Physics-based modelling for remote sensing of snow

Remote sensing is the only practicable means to observe snow at large scales. Measurements from passive microwave instruments have been used to derive snow climatology since the late 1970’s, but the algorithms used were limited by the computational power of the era. Simplifications such as the assumption of constant snow properties enabled snow mass to be retrieved from the microwave measurements, but large errors arise from those assumptions, which are still used today. A better approach is to perform retrievals within a data assimilation framework, where a physically-based model of the snow properties can be used to produce the best estimate of the snow cover, in conjunction with multi-sensor observations such as the grain size, surface temperature, and microwave radiation. We have developed an existing snow model, SNOBAL, to incorporate mass and energy transfer of the soil, and to simulate the growth of the snow grains. An evaluation of this model is presented and techniques for the development of new retrieval systems are discussed.

Integration of remote sensing and GIS for modelling flash floods in Wadi Hudain catchment, Egypt

This paper presents a new approach to modelling flash floods in dryland catchments by integrating remote sensing and digital elevation model (DEM) data in a geographical information system (GIS). The spectral reflectance of channels affected by recent flash floods exhibit a marked increase, due to the deposition of fine sediments in these channels as the flood recedes. This allows the parts of a catchment that have been affected by a recent flood event to be discriminated from unaffected parts, using a time series of Landsat images. Using images of the Wadi Hudain catchment in southern Egypt, the hillslope areas contributing flow were inferred for different flood events. The SRTM3 DEM was used to derive flow direction, flow length, active channel cross-sectional areas and slope. The Manning Equation was used to estimate the channel flow velocities, and hence the time-area zones of the catchment. A channel reach that was active during a 1985 runoff event, that does not receive any tributary flow, was used to estimate a transmission loss rate of 7·5 mm h−1, given the maximum peak discharge estimate. Runoff patterns resulting from different flood events are quite variable; however the southern part of the catchment appears to have experienced more floods during the period of study (1984–2000), perhaps because the bedrock hillslopes in this area are more effective at runoff production than other parts of the catchment which are underlain by unconsolidated Quaternary sands and gravels. Due to high transmission loss, runoff generated within the upper reaches is rarely delivered to the alluvial fan and Shalateen city situated at the catchment outlet. The synthetic GIS-based time area zones, on their own, cannot be relied on to model the hydrographs reliably; physical parameters, such as rainfall intensity, distribution, and transmission loss, must also be considered.

Interchange reconnection: remote sensing of solar signature and role in heliospheric magnetic flux budget

Interchange reconnection at the Sun, that is, reconnection between a doubly-connected field loop and singly-connected or open field line that extends to infinity, has important implications for the heliospheric magnetic flux budget. Recent work on the topic is reviewed, with emphasis on two aspects. The first is a possible heliospheric signature of interchange reconnection at the coronal hole boundary, where open fields meet closed loops. The second aspect concerns the means by which the heliospheric magnetic field strength reached record-lows during the recent solar minimum period. A new implication of this work is that interchange reconnection may be responsible for the puzzling, occasional coincidence of the heliospheric current sheet and the interface between fast and slow flow in the solar wind.

Characterization of a highly biodiverse floodplain meadow using hyperspectral remote sensing within a plant functional trait framework

