Characterising groundwater use by vegetation using a surface energy balance model and satellite observations of land surface temperature

This study presents a novel ‘model-data’ approach to detect groundwater-dependent vegetation (GDV), through differences in modelled and observed land surface temperatures (LST) in space and time. Vegetation groundwater use is inferred where modelled LST exceeds observed LST by more than a threshold determined from consideration of systematic and random errors in model and observations. Modelled LST was derived from a surface energy balance model and LST observations were obtained from Terra-MODIS thermal imagery. The model-data approach, applied in the Condamine River Catchment, Queensland, Australia, identified GDV coincident to existing mapping. GDV were found to use groundwater up to 48% of the time and for as many as 56 consecutive days. Under driest of conditions, groundwater was estimated to contribute up to 0.2 mm h⁻¹ to total ET for GDV. The ability to both detect the location and water-use dynamics of GDV is a significant advancement on previous remote-sensing GDV methods.     

Eddy-advective effects on the temperature and wind speed of the sea surface in the Northwest Pacific Subtropical Countercurrent area from satellite observations

In this study, satellite microwave and altimeter data from 1998 to 2007 are used to quantify the eddy-induced meridional heat advection (EMHA) in the Northwest Pacific Subtropical Countercurrent area. Generally, from March to May, the robust EMHA is formed at the point where meridional currents of eddies cross a zonal front of climatological background sea surface temperature (SST). The EMHA shifts westwards with eddies and varies seasonally with the SST front. It warms (cools) the sea surface west of anticyclonic (cyclonic) eddies, inducing noticeable SST anomalies (SSTAs), which are westwardly phase shifted from the eddy-induced sea surface height anomalies by about 90°. Surface wind subsequently varies with the induced SSTAs: it blows faster (slower) over the warm (cold) SST regions than the surroundings. The spatial variations of SST and sea surface wind due to the EMHA shift westwards with eddy motion. These findings from satellite observations give us the possibility of studying the role of oceanic eddies in ocean–atmosphere interaction at the timescale of weather systems in an open ocean.     

Predicting ocean surface currents using numerical weather prediction model and Kohonen neural network: a northern Adriatic study

The paper documents a concept of ocean forecasting system for ocean surface currents based on self-organizing map (SOM) trained by high-resolution numerical weather prediction (NWP) model and high-frequency (HF) radar data. Wind and surface currents data from the northern Adriatic coastal area were used in a 6-month long training phase to obtain SOM patterns. Very high correlation between current and joined current and wind SOM patterns indicated the strong relationship between winds and currents and allowed for creation of a prediction system. Increasing SOM dimensions did not increase reliability of the forecasting system, being limited by the amount of the data used for training and achieving the lowest errors for 4 × 4 SOM matrix. As the HF radars and high-resolution NWP models are strongly expanding in coastal oceans, providing reliable and long-term datasets, the applicability of the proposed SOM-based forecasting system is expected to be high.

     

Modelling surface energy fluxes over maize using a two-source patch model and radiometric soil and canopy temperature observations

Models estimating surface energy fluxes over partial canopy cover with thermal remote sensing must account for significant differences between the radiometric temperatures and turbulent exchange rates associated with the soil and canopy components of the thermal pixel scene. Recent progress in separating soil and canopy temperatures from dual angle composite radiometric temperature measurements has encouraged the development of two-source (soil and canopy) approaches to estimating surface energy fluxes given observations of component soil and canopy temperatures. A Simplified Two-Source Energy Balance (STSEB) model has been developed using a “patch” treatment of the surface flux sources, which does not allow interaction between the soil and vegetation canopy components. A simple algorithm to predict the net radiation partitioning between the soil and vegetation is introduced as part of the STSEB patch modelling scheme. The feasibility of the STSEB approach under a full range in fractional vegetation cover conditions is explored using data collected over a maize (corn) crop in Beltsville Maryland, USA during the 2004 summer growing season. Measurements of soil and canopy component temperatures as well as the effective composite temperature were collected over the course of the growing season from crop emergence to cob development. Comparison with tower flux measurements yielded root-mean-square-difference values between 15 and 50 W m^− 2 for the retrieval of the net radiation, soil, sensible and latent heat fluxes. A detailed sensitivity analysis of the STSEB approach to typical uncertainties in the required inputs was also conducted indicating greatest model sensitivity to soil and canopy temperature uncertainties with relative errors reaching ∼ 30% in latent heat flux estimates. With algorithms proposed to infer component temperatures from bi-angular satellite observations, the STSEB model has the capability of being applied operationally.     

