The AMSR-E/NASA (Advanced Microwave Scanning Radiometer-Earth Observing System/National Aeronautics and Space Administration) daily global SM (soil moisture) product (2002-2011, 25 km resolution) has been widely used but exhibits limited sensitivity to intra- and inter-annual variability in many regions. This limitation is mainly attributed to inaccurate parameter values (A0andA1), which account for vegetation transmittance and emission in the AMSR-E/NASA SM retrieval algorithm. To address this issue, we recalibratedA0andA1using in-situ SM measurements from 13 observation networks (192 sites) and established their empirical relationships with FVC (Fractional Vegetation Cover). Four dominant land-cover types (i.e., bare soil, grassland, cropland, and forest) were considered due to their global representativeness and extensive coverage with in-situ SM measurements. Based on these relationships, we generated global maps ofA0andA1, and produced an improved Global Daily AMSR-E SM dataset (GD_AMSR-E_SM; 2002-2011, 25 km resolution) using GLASS (Global Land Surface Satellite) FVC dataset and AMSR-E observations. Validation against in-situ SM measurements within six independent networks shows that the GD_AMSR-E_SM dataset achieves greater consistency with in-situ SM measurements, with MAE (Mean Absolute Error) and RMSE (Root Mean Square Error) values of 0.026 cm³/cm³ and 0.032 cm³/cm³, respectively. This represents average reductions of 20% and 26% compared with the AMSR-E/NASA, AMSR-E/JAXA (Japan Aerospace Exploration Agency), and AMSR-E/LPRM (Land Parameter Retrieval Model) SM products. The enhanced algorithm improves the accuracy and reliability of AMSR-E observations for long-term global SM monitoring.

The urban complex material and geometry characteristics result in a 3D thermal heterogeneity and that limits the urban surface temperature (UST) retrieval. In this study, we improved the temperature and emissivity separation (TES) algorithm by incorporating thermal heterogeneity within mixed pixel (MP). The improvement was based on the Discrete Anisotropic Radiative Transfer (DART) model and applied to retrieve land surface temperature (LST) from SDGSAT-1. The TES-MP algorithm was validated with ECOSTRESS and the data simulated by DART model, and the results show that it can reach good accuracy under complex urban conditions. Based on the simulated scenes from the Sheung Wan building in Hong Kong, the RMSE of TES-MP algorithm is 0.85K under thermal homogeneous conditions and 1.13K under thermal heterogeneous conditions. Additionally, new high-reflectivity construction materials are common in urban areas, i.e. metal materials. It shows that the relationship between MMD and minimums emissivity(εmin) is not applicable to these materials. Thus, the impacts of such materials on the UST retrieval were evaluated. The results show that the higher the reflectivity and the fractional abundance of such materials, the larger the LST underestimation. Under nadir observation conditions, the proportion of high-reflectivity walls does not cause significant LST retrieval errors. The geometry and adjacency effects on retrieved LST were evaluated, and results show that the TES-MP algorithm has some resistance to geometry and adjacency effects, thereby reducing errors in LST retrieval. This study provides a new view on retrieving LST of urban MPs, and also suggests that three or more bands should be considered when setting up thermal infrared sensors.

In the Tibetan Plateau (TP) region, the foreseeable increase in air temperature may have profound and complex effects on the local hydrological cycle, and is likely to increase water loss from the land surface to the atmosphere through evapotranspiration (ET). Quantifying ET and its regulatory mechanisms are major challenges for understanding the water cycle and land-atmosphere interactions in the TP region. We evaluated the performance of several Earth observation-based ET datasets in the TP region, and explored the spatiotemporal variation of ET in the same region. The accuracy of different global ET datasets was evaluated, and ETMonitor and PML-V2 provide the best accuracy with overall high correlation, low bias, and low root mean square error. ETMonitor ET is also the only product with both high spatial (~1 km) and temporal (daily) resolution. ETMonitor ET may reflect the effect of mountain topography on ET better than other global products, i.e., ET values are higher in the humid valleys with denser vegetation cover and higher soil moisture, and ET values are lower on the mountain slopes at higher elevations with less vegetation cover and colder climate. Other ET products failed to capture the spatial patterns of ET in the mountainous regions, and this suggests that the spatial resolution is not the only dominant factor leading to the poorer performance of these ET products in the mountain regions of the TP. The results show that multi-year average ET is 339 mm/yr in the TP region during 2000-2021, which accounts for about 51% of the total precipitation in the TP region. From 2000 to 2021, ET over the Tibetan Plateau shows an overall increasing trend with large spatial variability.

