other scatterometers flying in Sun-synchronous orbits. The double-difference technique was developed and successfully used for microwave radiometer calibration at the Remote Sensing Laboratory at the University of Central Florida, USA. This paper presents the extension of the double difference methodology to scatterometry. The methodology has been adopted for the cross-instrument calibration between RapidScat and QuikScat scatterometers simultaneously orbiting the Earth on-board two independent satellite platforms. The double-difference technique was deployed to compare measurements from these two scatterometers, as a more accurate alternative to the classic single difference approach. The work summarized in this paper addressed a cross-calibration algorithm developed and applied to RapidScat and QuikScat data in the period from January 2015 to March 2016. The initial results of the statistical analysis and biases between the two scatterometers are presented. Calculated biases may be used for measurement correction and reprocessing.

Creating a Multidecadal Ocean Microwave Brightness Dataset: Three-Way Intersatellite Radiometric Calibration Among GMI, TMI, and WindSat

The Tropical Rainfall Measuring Mission (TRMM), launched in late November 1997 into a low earth orbit, produced the longest satellite-derived precipitation time series of 17 years. During the second half of this mission, a collection of cooperative weather satellites, with microwave radiometers, was combined to produce a 6-h tropical precipitation product, and the TRMM Microwave Imager (TMI) was used as the radiometric transfer standard to intercalibrate the constellation members. To continue this valuable precipitation climate data record, the Global Precipitation Mission (GPM) observatory was launched in February 2014, and the GPM Microwave Imager (GMI) became the new transfer standard that normalized the microwave radiance measurements of the GPM constellation radiometers. Previously, the Central Florida Remote Sensing Lab conducted intercomparisons over oceans, between TMI and the Naval Research Laboratory's WindSat polarimetric radiometer and found that the radiometric calibration of TMI relative to WindSat exhibited exceptional long-term radiometric stability over a period >8 years. Moreover, for purposes of assessing global climate change, it is crucial that a seamless transfer between the TRMM and the GPM microwave brightness temperature time series be achieved. Therefore, this paper presents arguments that the 3-way (WindSat, TMI, and GMI) intersatellite radiometric comparisons, performed during the 13-month period overlap, can be used to bridge the TRMM and GPM eras and assure a stable radiometric calibration between the diverse constellation's member radiometers.