Effects of atmospheric water and surface wind on passive microwave retrievals of sea ice concentration: a simulation study

The atmospheric effects on the retrieval of sea ice concentration from passive microwave sensors were examined using simulated data typical for the Arctic summer. The simulation includes atmospheric contributions of cloud liquid water (CLW), water vapour (WV) and surface wind on the microwave signatures. A plane parallel radiative transfer model was used to compute brightness temperatures at SSM/I frequencies over surfaces that contained open water, first‐year (FY) ice, multi‐year (MY) ice and their combinations. Synthetic retrievals were performed using the NASA Team (NT) algorithm for the estimation of sea ice concentrations. Our results show that if the satellite sensor's field of view is filled with only FY ice, the retrieval is hardly affected by the atmospheric conditions because of the high contrast between emission signals from the FY ice surface and the atmosphere. Pure MY ice concentration is generally underestimated because of the low MY ice surface emissivity, which results in the enhancement of emission signals from the atmosphere. In marginal ice areas, the atmospheric and surface effects tend to degrade the accuracy at low sea ice concentrations. FY ice concentration is overestimated and MY ice concentration is underestimated in the presence of atmospheric water and surface wind at low ice concentration. Moreover, strong surface wind appears to be more important than atmospheric water in contributing to the retrieval errors of total ice concentrations over marginal ice zones.     

An algorithm for sea surface temperature estimation from ERS-1 ATSR using moisture dependent coefficients: a simulation study

A new algorithm for estimating sea surface temperature (SST) from ERS-1 ATSR data using moisture dependent coefficients has been proposed. The clear-column water vapour (WV) information required for selecting the appropriate coefficients has also been derived from ERS-1 ATSR data. The method has been tested with the simulated ATSR data by comparing the estimated SST with the true SST used for simulating the data. The comparison of SST estimation errors by present and standard methods, under different noise conditions, has indicated improvements by the present method over the standard method. The small errors anticipated in SST estimation due to the errors in WV estimation causing erroneous selection of coefficients have also been taken care of by making use of overlapping ranges of WV for moisture dependent SST estimation coefficients. The method could be used for operational SST from ATSR data.     

Identification of upwelling areas on sea surface temperature images using fuzzy clustering

Sixteen sea surface temperature (SST) images obtained over the coastal ocean of Portugal during the period September 1992-September 2003 were used aiming to identify automatically the areas covered by upwelling waters. Suitable high resolution colour scales were applied to the SST images in order to enhance the thermal patterns and easily identify the waters with a coastal upwelling origin. The automatic identification of the areas covered by upwelling waters was developed by the authors in a previous work, through the application of fuzzy clustering and validation indexes, and here is explored as an oceanographic application to the Portuguese coastal upwelling. The fuzzy c-means (FCM) algorithm showed to be able to find partitions that closely defined the upwelling areas and the visualization of the fuzzy c-partitions was achieved through the application of a colour scale. The Xie-Beni validation index was used to select the c-partition that best represented the stage of the upwelling event and showed an agreement with the oceanographic interpretation in 10 of the 14 SST segmented images used in this work. Two SST images without upwelling were also used in order to check the response of the algorithm to the absence of the phenomena. The computation of the matching rate between a c-partition and the two areas split by the hand-contoured upwelling boundary also allowed the evaluation of how closely the obtained segmentation reproduced the shape of the areas covered by upwelling waters. This method successfully identified the upwelling boundary regions in 10 of the 14 SST images. The values obtained for the matching rate were higher than 0.77, thus indicating the good quality of the fuzzy partitions. The segmented images with 3 or 4 clusters were the most suitable ones to reproduce the areas covered by upwelling waters, but it was also shown that, for some cases, the upwelling areas could be reasonably well reproduced by the FCM 2-partition images. While in the latter, the area covered with upwelling waters was coincident with the first cluster, in the former, the segmented image showed two clusters within the upwelling area: the first cluster coincided with the area occupied by the most recently upwelled waters near the coast, while the second cluster was coincident with the area occupied by the “older” upwelling waters with its extensions offshore, the so-called cold filaments. The FCM algorithm revealed to be a promising technique in the automatic identification of upwelling areas on SST images.     