Accurate knowledge of the at-surface solar irradiance (SSI) is essential to retrieving surface and atmospheric properties using satellite measurements of back-scattered and reflected radiance. The latter is affected by surface-atmosphere interactions, including the effects of terrain. The SSI is affected by the same processes. This study proposes a method to estimate the components of instantaneous surface solar irradiance: direct, isotropic and circumsolar diffuse, and terrain irradiance, which is expected to improve the simultaneous retrieval of Aerosol Optical Depth (AOD) and surface reflectance. The method takes into account the coupled effects of topography and atmosphere by combining parametrization and the Look-Up Table (LUT) approaches. The method was applied to rugged terrain over the Tibetan Plateau using MODIS atmosphere and surface data, ERA5 reanalysis data, CALIOP aerosol data and a Digital Elevation Model (DEM). The results showed that the SSI estimates were in satisfactory agreement with ground observations at four stations over the Tibetan Plateau in 2018 with R2values of 0.61, 0.44, 0.41, and 0.49, respectively, and RMSE of 205.7 W/m2, 176.9 W/m2, 186.0 W/m2, and 201.2 W/m2, respectively. Estimations of the diffuse irradiance were evaluated separately against the only available in-situ observations at the Dali Station and the results were better than our SSI estimates with R2, RMSE, and relative BIAS being 0.71, 94.98 W/m2, and 31%, respectively. The isotropic and circumsolar diffuse irradiance accounted for 37.57% and 7.68% of the total annual SSI respectively, while diffuse irradiance accounted for 46.48% of the total annual SSI. Under clear skies, every 0.1 increase in AOD caused about a 35 W/m2increase in diffuse irradiance and a decrease of about 25 W/m2of SSI.

The ambition of H2020 OPERANDUM project is to develop and document Nature Based Solutions (NBS) to mitigate risks associated with hydro-meteorological (HM) hazards. NBS mitigate risks by reducing the vulnerability of a particular system. The aim of this work is to demonstrate the use of multisource remote sensing data in documenting the impact of extreme HM events to advance knowledge on vulnerability and exposure. In particular the focus is to document past impacts due to extreme events selected from a characterization of recent (3 0 years) HM events in 11 Open Air Laboratories (OALs) where co-design, co-development and deployment of NBS are taking place. The impacts were documented by applying a wide spectrum of satellite image data and other, close - range, remote sensing techniques. A better understanding of the consequences due to extreme HM events in a particular area (OALs) is essential to identify elements at risk and expected to provide a reference to evaluate the reduction of vulnerability and mitigation of risks past the completion of NBS.

Drought hazards induced by continuous water shortage may damage crop growth and cause severe grain loss. With one of the most intensive irrigation systems, the Indus basin has supported agriculture for millennia and feeds up more than 300 million people. The water supply for the basin scale irrigation is dominated by melting of glaciers and snowpack in the Himalaya and Karakoram mountain ranges, ground water as well as the Asian monsoon rainfall. To understand how ecosystems over the Indus basin with such complex water supply mechanism response to meteorological drought (rainfall shortage) is critical for future drought monitoring and evaluation applications. This study evaluated the spatiotemporal response pattern using correlation analysis of rainfall anomalies (3-month scale) and vegetation anomalies (1-month scale) with long-term satellite observations. The result found that the vegetation over northern Indus valley significantly coupled to rainfall variation during both summer (Kharif) and winter (Rabi) monsoon season. While significant response during Kharif season also was identified over the southern part, especially over the Punjab, where was well equipped with irrigation system. We concluded that special attention should be paid to drought assessment in terms of rainfall and vegetation anomaly over the Indus basin.

