Use of AVHRR-derived surface temperatures in evaluating a land–atmosphere model

The vertical transports of heat and moisture at the Earth's surface provide a critical linkage between meteorological, hydrological and ecological processes. Unfortunately, estimates of energy and water fluxes by land surface–atmosphere models are difficult to validate. This is due to the fine spatial and temporal resolution of the models, as well as a lack of widespread observations of these fluxes and other components of the surface energy budget. However, modelled fluxes of energy and water are often based largely on accurate determination of the modelled soil surface temperature. Land surface temperatures (LSTs) derived from the Advanced Very High Resolution Radiometer (AVHRR) onboard polar-orbiting satellites can be determined over large spatial extents a few times per day. They provide a means by which modelled surface temperatures, and thus modelled fluxes, can be evaluated. In this paper, two versions of the Simulator for Hydrology and Energy Exchange at the Land Surface (SHEELS) model are evaluated using AVHRR-derived LSTs. Model vegetation cover is held constant in the first version, whereas Normalized Difference Vegetation Index (NDVI) data are used to temporally modify vegetation cover in the second version. Inclusion of the NDVI data is found to decrease the bias between modelled and satellite-derived LSTs by 0.5?K. However, even with this improvement in vegetation parametrization, bias remains large (3.7?K).     

An analysis of GOES and NOAA derived land surface temperatures estimated over a boreal forest

Precise estimates of land surface temperature (LST) are important for land monitoring investigations. This study compares LST values calculated using different satellite platforms (Geostationary Operational Environmental Satellite-Imager and National Oceanographic and Atmospheric Administration-Advanced Very High Resolution Radiometer) and five different split window algorithms. The analysis includes (1) a fitting test with the reference dataset, (2) a comparison of differences between algorithms, and (3) an inter-sensor comparison. Considering the hypothesis of the Temperature/Vegetation Index (TVX) technique, the reference dataset was made with air temperature measured over dense canopy having maximum Normalized Difference Vegetation Index (NDVI). The first and second analyses show that algorithms used by Becker and Li and Ulivieri et al. have smaller estimation errors (less than 2.3 K) than the other algorithms, for example, best-fit linear regression. Although these algorithms show a good agreement in the paired algorithms analysis, the final analysis presents a considerable difference in the root mean square error between Imager and AVHRR (1.7 K for the Ulivieri et al. algorithm and 5.3 K for the Becker and Li algorithm). Finally we considered that the Ulivieri et al. method is more stable for both satellites.     

The evolution of precipitable water and precipitation over the Island of Tahiti from hourly to seasonal periods

The Island of Tahiti (French Polynesia) has a complex meteorological context governed by the South Pacific convergence zone on a large scale and the topography of a high volcanic island on the orographic scale. The island is subject to heavy rainfall (up to 8000 mm year^–1 in some areas), mainly during the rainy season (November to April), generating significant geo-morphological changes and property damage each year. To better understand the underlying complex mechanisms leading to precipitation over a broad range of timescales (from hourly to seasonal), we have analysed eight years of data (2001–2008) coming from five sources: a radiometer, radiosoundings, a GPS, Era-Interim reanalysis, and two rain gauges, all located close to or inside the Matatia valley, a small typical Tahitian valley. In particular, we have decomposed the precipitable water distribution into two statistical distributions corresponding to the dry and wet seasons. The time evolution of precipitable water in the dry season is characterized by a log-normal distribution, while the precipitable water time evolution in the wet season is characterized by a reverse log-normal distribution. The bimodality of the probability distributions describing the dynamical processes involved in the Tahiti climate is confirmed by the study of the diurnal evolution in absolute humidity, precipitable water, and precipitation.     

