A simple single layer model to estimate transpiration from vegetation using multi-spectral and meteorological data

A methodology is developed here to model evapotranspiration (λE_c ) from the canopy layer over large areas by combining satellite and ground measurements of biophysical and meteorological variables. The model developed here follows the energy balance approach, where λE_c is estimated as a residual when the net radiation (Rn), sensible heat flux (H) and ground flux (G) are known. Multi-spectral measurements from the NOAA Advanced Very High Resolution Radiometer (AVHRR) were used along with routine meteorological measurements made on the ground to estimate components of the energy balance. The upwelling long wave radiation, and H from the canopy layer were modelled using the canopy temperature, obtained from a linear relation between the Normalized Difference Vegetation Index (NDVI) and surface temperature. This method separates flux measurements from the canopy and bare soil without the need for a complex two layer model. From theoretical analysis of canopy reflectance, leaf area, and canopy resistance, a model is developed to scale the transpiration estimates from the full canopy to give an area averaged estimate from the mean NDVI of the study area. The model was tested using data collected from the First International Satellite Land Surface Climatology Project (ISLSCP) Field Experiment (FIFE), and the results show that the modelled values of total surface evapotranspiration from the soil and canopy layers vary from the ground measurements by less than 9 per cent.     

The patchwork problem: effects of sub-pixel scale non-uniformity on the spatially-averaged sensible heat flux

Previous experimental and theoretical studies have examined the possible use of thermal-infrared measurements of surface temperature to estimate sensible heat flux from homogeneous surfaces. In extending the methodology suggested by Lagouarde and McAneney to larger scales, it is necessary to consider the effect of spatial variability. Over a heterogeneous mixture of surfaces, statistical errors are possible due to spatially-correlated changes in windspeed, surface roughness and surface temperature. Errors due to the latter two variables are investigated here at the 1 km² scale of the NOAA AVHRR using two specific case studies. Errors due to correlated variations in windspeed and surface roughness, and windspeed and surface temperature, are also estimated. The results suggest that all these errors are generally negligible if application is restricted to surfaces having a maximum crop height of around I m. Given this qualification,estimates of regional scale sensible heat fluxes seem feasible using spatially-averaged measurements of surface temperature along with ancillary estimates of averaged maximum air temperatures, windspeed and surface roughness.     

Retrieval of Component Temperatures based on Landsat-7 ETM+ Remote Sensing Data

Land surface temperature plays an important role in drought monitoring and Simulation of surface heat flux.In arid and semi\|arid regions,the Two\|Source Energy Balance model (TSEB) is commonly used to calculate the heat flux of the earth’s surface.Taking the typical irrigated area of the middle reaches of Heihe as the research area,the four Landsat\|7 ETM+ remote sensing images are selected.The soil surface temperature and canopy temperature were retrieved by combining vegetation index with TSEB model.The decomposition algorithm of soil surface temperature and vegetation canopy temperature is mainly discussed.The results showed that soil surface temperature and vegetation canopy temperature had good temporal and spatial consistency.The inversion accuracy of soil surface temperature and vegetation canopy temperature is indirectly verified by surface net radiation and surface heat flux.The calculated values of surface net radiation and surface heat flux correlate well with the observed values,and the correlation coefficient is greater than 0.92.The linear regression analysis of surface net radiation and surface heat flux shows that the fitting accuracy is high.The soil surface temperature and canopy temperature obtained by surface temperature decomposition are feasible for monitoring drought in typical areas and simulating surface heat flux.     

Estimating the surface water and radiation balance in an upland area from space

The Synthetic Aperture Radar (SAR) carried aboard ERS-1 and-2 has been used to estimate soil moisture in a naturally vegetated area of upland moor in Lancashire, north-west England. This holds promise for the determination of catchment-scale evapotranspiration rates using direct measurements of changes in soil moisture content. Combining such measurements as these with observations of net radiation from the NOAA AVHRR passive radiometer has led to the development of a method, based upon the techniques of Price (IEEE Transactions on Geoscience and Remote Sensing, 28, 940-948, 1990) combined with some new methodologies, whereby the sensible heat flux can be found by the closure of the energy balance equation. A review of the methodology developed is presented together with the potential for refining the system using further instruments. There are limitations of the procedure due to the restricted temporal coverage of the sensors concerned, and their inability to penetrate the surface to investigate soil-water-vegetation processes within the root zone. It is believed, however, that models can be constructed to account for these deficiencies to enable retrieval of an entire surface energy and water budget. These would have a plethora of applications, both direct and as inputs to atmospheric and hydrological models.     