Mountain glaciers are one of the major fresh water resources. Glacier mass balance on the Tibetan Plateau (TP) can directly reflect local climate change and plays a crucial role in the terrestrial water cycle and food security of local people. In this study, we improved the procedure to analyze Three-Line-Array (TLA) stereo images to estimate the glaciers mass balance in Kangri Karpo mountains using Zi Yuan-3 (ZY-3) TLA data and C-band Shuttle Radar Topography Mission (SRTM) Digital Elevation Model (DEM) in two periods, i.e. 2000–2013 and 2013–2017. The results showed that the mean mass balance of glaciers between 2000 and 2017 was −0.91 ± 0.02 m w.e. a−1. The glaciers presented accelerated mass loss in recent years (2013–2017, −2.84 ±0.05 m w.e. a−1), compared with the first decade in 21st (2000–2013, −1.59 ± 0.03 m w.e. a−1), while the melt rate in debris-covered glaciers was larger than in clean-ice glaciers.

The AMSR-E (Advanced Microwave Scanning Radiometer - EOS) sensor onboard NASA's Aqua satellite is a passive microwave radiometer and is widely used in land surface Soil Moisture (SM) retrieval. The NASA National Snow and Ice Data Center Distributed Active Archive Center (NSIDC DAAC) archives and distributes a daily SM product (AMSR-E/NASA) from 2002 to 2011 at 25km resolution retrieved by a simplified soil moisture retrieval algorithm derived from a simplified radiative transfer model. However, the SM dynamic range of this product is very narrow, which basically cannot reflect intra- and inter-annual variations. This paper analyzed the AMSR-E/NASA SM product and compared it with the AMSR-E/JAXA SM. Furthermore, the reason of the narrow intra- and inter-annual variation of AMSR-E/NASA SM was analyzed in depth. Finally, the AMSR-E/NASA SM product was improved using in-situ data collected in the Tibetan Plateau. Results indicated that the improved monthly SM AMSR-E product was better than AMSR-E/NASA and AMSR-E/JAXA in Naqu study area from January to September of 2011, as documented by a lower RMSE (Root Mean Square Error) against in-situ SM data, i.e. 0.065 cm3cm-3.

The monthly nighttime light data observed by the Visible Infrared Imaging Radiometer Suite (VIIRS) sensor onboard the National Polar-orbiting Partnership (NPP) satellite are useful to assess human activities. However, noise in the VIIRS data and unstable power supply in many areas often limit its application in detecting anthropic activities. This study aims to develop a new method (i.e., Patches Denoising Method, PDM) to remove noise in the VIIRS monthly nighttime light data towards better information on anthropic activities. The new synthesized data were examined in the Sahel region where the intensity of nighttime light is often affected by unstable power supply. The results showed that the new data could capture more settlements correctly compared with the annual data released by the National Centers for Environmental Information (NCEI) of the National Oceanic and Atmospheric Administration (NOAA). This suggests that the Patches De-noising Method is a useful way to remove noise in the NPP-VIIRS monthly nighttime light data.

Mountain glaciers are sensitive to climate change and are thus relevant indicators of regional climate variability. In order to understand the dynamics of glaciers, retrieving glacier surface velocity is valuable in understanding physical processes in glaciers. We apply an image cross-correlation algorithm in the frequency domain to derive glacier velocity on the Yanong glacier between 2013 and 2018. The results indicate that the flow patterns are related to the terrain complexity. Yanong Glacier maximum velocity was 168 m/year at the elevation around 4700 meters. The surface velocity exceeded 100 m/year above the elevation of 4200 m, while above 5000 m the surface velocity fluctuated around 60 m/year along the main stream.

Reliable and timely mapping of crop growth conditions and of their water resources is considered a prioritary application in light of the abrupt climate changes. With this view, the paper presents a novel approach that makes use of dense C-Band time-series for the timely estimation of crop growth stages and for the detection of changes in crop water conditions, especially related to precipitation and irrigation events. Aided by vegetation indexes extracted from Landsat and Sentinel-2 imagery, the proposed Sentinel-l centered method exploits both temporal patterns of crop growth and spatial patterns of water anomalies to enhance its classification robustness.