We assessed the potential for using optical functional types as effective markers to monitor changes in vegetation in floodplain meadows associated with changes in their local environment. Floodplain meadows are challenging ecosystems for monitoring and conservation because of their highly biodiverse nature. Our aim was to understand and explain spectral differences among key members of floodplain meadows and also characterize differences with respect to functional traits. The study was conducted on a typical floodplain meadow in UK (MG4-type, mesotrophic grassland type 4, according to British National Vegetation Classification). We compared two approaches to characterize floodplain communities using field spectroscopy. The first approach was sub-community based, in which we collected spectral signatures for species groupings indicating two distinct eco-hydrological conditions (dry and wet soil indicator species). The other approach was “species-specific”, in which we focused on the spectral reflectance of three key species found on the meadow. One herb species is a typical member of the MG4 floodplain meadow community, while the other two species, sedge and rush, represent wetland vegetation. We also monitored vegetation biophysical and functional properties as well as soil nutrients and ground water levels. We found that the vegetation classes representing meadow sub-communities could not be spectrally distinguished from each other, whereas the individual herb species was found to have a distinctly different spectral signature from the sedge and rush species. The spectral differences between these three species could be explained by their observed differences in plant biophysical parameters, as corroborated through radiative transfer model simulations. These parameters, such as leaf area index, leaf dry matter content, leaf water content, and specific leaf area, along with other functional parameters, such as maximum carboxylation capacity and leaf nitrogen content, also helped explain the species’ differences in functional dynamics. Groundwater level and soil nitrogen availability, which are important factors governing plant nutrient status, were also found to be significantly different for the herb/wetland species’ locations. The study concludes that spectrally distinguishable species, typical for a highly biodiverse site such as a floodplain meadow, could potentially be used as target species to monitor vegetation dynamics under changing environmental conditions.

Toward vicarious calibration of microwave remote-sensing satellites in arid environments

The Soil Moisture and Ocean Salinity (SMOS) satellite marks the commencement of dedicated global surface soil moisture missions, and the first mission to make passive microwave observations at L-band. On-orbit calibration is an essential part of the instrument calibration strategy, but on-board beam-filling targets are not practical for such large apertures. Therefore, areas to serve as vicarious calibration targets need to be identified. Such sites can only be identified through field experiments including both in situ and airborne measurements. For this purpose, two field experiments were performed in central Australia. Three areas are studied as follows: 1) Lake Eyre, a typically dry salt lake; 2) Wirrangula Hill, with sparse vegetation and a dense cover of surface rock; and 3) Simpson Desert, characterized by dry sand dunes. Of those sites, only Wirrangula Hill and the Simpson Desert are found to be potentially suitable targets, as they have a spatial variation in brightness temperatures of <4 K under normal conditions. However, some limitations are observed for the Simpson Desert, where a bias of 15 K in vertical and 20 K in horizontal polarization exists between model predictions and observations, suggesting a lack of understanding of the underlying physics in this environment. Subsequent comparison with model predictions indicates a SMOS bias of 5 K in vertical and 11 K in horizontal polarization, and an unbiased root mean square difference of 10 K in both polarizations for Wirrangula Hill. Most importantly, the SMOS observations show that the brightness temperature evolution is dominated by regular seasonal patterns and that precipitation events have only little impact.

Sequential variations of phytoplankton growth and mortality in an NPZ model: a remote-sensing-based assessment

Radiometric data in the visible domain acquired by satellite remote sensing have proven to be powerful for monitoring the states of the ocean, both physical and biological. With the help of these data it is possible to understand certain variations in biological responses of marine phytoplankton on ecological time scales. Here, we implement a sequential data-assimilation technique to estimate from a conventional nutrient–phytoplankton–zooplankton (NPZ) model the time variations of observed and unobserved variables. In addition, we estimate the time evolution of two biological parameters, namely, the specific growth rate and specific mortality of phytoplankton. Our study demonstrates that: (i) the series of time-varying estimates of specific growth rate obtained by sequential data assimilation improves the fitting of the NPZ model to the satellite-derived time series: the model trajectories are closer to the observations than those obtained by implementing static values of the parameter; (ii) the estimates of unobserved variables, i.e., nutrient and zooplankton, obtained from an NPZ model by implementation of a pre-defined parameter evolution can be different from those obtained on applying the sequences of parameters estimated by assimilation; and (iii) the maximum estimated specific growth rate of phytoplankton in the study area is more sensitive to the sea-surface temperature than would be predicted by temperature-dependent functions reported previously. The overall results of the study are potentially useful for enhancing our understanding of the biological response of phytoplankton in a changing environment.

