On reconciling ground-based with spaceborne normalized radar cross section measurements

©2002 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.This study examines differences in the normalized radar cross section, derived from ground-based versus spaceborne radar data. A simple homogeneous half-space model, indicates that agreement between the two improves as 1) the distance from the scatterer is increased; and/or 2) the extinction coefficient increases

Analysis of the Canadian Boreal Forest Using Enhanced Resolution ERS-1 Scatterometer Imagery

Scatterometer backscatter measurements ( u0 ) are primarily and traditionally used to estimate wind speed and direction over the ocean. This paper presents an investigation of the backscatter coefficient of boreal forest and neighboring vegetation regions. ERS-1 backscatter A-values of the Canadian boreal forest are imaged using a resolution enhancement algorithm for this analysis. Regions of boreal forest, tundra, and grassland are individually analyzed over the extent of the ERS-1 scatterometer\u27s data set (1992-1995). The annual variation of the mean u0 value for each region is presented. Distinct seasonal variations exist for these vegetation types. Boreal forests exhibit a stronger response ( ...... 2.5 dB) during warm summer months than during the snow and ice covered winter months. The results of this study indicate good potential for further analysis of boreal forest regions using ERS-1 scatterometer data

Sea ice classification in the Weddell Sea based on scatterometer data

Sea ice type is an important factor for accurately calculating sea ice parameters such as sea ice concentration, sea ice area and sea ice thickness using satellite remote sensing data. In this study, sea ice in the Weddell Sea was classified from scatterometer data by the histogram threshold method and the Spreen model method, and evaluated and validated with the Antarctic Sea Ice Processes and Climate (ASPeCt) sea ice type ship-based observations. The results show that the two methods can both distinguish multi-year (MY) ice and first-year (FY) ice during the ice growth season, and that the histogram threshold method has a relatively larger MY ice classification extent than the Spreen model. The classification accuracy of the histogram threshold method is 77.8%, while the Spreen model method accuracy is 80.3% compared with the ship-based observations, thus indicating that the Spreen model method is better for discriminating MY ice from FY ice from scatterometer data. These results provide a basis and reference for further retrieval of long-time sea ice type information for the whole Antarctica

Gaps analysis and requirements specification for the evolution of Copernicus system for polar regions monitoring: addressing the challenges in the horizon 2020-2030

This work was developed as part of the European H2020 ONION (Operational Network of Individual Observation Nodes) project, aiming at identifying the technological opportunity areas to complement the Copernicus space infrastructure in the horizon 2020–2030 for polar region monitoring. The European Earth Observation (EO) infrastructure is assessed through of comprehensive end-user need and data gap analysis. This review was based on the top 10 use cases, identifying 20 measurements with gaps and 13 potential EO technologies to cover the identified gaps. It was found that the top priority is the observation of polar regions to support sustainable and safe commercial activities and the preservation of the environment. Additionally, an analysis of the technological limitations based on measurement requirements was performed. Finally, this analysis was used for the basis of the architecture design of a potential polar mission.Peer ReviewedPostprint (published version