The state of the land surface and the water cycle over the South and East Asia can be determined by space observation. New or significantly improved algorithms have been developed and evaluated against ground measurements. Variables retrieved include land surface properties, i.e. NDVI, LAI, FPAR, albedo, soil moisture, glacier and lake levels. Based on these biophysical parameters derived from microwave and optical remote sensing observations, a hybrid remotely sensed evapotranspiration (ET) estimation model named ETMonitor was developed and applied to estimate the daily actual ET of the Southeast Asia at a spatial resolution of 1 km. The changes in glaciers and lakes on the Tibetan Plateau, and the drainage links between glaciers and lakes are determined in this climate-sensitive region.

Radiometric measurements taken at a distance from evaporating bodies have been used for about 50 years to obtain a measure of water use. The basic physics of determining water use is the principle of energy conservation at the evaporating surface, but energy flux densities need to be parameterized using the variables which can be actually captured by radiometric measurements. Both observations of surface temperature and of soil water content are used for this purpose. Models of energy and water exchange vary from semi-empirical relationships between latent heat flux density and surface temperature to fully three-dimensional models of the soil-foliage-atmosphere system. Several ET satellite data products are available and evaluations against ground measurements have been published. Results suggest a dependence of accuracy on climate, soil and vegetation. The review of documented performance is combined with the sensitivity analysis of widely used ET algorithms, e.g. SEBI, SEBS, SEBAL and ET Monitor.

The applications of Multi-temporal Normalized Difference Vegetation Index (NDVI), a critical proxy for analysing vegetation dynamics, have been long hindered by prevalent noise. The Harmonic ANalysis of Time Series (HANTS) has been widely applied to reconstruct noise- and cloud-free NDVI time series data set from regional to global scales. The reconstruction performance of HANTS is severely dependent on the inherent parameter settings in HANTS. However, most applications set the parameters of HANTS based on users' experience. This study analysed the sensitivity of the reconstruction performance to several critical parameters and weighting scheme of HANTS. The results document the optimized parameter settings for global NDVI time series reconstruction.

Two different land surface temperatures (with considering geometry effect and without considering geometry effect) were derived to estimate sensible heat flux over urban areas using remotely sensed land surface temperature. Results showed that the surface temperatures with considering geometry effect could bring up to 18.0% difference in the estimated sensible heat flux over built-up areas. The sensible heat fluxes derived from surface temperature with considering geometry effect were lower than those without considering geometry effect, particularly over built-up areas (e.g., the mean value is 44.3 W · m-2). The sensible heat fluxes over built-up areas are higher than those over flat impervious surfaces. In addition to the anthropogenic heat emission over urban areas, dense buildings cause higher displacement heights and roughness lengths than flat surfaces; thus, aerodynamic resistances for heat are lower over built-up areas; this makes the sensible heat dissipation into atmosphere easier over built-up areas than over flat surfaces.

Anthropogenic heat is the heat flux generated by human activities and is a major contributor to the formation of an urban heat island. In a city such as Hong Kong, obtaining pure pixels from medium- or coarse-resolution remote sensing images is challenging. Considering the completely different thermal properties of vegetation and impervious surfaces, this letter developed a novel algorithm to estimate anthropogenic heat fluxes by decomposing image pixels into fractions of impervious surfaces and vegetation, and by estimating the total heat flux for the mixed pixel. The Chinese small satellite HJ-1B images with a spatial resolution of 30 and 300 m for visible and thermal wavebands, respectively, and the temporal resolution of four days were used for the heat flux modeling. Results show that anthropogenic heat fluxes in Hong Kong are correlated to the building density and the building height, with${r}^{2} = \mbox{0.92}\ \text{and}\ \mbox{0.58} $on October 11, 2012 and${r}^{2} = \mbox{0.94}\ \text{and}\ \mbox{0.62} $on January 13, 2013, respectively. The average anthropogenic heat fluxes in urban areas are 289.16 and 283.17 W/m2on October 11, 2012 and on January 13, 2013, respectively, and the commercial areas emit the largest anthropogenic heat fluxes around 500–600 W/m2compared with other land-use types. The derived anthropogenic heat fluxes can help in planning and environmental authorities to pinpoint “hot-spot” areas, and they can be used for compliance monitoring.