Modelling the time-evolution of phytoplankton size spectra from satellite remote sensing

A dynamic size-structured model is developed for phytoplankton and nutrients in the oceanic mixed layer and applied to extract phytoplankton biomass at discrete size fractions from remotely sensed, ocean-colour data. General relationships between cell size and biophysical processes (such as sinking, grazing, and primary production) of phytoplankton were included in the model through a bottom–up approach. Time-dependent, mixed-layer depth was used as a forcing variable, and a sequential data-assimilation scheme was implemented to derive model trajectories. From a given time-series, the method produces estimates of size-structured biomass at every observation, so estimates seasonal succession of individual phytoplankton size, derived here from remote sensing for the first time. From these estimates, normalized phytoplankton biomass size spectra over a period of 9 years were calculated for one location in the North Atlantic. Further analysis demonstrated that strong relationships exist between the seasonal trends of the estimated size spectra and the mixed-layer depth, nutrient biomass, and total chlorophyll. The results contain useful information on the time-dependent biomass flux in the pelagic ecosystem.

The phenology of phytoplankton blooms: ecosystems indicators from remote sensing

Remote sensing offers many advantages in the development of ecosystem indicators for the pelagic zone of the ocean. Particularly suitable in this context are the indicators arising from time series that can be constructed from remotely sensed data. For example, using ocean-colour radiometry, the phenology of phytoplankton blooms can be assessed. Metrics defined in this way show promise as informative indicators for the entire pelagic ecosystem. A simple phytoplankton–substrate model, with forcing dependent on latitude and day number is used to explore the qualitative features of bloom phenology for comparison with the results observed in a suite of 10-year time series of chlorophyll concentration, as assessed by remote sensing, from the Northwest Atlantic Ocean. The model reveals features of the dynamics that might otherwise have been overlooked in evaluation of the observational data.

In situ and remote-sensing measurements of the mean microphysical and optical properties of industrial pollution aerosol during ADRIEX

We present a summary of the principal physical and optical properties of aerosol particles using the FAAM BAE-146 instrumented aircraft during ADRIEX between 27 August and 6 September 2004, augmented by sunphotometer, lidar and satellite retrievals. Observations of anthropogenic aerosol, principally from industrial sources, were concentrated over the northern Adriatic Sea and over the Po Valley close to the aerosol sources. An additional flight was also carried out over the Black Sea to compare east and west European pollution. Measurements show the single-scattering albedo of dry aerosol particles to vary considerably between 0.89 and 0.97 at a wavelength of 0.55 μm, with a campaign mean within the polluted lower free troposphere of 0.92. Although aerosol concentrations varied significantly from day to day and during individual days, the shape of the aerosol size distribution was relatively consistent through the experiment, with no detectable change observed over land and over sea. There is evidence to suggest that the pollution aerosol within the marine boundary layer was younger than that in the elevated layer. Trends in the aerosol volume distribution show consistency with multiple-site AERONET radiometric observations. The aerosol optical depths derived from aircraft measurements show a consistent bias to lower values than both the AERONET and lidar ground-based radiometric observations, differences which can be explained by local variations in the aerosol column loading and by some aircraft instrumental artefacts. Retrievals of the aerosol optical depth and fine-mode (<0.5 μm radius) fraction contribution to the optical depth using MODIS data from the Terra and Aqua satellites show a reasonable level of agreement with the AERONET and aircraft measurements.

Thermal remote sensing of sea surface temperature

Sea surface temperature has been an important application of remote sensing from space for three decades. This chapter first describes well-established methods that have delivered valuable routine observations of sea surface temperature for meteorology and oceanography. Increasingly demanding requirements, often related to climate science, have highlighted some limitations of these ap-proaches. Practitioners have had to revisit techniques of estimation, of characterising uncertainty, and of validating observations—and even to reconsider the meaning(s) of “sea surface temperature”. The current understanding of these issues is reviewed, drawing attention to ongoing questions. Lastly, the prospect for thermal remote sens-ing of sea surface temperature over coming years is discussed.