Classification of radar imagery over boreal regions for methane exchange studies

Airborne synthetic aperture radar (SAR) data acquired over Alaska are used to investigate the ability of SAR to distinguish between land cover classes of differing methane exchange rates. Land cover within the study area is divided into four classes: forest, bog, water, and fen, with fen having the highest methane emission. Accurate classification is achieved using both statistical and neural network techniques applied to fully polarimetric L- and C-band data. Similar classification accuracies are also obtained using non-polarimetric subsets of the data, analogous to data that would be available by combining SAR observations from ERS-1/2, JERS-I (Fuyo-1), and RADARSAT. Accurate classification of fens, however, is possible only when the non-polarimetric subset includes L-band data     

Calculating flow into coastal lakes from water level measurements

Some coastal lakes in New South Wales have narrow channels to the ocean. Runoff from the catchment elevates water levels within the lake because of the constricted nature of the entrance. Freshwater inflow affects many ecological processes in these lakes. Often freshwater inflow comes from multiple sources and is difficult to measure directly. Water level is easy to measure and is commonly measured for flood management purposes. A simple dynamical model can represent features of water level in such lakes to a good approximation. This is demonstrated by comparing model output with measurements of tidal oscillations and tidal pumping within the lake. Residuals of modelled water level relative to measurements within the lake are statistically analysed to estimate freshwater inflow to the lake. Comparisons of freshwater inflow with other estimates of catchment runoff are favourable.     

Determination of phenological dates in boreal regions using normalized difference water index

Monitoring and understanding plant phenology are important in the context of studies of terrestrial productivity and global change. Vegetation phenology, such as dates of onsets of greening up and leaf senescence, has been determined by remote sensing using mainly the normalized difference vegetation index (NDVI). In boreal regions, the results suffer from significant uncertainties because of the effect of snow on NDVI. In this paper, SPOT VEGETATION S10 data over Siberia have been analysed to define a more appropriate method. The analysis of time series of NDVI, normalized difference snow index (NDSI), and normalized difference water index (NDWI), together with an analysis of in situ phenological records in Siberia, shows that the vegetation phenology can be detected using NDWI, with small effect of snow. In spring, the date of onset of greening up is taken as the date at which NDWI starts increasing, since NDWI decreases with snowmelt and increases with greening up. In the fall, the date of onset of leaf coloring is taken as the date at which NDWI starts decreasing, since NDWI decreases with senescence and increases with snow accumulation. The results are compared to the results obtained using NDVI-based methods, taking in situ phenological records as the reference. NDWI gives better estimations of the start of greening up than NDVI (reduced RMSE, bias and dispersions, and higher correlation), whereas it does not improve the determination of the start of leaf coloring. A map of greening up dates in central Siberia obtained from NDWI is shown for year 2002 and the reliability of the method is discussed.     

A 20-year Landsat water clarity census of Minnesota's 10,000 lakes

A 20-year comprehensive water clarity database assembled from Landsat imagery, primarily Thematic Mapper and Enhanced Thematic Mapper Plus, for Minnesota lakes larger than 8 ha in surface area contains data on more than 10,500 lakes at five-year intervals over the period 1985–2005. The reliability of the data was evaluated by examining the precision of repeated measurements on individual lakes within short time periods using data from adjacent overlapping Landsat paths and by comparing water clarity computed from Landsat data to field-collected Secchi depth data. The agreement between satellite data and field measurements of Secchi depth within Landsat paths was strong (average R² of 0.83 and range 0.71–0.96). Relationships between late-summer Landsat and field-measured Secchi depth for the combined statewide data similarly were strong (r² of 0.77–0.80 for individual time periods and r² = 0.78 for the entire database). Lake clarity has strong geographic patterns in Minnesota; lakes in the south and southwest have low clarity, and lakes in the north and northeast tend to have the highest clarity. This pattern is evident at both the individual lake and the ecoregion level. Mean water clarity in the Northern Lakes and Forest and North Central Hardwood Forest ecoregions in central and northern Minnesota remained stable from 1985 to 2005 while decreasing water clarity trends were detected in the Western Corn Belt Plains and Northern Glaciated Plains ecoregions in southern Minnesota, where agriculture is the predominant land use. Mean water clarity at the statewide level also remained stable with an average around 2.25 m from 1985 to 2005. This assessment demonstrates that satellite imagery can provide an accurate method for obtaining comprehensive spatial and temporal coverage of key water quality characteristics that can be used to detect trends at different geographic scales.     