High-density airborne light detection and ranging (LiDAR) data with point densities over 50 points/ m2provide new opportunities, because previously inaccessible quantities of an individual tree can be derived directly from the data. We introduce a skeleton measurement methodology to extract the diameter at breast height (DBH) from airborne point clouds of trees. The estimates for the DBH are derived by analyzing the point distances to a suitable tree skeleton. The method is validated in three scenarios: 1) on a synthetic point cloud, simulating the point cloud acquisition over a forest; 2) on examples of free-standing and partly occluded trees; and 3) on automatically extracted trees from a sampled forest. The proposed diameter estimation performed well in all three scenarios, although influences of the tree extraction method and the field validation could not be fully excluded.

Land surface albedo is a very important parameter, which can be derived from a bidirectional reflectance distribution function (BRDF) model with angular integration of BRDF in a particular distribution of downward solar irradiance. The Algorithm for MODIS Bidirectional Reflectance Anisotropic of Land Surface (AMBRALS) utilizes a kernel-driven BRDF model to retrieve land surface albedo, but it does not take into account the topographic effect. This letter proposed an improved method by adding topographic factor to the AMBRALS, which removes the topographic effect on land surface reflectance and considers the topographic effect in retrieving land surface albedo. This method is applied to the HJ-1A/B CCD land surface reflectance data to retrieve the land surface albedo. The results are compared with those from the AMBRALS and with the ground measurements. The comparison results show that the proposed method performs well in general and better than the AMBRALS over rugged area.

Land surface albedo is derived from a bidirectional reflectance distribution function (BRDF) model with angular integration of BRDF. The kernel-driven model is based on the Algorithm for MODIS Bidirectional Reflectance Anisotropic of Land Surface (AMBRALS) to retrieve land surface albedo. However, at moderate/low spatial resolution scale, topography results in mutual shading of sub-pixels, the kernel-driven model does not deal explicitly with such topographic effects. This paper proposes an improved kernel-driven model by adding a topographic factor to the kernel-driven model. This model was applied to MODIS land surface reflectance and the results were compared with the ones obtained with AMBRALS. The results were evaluated against ground measurements.

The polythermal glacier is one of the dominant glaciers distributed widely in the transition zone of monsoon and continental climate, Tibetan Plateau. However, the glacier dynamics study of this type glacier is still limited in most of glaciated area due to the remote mountains, and high altitude issues. Meanwhile, the surface displacement of polythermal glacier appears strong variations both in seasonal and even shorter time-scales, based on the principles of glaciology mechanics. On one hand, this seasonal variations indicate drainage and movement of melting water between ice body and glacier bed. On the other hand, shorter variations relate to the amount of input water for this supraglacial drainage system. Here, we choose five typical polythermal type glaciers in Nyainqêntanglha Mountains, the south of Nam Co Lake, Tibetan Plateau for a case study. In this study we monitoring the ice surface displacement of these glaciers nearly two decades from 1993 to 2009 using features tracking method. Through this generation of ice motion maps, we intend to explore the association between spatial distribution of ice surface motion anomalies, and the possible patterns of ice speed variations during this period which responds to probable climate influence on surface melting.

Glaciers in the Tibetan mountains are expected to be sensitive to climate change. The change of glacier mass is locally depending on e.g. precipitation, melting and variation in ice flow. In this paper we analyze the data acquired by ICESat/GLAS laser altimetry, along several tracks over the glaciers of the Nyainqêntanglha range from February, 2003 to November, 2004. The surface characteristics are evaluated within laser footprints over the glacier outlines based on the glaciological inventory on the Tibetan Plateau constructed by CAREERI, CAS. For this purpose, we extract two waveform parameters: the waveform width and the number of modes. These parameters are compared with surface slope and roughness obtained from the ASTER GDEM (Global Digital Elevation Model).