A comparative assessment of IRS-P4 (MSMR)-derived sea surface temperature and sea surface wind speed over the north Indian Ocean

Indian Remote Sensing Satellite (IRS-P4) multi-frequency scanning microwave radiometer (MSMR) provides geophysical parameters like sea surface temperature (SST), sea surface wind speed (SSWS), integrated water vapour (IWV) and cloud liquid water (CLW). The retrieval procedure of these parameters given by Gohil et al. (2000 Gohil, B.S., Mathur, A.K. and Varma, A.K. Geophysical parameter retrieval over global oceans from IRS-P4 (MSMR). Preprints, Fifth Pacific Ocean Remote Sensing Conference. December5–82000, Goa. pp.207–211. Goa, , India: National Institute of Oceanography.  [Google Scholar], Geophysical parameter retrieval over global oceans from IRS-P4 (MSMR). In Preprints, Fifth Pacific Ocean Remote Sensing Conference, 5–8 December 2000, Goa, India (Goa: National Institute of Oceanography), pp. 207–211) was summarized by Sharma et al. (2002 Sharma, R., Babu, K.N., Mathur, A.K. and Ali, M.M. 2002. Identification of large scale atmospheric and oceanic features from IRS-P4 multifrequency scanning microwave radiometer: preliminary results. Journal of Atmospheric and Oceanic Technology, 19: 1127–1134. [Crossref], [Web of Science ®] , [Google Scholar], Identification of large scale atmospheric and oceanic features from IRS-P4 multifrequency scanning microwave radiometer: preliminary results. Journal of Atmospheric and Oceanic Technology, 19, pp. 1127–1134) and Jena (2007 Jena, B. 2007. Studies on the retrieval, validation and applications of geophysical parameters from IRS-P4 (MSMR) data, Orissa: PhD thesis, Berhampur University.  [Google Scholar], Studies on the retrieval, validation and applications of geophysical parameters from IRS-P4 (MSMR) data. PhD thesis, Berhampur University, Orissa). Demonstration of self-consistency of these parameters has primary scientific importance. This article deals with the validation of MSMR geophysical parameters such as SST and SSWS with in situ observations (buoy data) over the north Indian Ocean during 2000. Result shows that the MSMR-derived SST and SSWS can be utilized for several applications because of their reasonable accuracy and coverage even under cloudy condition.     

Estimation of snow water equivalent over first-year sea ice using AMSR-E and surface observations

A SWE retrieval algorithm developed in-situ using passive microwave surface based radiometer data is applied to the Advanced Microwave Scanning Radiometer for Earth Observation System (AMSR-E). Snow water equivalent is predicted from two pixels located in Canadian Arctic Shelf Exchange Study (CASES) overwintering study area in Franklin Bay, N.W.T., Canada. Results show that the satellite SWE predictions are statistically valid with measured in-situ snow thickness data in both smooth and rough ice environments where predicted values range from 15 to 25 mm. Stronger correlation between measured and predicted data is found over smooth ice with R² value of 0.75 and 0.73 for both pixels respectively. Furthermore, a qualitative study of sea ice roughness using both passive and active microwave satellite data shows that the two pixels are rougher than the surrounding areas, but the SWE predictions do not seem to be affected significantly.     