Inter-comparison and evaluation of Arctic sea ice type products

oai:publications.copernicus.org:tc102910Arctic sea ice type (SITY) variation is a sensitive indicator of climate change. However, systematic inter-comparison and analysis for SITY products are lacking. This study analysed eight daily SITY products from five retrieval approaches covering the winters of 1999–2019, including purely radiometer-based (C3S-SITY), scatterometer-based (KNMI-SITY and IFREMER-SITY) and combined ones (OSISAF-SITY and Zhang-SITY). These SITY products were inter-compared against a weekly sea ice age product (i.e. NSIDC-SIA – National Snow and Ice Data Center sea ice age) and evaluated with five synthetic aperture radar (SAR) images. The average Arctic multiyear ice (MYI) extent difference between the SITY products and NSIDC-SIA varies from -1.32×106 to 0.49×106 km2. Among them, KNMI-SITY and Zhang-SITY in the QuikSCAT (QSCAT) period (2002–2009) agree best with NSIDC-SIA and perform the best, with the smallest bias of -0.001×106 km2 in first-year ice (FYI) extent and -0.02×106 km2 in MYI extent. In the Advanced Scatterometer (ASCAT) period (2007–2019), KNMI-SITY tends to overestimate MYI (especially in early winter), whereas Zhang-SITY and IFREMER-SITY tend to underestimate MYI. C3S-SITY performs well in some early winter cases but exhibits large temporal variabilities like OSISAF-SITY. Factors that could impact performances of the SITY products are analysed and summarized. (1) The Ku-band scatterometer generally performs better than the C-band scatterometer for SITY discrimination, while the latter sometimes identifies FYI more accurately, especially when surface scattering dominates the backscatter signature. (2) A simple combination of scatterometer and radiometer data is not always beneficial without further rules of priority. (3) The representativeness of training data and efficiency of classification are crucial for SITY classification. Spatial and temporal variation in characteristic training datasets should be well accounted for in the SITY method. (4) Post-processing corrections play important roles and should be considered with caution.</p

Master of Science

thesisRecent accelerated mass loss offset by increased Arctic precipitation highlights the importance of a comprehensive understanding of the mechanisms controlling mass balance on the Greenland ice sheet. Knowledge of the spatiotemporal variability of snow accumulation is critical to accurately quantify mass balance, yet, considerable uncertainty remains in current snow accumulation estimates. Previous studies have shown the potential for large-scale retrievals of snow accumulation rates in regions that experience seasonal melt-refreeze metamorphosis using active microwave remote sensing. Theoretical backscatter models used in these studies to validate the hypothesis that observed decreasing freezing season backscatter signatures are linked to snow accumulation rates suggest the relationship is inverse and linear (dB). The net backscatter measurement is dominated by a Mie scattering response from the underlying ice-facie. Two-way attenuation resulting from a Raleigh scattering response within the overlying layer of snow accumulation forces a decrease in the backscatter measurement over time with increased snow accumulation rates. Backscatter measurements acquired from NASA's Ku-band SeaWinds scatterometer on the QuikSCAT satellite together with spatially calibrated snow accumulation rates acquired from the Polar MM5 mesoscale climate model are used to evaluate this relationship. Regions that experienced seasonal melt-refreeze metamorphosis and potentially formed dominant scattering layers are delineated, iv freeze-up and melt-onset dates identifying the freezing season are detected on a pixel-by-pixel basis, freezing season backscatter time series are linearly regressed, and a microwave snow accumulation metric is retrieved. A simple empirical relationship between the retrieved microwave snow accumulation metric (dB), , and spatially calibrated Polar MM5 snow accumulation rates (m w. e.), , is derived with a negative correlation coefficient of R=-.82 and a least squares linear fit equation of . Results indicate that an inverse relationship exists between decreasing freezing season backscatter decreases and snow accumulation rates; however, this technique fails to retrieve accurate snow accumulation estimates. An alternate geometric relationship is suggested between decreasing freezing season backscatter signatures, snow accumulation rates, and snowpack stratigraphy in the underlying ice-facie, which significantly influences the microwave scattering mechanism. To understand this complex relationship, additional research is required

Applications of satellite ‘hyper-sensing’ in Chinese agriculture:Challenges and opportunities