Probabilistic downscaling of remote sensing data with applications for multi-scale biogeochemical flux modeling

Upscaling ecological information to larger scales in space and downscaling remote sensing observations or model simulations to finer scales remain grand challenges in Earth system science. Downscaling often involves inferring subgrid information from coarse-scale data, and such ill-posed problems are classically addressed using regularization. Here, we apply two-dimensional Tikhonov Regularization (2DTR) to simulate subgrid surface patterns for ecological applications. Specifically, we test the ability of 2DTR to simulate the spatial statistics of high-resolution (4 m) remote sensing observations of the normalized difference vegetation index (NDVI) in a tundra landscape. We find that the 2DTR approach as applied here can capture the major mode of spatial variability of the high-resolution information, but not multiple modes of spatial variability, and that the Lagrange multiplier (γ) used to impose the condition of smoothness across space is related to the range of the experimental semivariogram. We used observed and 2DTR-simulated maps of NDVI to estimate landscape-level leaf area index (LAI) and gross primary productivity (GPP). NDVI maps simulated using a γ value that approximates the range of observed NDVI result in a landscape-level GPP estimate that differs by ca 2% from those created using observed NDVI. Following findings that GPP per unit LAI is lower near vegetation patch edges, we simulated vegetation patch edges using multiple approaches and found that simulated GPP declined by up to 12% as a result. 2DTR can generate random landscapes rapidly and can be applied to disaggregate ecological information and compare of spatial observations against simulated landscapes.

Global climatology of surface water temperatures of large lakes by remote sensing

Lake surface water temperatures (LSWTs) of 246 globally distributed large lakes were derived from Along-Track Scanning Radiometers (ATSR) for the period 1991–2011. The climatological cycles of mean LSWT derived from these data quantify on a global scale the responses of large lakes' surface temperatures to the annual cycle of forcing by solar radiation and the ambient meteorological conditions. LSWT cycles reflect the twice annual peak in net solar radiation for lakes between 1°S to 12°N. For lakes without a lake-mean seasonal ice cover, LSWT extremes exceed air temperatures by 0.5–1.7 °C for maximum and 0.7–1.9 °C for minimum temperature. The summer maximum LSWTs of lakes from 25°S to 35°N show a linear decrease with increasing altitude; −3.76 ± 0.17 °C km−1 (inline image = 0.95), marginally lower than the corresponding air temperature decrease with altitude −4.15 ± 0.24 °C km−1 (inline image = 0.95). Lake altitude of tropical lakes account for 0.78–0.83 (inline image) of the variation in the March to June LSWT–air temperature differences, with differences decreasing by 1.9 °C as the altitude increases from 500 to 1800 m above sea level (a.s.l.) We define an ‘open water phase’ as the length of time the lake-mean LSWT remains above 4 °C. There is a strong global correlation between the start and end of the lake-mean open water phase and the spring and fall 0 °C air temperature transition days, (inline image = 0.74 and 0.80, respectively), allowing for a good estimation of timing and length of the open water phase of lakes without LSWT observations. Lake depth, lake altitude and distance from coast further explain some of the inter-lake variation in the start and end of the open water phase.

Datasets related to in-land water for limnology and remote sensing applications: distance-to-land, distance-to-water, water-body identifier and lake-centre co-ordinates

Datasets containing information to locate and identify water bodies have been generated from data locating static-water-bodies with resolution of about 300 m (1/360 deg) recently released by the Land Cover Climate Change Initiative (LC CCI) of the European Space Agency. The LC CCI water-bodies dataset has been obtained from multi-temporal metrics based on time series of the backscattered intensity recorded by ASAR on Envisat between 2005 and 2010. The new derived datasets provide coherently: distance to land, distance to water, water-body identifiers and lake-centre locations. The water-body identifier dataset locates the water bodies assigning the identifiers of the Global Lakes and Wetlands Database (GLWD), and lake centres are defined for in-land waters for which GLWD IDs were determined. The new datasets therefore link recent lake/reservoir/wetlands extent to the GLWD, together with a set of coordinates which locates unambiguously the water bodies in the database. Information on distance-to-land for each water cell and the distance-to-water for each land cell has many potential applications in remote sensing, where the applicability of geophysical retrieval algorithms may be affected by the presence of water or land within a satellite field of view (image pixel). During the generation and validation of the datasets some limitations of the GLWD database and of the LC CCI water-bodies mask have been found. Some examples of the inaccuracies/limitations are presented and discussed. Temporal change in water-body extent is common. Future versions of the LC CCI dataset are planned to represent temporal variation, and this will permit these derived datasets to be updated.