Temporal and spatial variability of satellite sea surface temperature and ocean colour in the Japan/East Sea

Temporal and spatial variability of Sea Surface Temperature (SST) and ocean colour in the Japan/East Sea (JES) are examined during winter and spring using satellite data from Advanced Very High Resolution Radiometer (AVHRR) and Sea-viewing Wide Field of view Sensor (SeaWiFS). The timing of the spring phytoplankton bloom and the locations of the chlorophyll fronts are related to changes in the thermal fields and the locations of the temperature fronts. Daily images of SST and chlorophyll concentration show both differences and similarities of bio-optical and thermal front location, depending on region and season. Four sub-regions in the JES were defined and SST and chlorophyll values were extracted from weekly and monthly composite images to derive summary statistics. SST at the Subpolar Front increased about 7°C over a 1.5-month period from late April to early June in 1999. During this same period, elevated chlorophyll values near the Korean coast and in the southern basin decreased sharply as the phytoplankton bloom that first developed in the southern basin progressed to the front and northward. The SST/chlorophyll relationship is complex and seasonal. Near the Subpolar Front, SST and chlorophyll were positively related in April. In May, highest chlorophyll values corresponded to mixing regimes (such as areas of convergence and divergence at the edges of meanders) and, by June, SST and chlorophyll near the front were inversely related.     

Mapping sea surface temperature from aircraft using a multi-angle technique: An experiment over the Orbetello lagoon

Airborne scanner data collected in the thermal band over the Orbetello lagoon on 14 May 1993 were used to draw out a water surface temperature map. The geometric correction method was chosen after a quantitative comparison between the classic polynomial method and a technique based on matching Delaunay triangles. Sensor raw data were calibrated and striping noise was removed. Finally, a new atmospheric correction method was developed. The method consists of extracting, from the superimposed portion of two images, couples of measurements relative to the same resolution cell observed under two different angles. From these measurements we retrieved the atmospheric transmittance and integrated emitted radiance using an iterative algorithm capable of reducing the effects of measurement errors and taking into account the nonlinearity of the derived equations. The variation of surface emissivity with observation angle is also taken into account in the equations. In order to apply the method, the unknown covariance matrix of the parameters to be retrieved was valued. To this aim, we generated many statistical realizations of such atmospheric radiative quantities using the Lowtran 7 code applied to a large number of radiosoundings. The obtained atmospheric parameters were then used to correct the entire set of collected images and to produce a temperature map of the lagoon.     

Remote sensing of the Indo-Pacific region: ocean colour,sea level,winds and sea surface temperatures

The 1997–1998 ENSO (El Niño-Southern Oscillation) was not only the largest event of the century but also the most comprehensively observed. Satellite data were employed for ocean colour, sea level, winds, sea surface temperature (SST), and outgoing longwave radiation (OLR) were used to describe the response of the surface marine ecosystem associated with the ENSO event. Some of the large-scale anomalies in ocean colour include elevated biological activity to the north of the Equator in the Pacific coincident with lower sea levels associated with the classic ENSO-horseshoe pattern ecosystem response to the anomalous upwelling in the eastern Indian Ocean caused by the 1997–1998 dipole event, and the dramatic eastward propagating feature in the Equatorial Pacific in response to the La Niña dynamics. Ocean general circulation model (OGCM) experiments show that capturing the high-frequency wind changes is crucial for simulating the La Niña and the coupled biological–physical model (OBGCM) runs clearly show that higher frequency winds are also important for capturing the mean upwelling and nutrient supply into the euphotic zone. Thus, the QuickSCAT winds are expected to play a major role in ecosystem modelling in the future. This study shows the utility of satellite data for understanding not only ocean circulation but also the coupled ecosystem variability. Morcover, it is also shown that spatio-temporal resolution of the satellite winds will directly affect the accuracy of oceanic and ecosystem simulations.     