Ensuring adequate food supplies to a large and increasing population continues to be the key challenge for China. Given the increasing integration of China within global markets for agricultural products, this issue is of considerable significance for global food security. Over the last 50 years, China has increased the production of its staple crops mainly by increasing yield per unit land area. However, this has largely been achieved through inappropriate agricultural practices, which have caused environmental degradation, with deleterious consequences for future agricultural productivity. Hence, there is now a pressing need to intensify agriculture in China using practices that are environmentally and economically sustainable. Given the dynamic nature of crops over space and time, the use of remote sensing technology has proven to be a valuable asset providing end-users in many countries with information to guide sustainable agricultural practices. Recently, the field has experienced considerable technological advancements reflected in the availability of ‘hyper-sensing’ (high spectral, spatial and temporal) satellite imagery useful for monitoring, modelling and mapping of agricultural crops. However, there still remains a significant challenge in fully exploiting such technologies for addressing agricultural problems in China. This review paper evaluates the potential contributions of satellite ‘hyper-sensing’ to agriculture in China and identifies the opportunities and challenges for future work. We perform a critical evaluation of current capabilities in satellite ‘hyper-sensing’ in agriculture with an emphasis on Chinese sensors. Our analysis draws on a series of in-depth examples based on recent and on-going projects in China that are developing ‘hyper-sensing’ approaches for (i) measuring crop phenology parameters and predicting yields; (ii) specifying crop fertiliser requirements; (iii) optimising management responses to abiotic and biotic stress in crops; (iv) maximising yields while minimising water use in arid regions; (v) large-scale crop/cropland mapping; and (vi) management zone delineation. The paper concludes with a synthesis of these application areas in order to define the requirements for future research, technological innovation and knowledge exchange in order to deliver yield sustainability in China

Controls of the sea ice extent in the Ross Sea and development of a wireless sensor network.

Polar sea ice is an important climatic variable. In the Arctic, the steady decrease in sea ice since the 1970's is a direct result of global warming. Due to the different land and ocean distribution in the Southern Hemisphere as well as circulatory effects from the ozone hole, Antarctica is isolated from these changes. These along with other factors have meant that Antarctic sea ice has experienced a slight increase over the same time period. Sea ice extent (SIE) is controlled by physical processes such as wind and ocean currents and temperature gradients, and these contribute to the seasonal and long term patterns in the formation and melting of sea ice. To date, climate models have had only limited success in modelling SIE and its geographic variation. The most commonly used measure to compare observations and models is the total sea ice area. However, observations suggest that the spatial variability of sea ice in response to climate drivers is complicated and differs markedly around the Antarctic. Various studies have suggested schemes for analysing SIE in terms of regional effects, although these schemes are generally somewhat arbitrary and may not be optimal for analysis of certain atmospheric circulation patterns. This research examines a new method for Antarctic sea ice analysis. Using sets of satellite based observations of the SIE over the entire Antarctic continent, the edge of the sea ice can be described in terms of an ellipse. This provides an integrated measure of sea ice that also describes geographical variations while being mathematically simple to describe in terms of the five parameters that completely define an ellipse (centroid coordinates; major and minor axes lengths; rotation angle of major axis). This study demonstrates that the elliptical diagnostic analysis of sea ice captures seasonal and long term behaviour in sea ice well, and this behaviour was analysed in terms of atmospheric circulation patterns such as the El Ni~no Southern Oscillation (ENSO) and the Southern Annular Mode (SAM). Analysis of the ENSO and SAM on the Antarctic SIE show evidence that both are potentially important in controlling sea ice. Patterns in the ellipse parameters display results consistent with previous studies of the effect of ENSO and SAM on sea ice, but the significance of these forcings on sea ice remains an open question. Part of this research involved development of a method to measure the atmospheric parameters that affect sea ice in situ in Antarctica, known as SNOW-WEB. The aim of the SNOW-WEB project is to design and implement a network of weather stations that can communicate wirelessly to each other, allowing near real-time measurement of weather variables over very high spatial and temporal resolutions, in the order of kilometres and minutes. Measuring the wind velocity, temperature and pressure over such high resolutions allow small scale atmospheric phenomena to be analysed in terms of their effects on sea ice. The first deployment of the SNOW-WEB system was in January 2011 spanning the area between Scott Base and Windless Bight on Ross Island in Antarctica. One of the most important components of SNOW-WEB was its power supply system. A system was designed that would allow the SNOW-WEB nodes to operate continuously for over a week by a combination of lead acid batteries and a solar trickle charger. In addition, a research grade weather station was deployed as a reference and calibration point for the sensors on board each SNOW-WEB node. Due to the difficulties involved with Antarctic field work, the expectations for the performance of the SNOW-WEB were conservative, but the SNOWWEB exceeded these comfortably