The sensitivity of MERIS atmospheric correction over water to aerosol climatology

Performing a classical atmospheric correction over water requires a well-defined climatology representative of the aerosols encountered in the remote areas of oceans. Different climatologies built up at the global scale are candidates to be implemented, as an auxiliary data file (ADF) including look-up tables (LUTs) with radiative properties of the aerosols, in a traditional atmospheric correction algorithm. In addition to these, two regional climatologies were developed in the 2-Seas region, comprising both the Eastern English Channel and the North Sea, and at the Acqua Alta Oceanographic Tower (AAOT) in the Adriatic Sea. By using the optical data processor of the European Space Agency (ODESA), Medium Resolution Imaging Spectrometer (MERIS) level-1 (L1) data extracted from the MERis MAtchup In-situ Database (MERMAID) were processed to obtain the level-2 (L2) products over water. For a given climatology, a full processing chain was developed to generate the MERIS aerosol LUTs suitable to ODESA. The final step consisted of an analysis of the L2 products, for both the aerosols and marine reflectance, in the framework of the evaluation of the performance of each climatology in atmospheric correction over oceans. Finally, we recommend using the regional aerosol climatologies available from the AErosol RObotic NETwork (AERONET) database, at least for the retrieval of the L2 aerosol product. In regard to marine reflectance, this remains more challenging and needs a more extensive analysis.     

Modelling the dynamics of ERS-1/2 coherence with increasing woody biomass over boreal forests

In this study, changes in the temporal and volume contributions (| n | temporal, | n | volume ) to the observed ERS-1/2 coherence are modelled in response to an increase of forest woody biomass in Central Siberia. It is demonstrated that the distance between neighbouring trees and the vertical structure of boreal forest canopies must be accounted for to model | n | volume . Results confirm that | n | temporal is the main decorrelation factor although | n | volume contributes significantly to the overall loss of coherence observed over mature forests.     

The simulation of texture evolution with finite elements over orientation space I. Development

A new class of finite element schemes is presented for the modeling of arbitrary polycrystalline microstructure. A framework is constructed by viewing texture as arising out of maps that associate crystals with orientations drawn from a fundamental region of orientation space. The state of the microstructure is characterized by an orientation distribution function and a hardness field over a fundamental region. Differential equations are derived to describe the evolution of the two fields. Finite element schemes, cast over discretized fundamental regions, are considered for the solution of the differential set. Properties of the schemes considered are illustrated by application to the texturing of a planar polycrystalline microstructure.     

The contribution of AMSR-E 18.7 and 10.7 GHz measurements to improved boreal forest snow water equivalent retrievals

Four seasons (2004–2007) of snow surveys across the boreal forest of northern Manitoba were utilized to determine relationships between vertically polarized Advanced Microwave Scanning Radiometer (AMSR-E) brightness temperatures (T_B) and ground measurements of snow water equivalent (SWE). Regression analysis identified moderate strength, yet statistically significant relationships between SWE and T_B differences (36.5–18.7; 36.5–10.7; 18.7–10.7) for individual seasons. When multiple seasons were considered collectively, however, the 36.5–18.7 and 36.5–10.7 differences were insignificant because the seasonal linear relationships shifted from year to year over the same T_B range regardless of SWE. This inter-seasonal consistency in T_B was explained through significant correlations with vegetation density as characterized by a MODIS-derived forest transmissivity dataset. More encouraging results were found for the 18.7–10.7 difference: the relationship with SWE remained statistically significant when multiple years were considered together, and the 18.7–10.7 difference was not significantly associated with vegetation density. Additional snow survey data from the Northwest Territories (2005–2007) were used to verify the 18.7–10.7 relationship with SWE across the northern boreal forest. These results suggest use of the 18.7–10.7 T_B difference, rather than the traditional 36.5–18.7 T_B difference, is necessary to capture inter-seasonal SWE variability across forested regions.     