This paper presents a method to retrieve surface soil moisture at high-resolution from brightness temperature. To obtain high resolution brightness temperature, downscaling brightness temperature uses higher resolution visible/infrared satellite data and is based on the method of retrieving land surface temperature with passive microwave and the relationship between Microwave Polarization Difference Index (MPDI) and NDVL High resolution soil moisture is retrieved with downscaled brightness temperature using a single-channel algorithm (SCA), and the Qpmodel used to deal with the influence of roughness. Downscaled brightness temperature is compared with the aircraft data during the Experiment in the Heihe River Basin in China. The retrieved high resolution soil moisture is compared with in situ (0-6 cm depth) point measurements during SMEX02, obtaining R2= 0.74 and RMSE as 0.047 cm3cm-3.

Current open information systems need formal semantic definitions to describe and handle information which is dealt within different interaction environments such as human-to-human, human-to-computer, and computer-to-computer interactions. This paper provides a semantic definition of the knowledge used by environmental management information systems. We describe an ontological model in UML class diagrams and present a formalization of the ontology in first-oder logic and encoded in description logic. A model transformation approach is also used to map the UML class diagram elements into web ontology language. A running example is presented to explain the ontological knowledge modeling of EMIS SOLERES cartography and satellite information.

Light detection and ranging (LiDAR) has been shown to have a great potential in the accurate characterization of forest systems; however, its application to salt-marsh environments is challenging because the characteristic short vegetation does not give rise to detectable differences between first and last LiDAR returns. Furthermore, the lack of precisely identifiable references (e.g., buildings, roads, etc.) in marsh areas makes the registration and bias correction of the LiDAR data much more difficult than in conventional urban- or forested-area applications. In this paper, we introduce reliable methods to remove random and systematic errors and to register raw data, as well as a new procedure, to determine the optimal filter window size to separate ground and canopy returns. A limited amount of field observations is used to determine the size of the filtering window which produces the minimally biased estimates of the digital terrain model (DTM). The digital surface model (DSM, representing the canopy top) is then obtained in a similar manner, and the digital vegetation model (DVM, representing the vegetation height) is computed as the difference between the DSM and the DTM. We apply this procedure to a study marsh within the Venice Lagoon, Italy, and obtain a high-accuracy DTM. The error$(z\_LiDAR-z\_field)$is 2.2 cm, with a standard deviation of 6.4 cm. The comparison of the estimated DVM with field observations shows an underestimation of the height of the canopy top (17.7 cm, on average). The height of the lowest canopy elements (e.g., basal leaves), however, is significantly correlated to the LiDAR-derived DVM, showing that this contains useful information on the canopy structure.

Vegetation moisture is a key parameter in fire risk modeling. Many authors have demonstrated the role of remote sensing in the assessment of the equivalent water thickness (EWT), which is defined as the weight of liquid water per unit of leaf surface. However, forest fire danger models rely on fuel moisture content (FMC) as a measure of vegetation moisture. FMC is defined as the ratio of the leaf liquid water weight over the leaf dry weight. In a previous research it has been shown the potential of the Moderate Resolution Imaging Spectroradiometer (MODIS) ground reflectance data in retrieving both EWT and FMC at leaf level. Though the atmosphere alters ground signature recorded by the sensor, in this paper it will be shown that a simple index can be designed that allows a fast and accurate estimation of FMC (r2= 0.83) with MODIS data.

In the process of applying the theoretic results of BRDF model study to remote sensing image data, an important step is to extract BRDF features from multiangular images. Because of the limitations of registration, pixel alignment and the intrinsic scale scene, it is necessary to perform spatial average to the georeferenced multiangular images before extracting BRDF feature. Otherwise the feature will no be representative to the surface property. Based on the analysis of geometrical limitations, this paper discussed how the apparent BRDF feature changes from random to order after the spatial average.

Presents a new method to analyze the relation between canopy spectral reflectance and component spectral properties. The polynomial decomposition method differs from linear spectral unmixing because it takes into consideration the multiple scattering inside canopy. It is consistent with physical BRDF models and more flexible because it does not depend on certain assumption on canopy structure. This method is superior for some kinds of canopy whose structure is ambiguous between homogeneous and discrete. The output of the analysis is "angular-structural coefficients" which is possible to be related directly to canopy biophysical parameters.