Estimates of sea surface nitrate concentrations from sea surface temperature and chlorophyll concentration in upwelling areas: A case study for the Benguela system

The knowledge of nitrate fields at global or regional scales in the ocean is fundamental for the study of oceanic biogeochemical processes, particularly those linked to new primary production. The estimate of nitrate concentrations from space is generally based on empirical inverse relationships between sea surface temperature (SST) and nitrate concentrations. These relationships, however, are often highly variable spatially and temporally, and hardly applicable to large areas (i.e., larger than a few degrees in latitude). In this paper we propose a new approach specifically developed for areas influenced by upwelling processes. It relates the nitrate concentration to the difference between SST and the estimated temperature of the upwelled water (variable with latitude and season), δT, which is an indicator of the time elapsed since upwelling. This approach is tested for the Benguela upwelling system, and algorithms are developed using in situ data provided by the World Ocean Database 2005 of the NOAA-NESDIS-National Oceanographic Data Center. The results reveal a significant improvement compared to the NO₃-SST relationships, and a single algorithm can be applied to the whole upwelling area (15 to 35°S). Further improvement is gained by coupling this approach with a method that derives sea surface nitrate concentrations from SST and surface chlorophyll a concentration using multiple regression analyses, as proposed by Goes et al. [Goes, Saino, Oaku, Jiang, (1999). Method for estimating sea surface nitrate concentrations from remotely sensed SST and chlorophyll a: A case study for the North Pacific Ocean using OCTS/ADEOS data. IEEE Transactions on Geoscience and Remote Sensing, 37, no. 3 II, 1633-1644].     

Dynamic aspects study of surface temperature firom remotely-sensed data using advanced thermal inertia model

The satellite over-pass time ground surface temperature can be determined using split-window methods. The diurnal ground temperature is derived from the advanced thermal inertia model as a solution of the heat diffusion equation with a constant diffusivity under periodic forcing on the ground surface in terms of temperature. The model was tested by applying it to NOAA AVHRR data for France. The results indicate that the advanced thermal inertia model can be used to predict the diurnal ground surface temperature quasi-operationally with any two sets of over-pass satellite data and it is better to use any two daytime sets of over-pass satellite data than one night-time set and one day time set of over-pass satellite data, especially in vegetated areas. The model can be used to interpolate the surface temperature values between two over-pass time satellite measurements.     

Split-window and multi-angle methods of sea surface temperature determination: An analysis

A physical model of split-window and multi-angle algorithms for the retrieval of sea surface temperature has been obtained. It is shown that optical thickness in the thermal infrared window region is approximately represented by a separable function of wavelength and atmospheric variables which greatly simplify the radiative transfer model. In modelling the temperature retrieval algorithms we exploit the correlation that exists between the sea surface temperature and the average temperature of the atmosphere. It is shown that, to the extent this correlation is maintained, there is a simple relationship between surface temperature and the brightness temperatures measured in two split-window or multi-angle channels. The different atmospheric conditions which cause spread in this relationship are briefly discussed. It is shown that measurements at three optical thicknesses instead of two as in the case of the split-window method improve the SST retrieval accuracy since it can take care of non-equilibrium conditions such as inversions, surface instabilities, etc., prevailing in the atmosphere.     

The polarized emissivity of a wind-roughened sea surface: A Monte Carlo model

We use a Monte Carlo ray-tracing model to compute the thermal-infrared emissivity of a wind-roughened sea surface. The model includes the effects of both shadowing and the reflected component of surface emission. By using Stokes vectors to quantify the radiation along a given ray path, we compute the effects of polarization as well. We separate the direct emission from surface reflections to show how each affects the nature of the emitted field. The reflected component is an important part of the radiative transfer and affects nearly 10% of the ray paths at emission angles between 60° and 80° at wind speeds ?5 m/s, increasing the effective emissivity by as much as 0.03. The modeled emissivities agree nicely with recent sea surface emissivity measurements. We also compare the Monte Carlo results to a recently published analytic model and show that the two vary somewhat due to differences in the amount of the reflected component included in the calculations. Surface roughness has a large effect on the polarization between 60° and 90° but less so at smaller angles. Including the reflected component has a small but noticeable effect which actually enhances the degree of polarization at intermediate angles.     