A scenario-based stochastic programming model for water supplies from the highland lakes

A scenario-based, multistage stochastic programming model is developed for the management of the Highland Lakes by the Lower Colorado River Authority (LCRA) in Central Texas. The model explicitly considers two objectives: (1) maximize the expected revenue from the sale of interruptible water while reliably maintaining firm water supply, and (2) maximize recreational benefits. Input data can be represented by a scenario tree, built empirically from a segment of the historical flow record. Thirty-scenario instances of the model are solved using both a primal simplex method and Benders decomposition, and results show that the first-stage ('here and now') decision of how much interruptible water to contract for the coming year is highly dependent on the initial (current) reservoir storage levels. Sensitivity analysis indicates that model results can be improved by using a scenario generation technique which better preserves the serial correlation of flows. Ultimately, it is hoped that use of the model will improve the LCRA's operational practices by helping to identify flexible policies that appropriately hedge against unfavorable inflow scenarios.     

Development and validation of AVHRR-derived regional forest cover data for the north-eastern U.S.

As part of developing the geographic information system (GIS) to support a north-eastern U.S. regional forest change modelling effort, we investigated the utility of several sources of AVHRR data in regional forest cover mapping. Single-date classified Advanced Very High Resolution Radiometer (AVHRR) imagery in combination with existing USGS Land Use/Land Cover data was used to create a forest cover database that encompassed eastern New York state and all of New England. The USGS EROS Data Center Conterminous U.S. Land Cover Characteristics database was also evaluated for comparison. Statistical analysis showed that the AVHRR-derived regional land cover datasets provided estimates of total forest area that were comparable to U.S. Forest Service county level estimates. The AVHRR imagery recorded after leaf fall appeared to enhance the discrimination of coniferous vs. deciduous forests.     

河湖水环境治理支撑系统设计及应用构想

为提高水环境治理水平、改善水环境质量提供服务和支撑,针对水环境治理信息化支撑不足的现状,探讨信息化支撑系统设计思路、实现技术和应用场景.基于GIS技术构建水环境治理数据库,利用支撑系统融合应用GIS、数据库、环境模拟和绩效评价等技术,对不同要素集成管理,供各子系统调用,结合水环境模型、绩效评价等实现治理效果的模拟、预测...     

基于蒙特卡罗仿真的湖库水质预测及富营养化风险评估方法

已有的水质预测研究通常是单值预测,并以此为依据分析富营养化状态,具有一定的偶然性和不确定性。结合水质动力学模型,提出了一种基于蒙特卡罗仿真的湖库水质预测及富营养化风险评估方法。在已知水质动力学模型水质指标和模型参数的先验分布基础上,利用蒙特卡罗仿真预测水质指标的演化过程,获得未来时刻水质指标取值的概率分布,实现水质预测。进一步,构造综合营养状态指数,结合水质指标预测结果,计算综合营养状态指数的概率分布和处于不同营养程度的概率,实现富营养化风险评估。仿真结果表明,该方法能够有效实现水质预测和富营养化分析,且考虑更加全面、准确,克服了单值预测结果带来的偶然性。     

Atmospheric correction of ENVISAT/MERIS data over inland waters: Validation for European lakes