Quantitative vegetation monitoring by means of remote sensing methods, beyond simple spectral indices, is an objective for the next generation of satellite systems, such as the ESA Earth Explorer Core Mission candidate SPECTRA (Surface Processes and Ecosystem Changes Through Response Analysis). In order to derive and validate methods for retrieval of biophysical information, and in particular vegetation properties from hyperspectral / multiangular measurements, a series of campaigns genetically called DAISEX (Digital Airborne Imaging Spectrometer Experiment) were carried out in La Mancha, Spain, from 1998-2000. A combination of aircrafts carrying different sensors and flying in different configurations allowed to get a complete dataset of hyperspectral and multiangular data acquired simultaneously, together with the necessary atmospheric information and ground data needed for validation of biophysical retrievals. A two-years study funded by ESA has exploited in depth the DAISEX dataset to evaluate the accuracy achieved in the retrieval of each one of the different vegetation properties, by using different methods applied over the same dataset. The results have shown potentials and limitations, but have allowed to set accuracy limits realistically achievable and to set more precise requirements for future developments.

Principal Component Analysis (PCA) is a classical multivariate data analysis method that is useful in linear feature extraction and data compression. It can compress the most information in the original data space into a few features. Generally, remote sensing image contains (is composed of) many different objects such as land cover classes, but for a specific purpose of remote sensing application, only a few classes may be relevant. In this paper, a new method called Modified Principal Component Analysis (MPCA) is proposed and applied to a DAIS (Digital Airborne Imaging Spectrometer) image acquired in Venice, Italy. The results show that the features form MPCA is more effective in information compression, classes separablity and classification accuracy than those form PCA.

A methodology is proposed for selecting the best training set, the best classifier and the best set of parameters associated to the classifier. A performance indicator is defined from measures of signatures separability, accuracy between signatures in the training set and in the classified final image, and probability that a pixel belongs to the class which has been assigned. The theoretical description of the suggested methodology has been proved using a image from the Fourier time series analysis. The algorithm has been executed and the obtained results for the best and the worst classifier have been analyzed and evaluated.

Searching specific structures of different scales in digital images, we tested multiple alternatives of a mobile window method, developed starting from an algorithm by Cayula and Cornillon. These alternatives presented an increasing computing cost when detecting meso-structures in complex images. Quality borders required processing a great number of window-threshold pairs, because to detect some structures was required to relax the work conditions (bimodality and cohesion). Our proposal, in this work, is relaxing the conditions but not for all the window-threshold pairs, only to a selected set of windows, those near windows that accomplish strict conditions. We propose a new method that is a combination of the mobile window one, previously used, and the seeded regions approach. In this case, we seed not regions but borders.

Land surface parameterizations in numerical weather prediction describe the exchange of energy and water at the land-atmosphere interface. They are generally based on a coupled energy-water budget equation, and the results depend critically on (prescribed) land surface characteristics, notably albedo, soil water storage capacity and aerodynamic roughness. In a recent case-study bi-angular (nadir and forward) observations provided by the ATSR instrument were used to estimate the component soil and foliage temperature. For two images overlying the Netherlands on 14 May 1998 and South-East Spain on 9 September 1998 vegetation and bare ground temperatures were derived from ATSR data, and aggregated to a grid of 25/spl times/25 km, compatible with a NWP model. This NWP model was equipped with a land surface parameterization scheme in which vegetation and bare ground temperature were calculated separately. In a variational assimilation scheme, the NWP model was forced to match the observed component temperatures by changing both the soil moisture content and the aerodynamic roughness for the heat exchange of the bare ground component. It appeared that the optimal solution differed significantly for the two regions. For The Netherlands, changing the aerodynamic roughness did not affect the correspondence to observations, and soil moisture had to be changed to increase the overall model performance.