Evaluation of ASTER and MODIS land surface temperature and emissivity products using long-term surface longwave radiation observations at SURFRAD sites

Land surface temperature (LST) and emissivity are key parameters in estimating the land surface radiation budget, a major controlling factor of global climate and environmental change. In this study, Terra Advanced Spaceborne Thermal Emission Reflection Radiometer (ASTER) and Aqua MODerate resolution Imaging Spectroradiometer (MODIS) Collection 5 LST and emissivity products are evaluated using long-term ground-based longwave radiation observations collected at six Surface Radiation Budget Network (SURFRAD) sites from 2000 to 2007. LSTs at a spatial resolution of 90 m from 197 ASTER images during 2000-2007 are directly compared to ground observations at the six SURFRAD sites. For nighttime data, ASTER LST has an average bias of 0.1 °C and the average bias is 0.3 °C during daytime. Aqua MODIS LST at 1 km resolution during nighttime retrieved from a split-window algorithm is evaluated from 2002 to 2007. MODIS LST has an average bias of − 0.2 °C. LST heterogeneity (defined as the Standard Deviation, STD, of ASTER LSTs in 1 × 1 km² region, 11 × 11 pixel in total) and instrument calibration error of pyrgeometer are key factors impacting the ASTER and MODIS LST evaluation using ground-based radiation measurements. The heterogeneity of nighttime ASTER LST is 1.2 °C, which accounts for 71% of the STD of the comparison, while the heterogeneity of the daytime LST is 2.4 °C, which accounts for 60% of the STD. Collection 5 broadband emissivity is 0.01 larger than that of MODIS Collection 4 products and ASTER emissivity. It is essential to filter out the abnormal low values of ASTER daily emissivity data in summer time before its application.     

Determination of sea surface temperature at large observation angles using an angular and emissivity-dependent split-window equation

This paper proposes an angular and emissivity-dependent split-window equation that permits the determination of the sea surface temperature (SST) to a reasonable level of accuracy for any observation angle, including large viewing angles at the image edges of satellite sensors with wide swaths. This is the case of the MODIS radiometer both on EOS Terra/Aqua platforms, with observation angles of up to 65° at the surface, for which the split-window equation has been developed in this study. The algorithm takes into account the angular dependence of both the atmospheric correction (due to the increase of the atmospheric optical path with angle) and the emissivity correction (since sea surface emissivity (SSE) decreases with observation angle). Angular-dependent coefficients have been estimated for the atmospheric terms, and also an explicit dependence on the SSE has been included in the algorithm, as this parameter has values different to a blackbody surface for off-nadir angles, the SSEs also being dependent on surface wind speed. The proposed algorithm requires as input data at-sensor brightness temperatures for the split-window bands (31 and 32 of MODIS), the observation angle at each pixel, an estimate of the water vapor content (which is provided by the MODIS MOD07/MYD07 products) and accurate SSE values for both channels. The preliminary results show a good agreement between SSTs estimated by the proposed equation for off-nadir viewings of MODIS-Terra images and in situ SST measurements, with a root-mean square error (RMSE) of about ± 0.3 K, for which the MODIS SST product gives an RMSE larger than ± 0.7 K.     

Joint segmentation of wind speed and direction using a hierarchical model

The problem of detecting changes in wind speed and direction is considered. Bayesian priors, with various degrees of certainty, are used to represent relationships between the two time series. Segmentation is then conducted using a hierarchical Bayesian model that accounts for correlations between the wind speed and direction. A Gibbs sampling strategy overcomes the computational complexity of the hierarchical model and is used to estimate the unknown parameters and hyperparameters. Extensions to other statistical models are also discussed. These models allow us to study other joint segmentation problems including segmentation of wave amplitude and direction. The performance of the proposed algorithms is illustrated with results obtained with synthetic and real data.     

Joint segmentation of wind speed and direction using a hierarchical model

The problem of detecting changes in wind speed and direction is considered. Bayesian priors, with various degrees of certainty, are used to represent relationships between the two time series. Segmentation is then conducted using a hierarchical Bayesian model that accounts for correlations between the wind speed and direction. A Gibbs sampling strategy overcomes the computational complexity of the hierarchical model and is used to estimate the unknown parameters and hyperparameters. Extensions to other statistical models are also discussed. These models allow us to study other joint segmentation problems including segmentation of wave amplitude and direction. The performance of the proposed algorithms is illustrated with results obtained with synthetic and real data.