Traditional methods for aerosol retrieval and atmospheric correction of remote sensing data over water surfaces are based on the assumption of zero water reflectance in the near-infrared. Another type of approach which is becoming very popular in atmospheric correction over water is based on the simultaneous retrieval of atmospheric and water parameters through the inversion of coupled atmospheric and bio-optical water models. Both types of approaches may lead to substantial errors over optically-complex water bodies, such as case II waters, in which a wide range of temporal and spatial variations in the concentration of water constituents is expected. This causes the water reflectance in the near-infrared to be non-negligible, and that the water reflectance response under extreme values of the water constituents cannot be described by the assumed bio-optical models. As an alternative to these methods, the SCAPE-M atmospheric processor is proposed in this paper for the automatic atmospheric correction of ENVISAT/MERIS data over inland waters. A-priori assumptions on the water composition and its spectral response are avoided by SCAPE-M by calculating reflectance of close-to-land water pixels through spatial extension of atmospheric parameters derived over neighboring land pixels. This approach is supported by the results obtained from the validation of SCAPE-M over a number of European inland water validation sites which is presented in this work. MERIS-derived aerosol optical thickness, water reflectance and water pigments are compared to in-situ data acquired concurrently to MERIS images in 20 validation match-ups. SCAPE-M has also been compared to specific processors designed for the retrieval of lake water constituents from MERIS data. The performance of SCAPE-M to reproduce ground-based measurements under a range of water types and the ability of MERIS data to monitor chlorophyll-a and phycocyanin pigments using semiempirical algorithms after SCAPE-M processing are discussed. It has been found that SCAPE-M is able to provide high accurate water reflectance over turbid waters, outperforming models based on site-specific bio-optical models, although problems of SCAPE-M to cope with clear waters in some cases have also been identified.     

Comparison of satellite-derived and in-situ observations of ice and snow surface temperatures over Greenland

The most practical way to get spatially broad and continuous measurements of the surface temperature in the data-sparse cryosphere is by satellite remote sensing. The uncertainties in satellite-derived LSTs must be understood to develop internally-consistent decade-scale land surface temperature (LST) records needed for climate studies. In this work we assess satellite-derived “clear-sky” LST products from the Moderate Resolution Imaging Spectroradiometer (MODIS) and the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), and LSTs derived from the Enhanced Thematic Mapper Plus (ETM+) over snow and ice on Greenland. When possible, we compare satellite-derived LSTs with in-situ air temperature observations from Greenland Climate Network (GC-Net) automatic weather stations (AWS). We find that MODIS, ASTER and ETM+ provide reliable and consistent LSTs under clear-sky conditions and relatively-flat terrain over snow and ice targets over a range of temperatures from ? 40 to 0 °C. The satellite-derived LSTs agree within a relative RMS uncertainty of ~ 0.5 °C. The good agreement among the LSTs derived from the various satellite instruments is especially notable since different spectral channels and different retrieval algorithms are used to calculate LST from the raw satellite data. The AWS record in-situ data at a “point” while the satellite instruments record data over an area varying in size from: 57 × 57 m (ETM+), 90 × 90 m (ASTER), or to 1 × 1 km (MODIS). Surface topography and other factors contribute to variability of LST within a pixel, thus the AWS measurements may not be representative of the LST of the pixel. Without more information on the local spatial patterns of LST, the AWS LST cannot be considered valid ground truth for the satellite measurements, with RMS uncertainty ~ 2 °C. Despite the relatively large AWS-derived uncertainty, we find LST data are characterized by high accuracy but have uncertain absolute precision.     

Soil water and plant canopy effects on remotely measured surface temperatures

Abstract.

Thermal infrared remote sensing of diurnal crop canopy temperature variations represents a possible method for determining the availability of soil water to plants. This study was performed to assess the effects of soil water and crop canopy on apparent temperatures observed by means of remote sensors, and to determine the impact of these effects on remote soil water monitoring. Airborne thermal scanner and apparent reflectance data (one date) and ground PRT-5 data (three dates) were collected primarily over barley and other small grain canopies. Plant heights, cover, and available soil water for four layers in the top 20 cm were determined. Analysis of the data showed a close inverse linear relationship between the available water and the day minus night temperature difference δT, for thick barley canopies (plant cover above 90 per cent) only. The use of apparent reflectance values in the visible region did not improve available soil water regression equations substantially. These results suggest that the available water or plant stress could only be accurately determined for thick canopies, and that the reflectance data could probably be used to identify such canopies but would not improve regression estimates of soil water from remote sensing data.