The exploration of the full potential of multi-angular thermal infrared measurements is hampered by the scarcity of accurate and representative data sets. Acquisition of field data has several constraints, including lack of a suitable airborne sensor system. Development and evaluation of algorithms requires detailed and accurate radiometric data, which have to be collected in a very short time to limit noise due to the temporal variability of surface temperature controlled by the local heat balance within the canopy. Several canopy properties have to be determined, e.g. leaf area index (LAI), leaf inclination distribution, in addition to soil and foliage temperature. A data set for validation studies should comprise a range of canopies and canopy conditions. We have coped with these difficulties by using a detailed radiative transfer model (CUPID) of the soil-plant--atmosphere system to produce synthetic data sets over a wide range of canopies and hydro-meteorological conditions. These data have been first used to assess the sensitivity of the anisotropy in TIR radiance to LAI and to the soil water content. It was shown that a dry top soil and a wet root zone imply that the brightness temperature is highest at nadir viewing, opposite to the condition with a wet top soil and a drier root zone. The relative magnitude of the directional change in surface temperature compared with the difference between soil and foliage temperature was evaluated in detail over a range of LAI values. Next, the radiance data base created by RT modeling was used to produce realistic complex scenes. Land cover of a complex, irrigated agriculture scene was mapped beforehand using a TM image.

A mixture of foliage and soil is thermally heterogeneous, so the radiometric temperature of the mixture depends on view direction. A simple linear mixture model was applied to estimate the component surface temperatures of foliage and soil temperatures. The potential of directional observations in the thermal infrared region for land surface studies is a largely uncharted area of research. The availability of the dual-view Along Track Scanning Radiometer (ATSR) observations led to explore new opportunities in this direction. In the context of studies on heat transfer at heterogeneous land surfaces, multiangular thermal infrared (TIR) observations offer the opportunity of overcoming fundamental difficulties in modeling sparse canopies. Three case studies were performed on the estimation of the component temperatures of foliage and soil. The first one included the use of multi-angular field measurements at view angles of 0/spl deg/, 23/spl deg/ and 52/spl deg/. The second and third one were done with directional ATSR observations at view angles of 0/spl deg/ and 53/spl deg/ only. Different models have been proposed in literature to interpret observations of directional exitance: (1) simple geometric (deterministic) models of the system, (2) radiative transfer within a complete canopy, and (3) radiative transfer in an inhomogeneous thick layer of vegetation. Our approach is based on the third modeling concept. A target comprising a mixture of foliage and soil is characterized by the gap fraction, and observed radiance is described as a weighted sum of foliage radiance and soil radiance, with the weights being the gap fraction and its complement, respectively.

The goal of the SPECTRA mission is to improve the description of those processes by means of better constraints on and parameterizations of the associated models. The prime objective of SPECTRA is to determine the amount, assess the conditions and understand the response of terrestrial vegetation to climate variability and its role in the coupled cycles of energy, water and carbon. The amount and state of vegetation will be determined by the combination of observed vegetation properties and data assimilation. Specifically, the mission will characterize the amount and state of vegetation with observations of the following variables: (1) fractional vegetation cover; (2) Fraction Absorbed Photosynthetically Active Radiation (FAPAR); (3) albedo; (4) Leaf Area Index (LAI); (5) leaf chlorophyll content; (6) leaf water content; (7) foliage temperature; (8) soil temperature; (9) fractional cover of living and dead biomass. SPECTRA will provide spatially distributed observations (maps) of the key vegetation properties at the spatial resolution of one image pixel and a temporal frequency of one week or lower. Each map will cover an area of 50 km/spl times/50 km. The SPECTRA mission is being studied by the European Space Agency to address these scientific issues. The mission comprises the following elements: a space segment consisting of an imaging spectrometer covering the region 400 nm-2400 nm with a nominal spectral resolution of 10 nm and of an agile platform to perform subsequent, along track observations at seven view angles between -70/spl deg/ and +70/spl deg/; a ground segment consisting of a core data processing facility and specialized Centers of Excellence to guarantee to a wide and diverse community access to higher level data products and to specialized data assimilation systems; and a field segment consisting of 50 to 100 dedicated sites where teams of investigators evaluate the observations and assimilate them in models describing the functioning of terrestrial ecosystems.