Aerosol optical depth spatio-temporal characterization over the Canadian BOREAS domain

A complete set of Advanced Very High Resolution Radiometer (AVHRR) data (75 images) is used to retrieve aerosol optical depth (AOD) over dense vegetation and over lake water in the visible AVHRR channel. The present approach for remote sensing of aerosols from the National Oceanic and Atmospheric Administration (NOAA)-11 AVHRR sensor is based on the detection of atmospherically dominated signals over dark surface covers such as dense dark vegetation (DDV). Such targets were identified using the reflective portion of the middle-wave AVHRR channel 3 signal. When a fixed DDV surface reflectance is subtracted from the observed reflectance after correction for all other atmospheric effects, the remaining part, which is due to aerosols, is inverted to derive aerosol optical thickness using a look-up table (LUT) similar to that used in water surface inversion. The algorithm was applied to the daily afternoon NOAA-11 AVHRR (1?km×1?km) data acquired from the end of May to mid-August 1994 over the Canadian 1000?km×1000?km Boreal Ecosystem Atmosphere Study (BOREAS) domain. A validation analysis using five ground-based Sun photometers within the studied area shows the good performance of the retrieval algorithm. The approach allows detailed analysis of the AOD spatio-temporal behaviour at the regional scale useful for climate and transport model validation.     

Evapotranspiration calculated from remote multispectral and ground station meteorological data

Evapotranspiration was evaluated by combining remotely sensed reflected solar radiation and surface temperatures with ground station meteorological data (incoming solar radiation, air temperature, windspeed, and vapor pressure) to calculate net radiation and sensible heat flux. Soil heat flux was estimated as a fraction of the net radiation. Instantaneous values of ET were calculated for 18 wheat plots for 44 cloudless days over a growing season. Three of the 18 plots contained lysimeters which provided data to compare against the instantaneous values. For the remaining plots, daily ET was estimated from the instantaneous data and compared with values calculated from soil water contents measured with a neutron moisture meter. For generally clear sky conditions, the comparisons indicated that ET could be adequately evaluated using a combination of remotely sensed and ground based meteorological data. The results suggest that ET maps of relatively large areas could be made using this method with data from airborne sensors. The extent of the area covered appears to be limited by the distance that air temperature and windspeed data can be extrapolated.     

Estimating surface solar radiation over complex terrain using moderate‐resolution satellite sensor data

It is well known that taking into account the influence of complex terrain is essential when using high‐resolution satellite remotely sensed data to estimate surface net solar radiation. This paper investigates whether this is also the case when using moderate‐resolution satellite remotely sensed data, such as Moderate Resolution Imaging Spectroradiometer (MODIS) and Advanced Very High Resolution Radiometer (AVHRR). Firstly, topographic data from a gridded digital elevation model, field measurements from the Tibetan Plateau, and results from the atmospheric 6S model are used to estimate surface incoming solar radiation over complex terrain. The associated error caused by not taking into account terrain complexity is then calculated, and the relative radiation error is estimated by standardizing the error. The results show that the standard deviation of the relative radiation error depends on the solar zenith angle, standard deviation of the height, and resolution of the digital elevation model (or resolution of the satellite sensor data). A single regression equation describes the change in the standard deviation of the relative radiation error with solar zenith angle, standard deviation of height, and resolution of the digital elevation model. This demonstrates that it is necessary to consider terrain complexity when using moderate‐resolution remotely sensed data.     

Correction of the effect of Sun-sensor-target geometry in NOAA AVHRR data

Models of determining the effects of the bidirectional reflectance distribution function (BRDF) of different surfaces and of eliminating the effect of Sun-sensor-target geometry from the remotely sensed satellite data are actual. The objective of this study is to develop a simple relation between the Sun-sensor-target geometry and the remotely sensed vegetation index. In this investigation 238 National Oceanic and Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR) images were used over Hungary during the period 1996-98. The greenness vegetation index (the difference between the reflectance values of near-infrared and visible channels) was used between days of the year 140-200, because the greenness values can be considered as constant in this period over the agricultural areas. The so-called 'hot spot effect' can be observed in the variation of reflectance values with different viewing zenith angles of the sensor. A simple quadratic relation was found between the raw AVHRR greenness values and the angle enclosed by the Sun-target and target-sensor directions over the agricultural areas, forests and grasslands. A correction method was developed to eliminate the effect of the Sun-sensor-target geometry, which it is hoped will improve the accuracy of yield forecasting and estimation procedures using NOAA AVHRR data.     

Estimation and comparison of evapotranspiration from MODIS and AVHRR sensors for clear sky days over the Southern Great Plains

Evapotranspiration (ET) cannot be measured directly from satellite observations but remote sensing can provide a reasonably good estimate of evaporative fraction (EF), defined as the ratio of ET and available radiant energy. It is feasible to estimate EF using a contextual interpretation of radiometric surface temperature (T_o) and normalized vegetation index (NDVI) from multiple satellites. Recent studies have successfully estimated net radiation (R_n) over large heterogeneous areas for clear sky days using only remote sensing observations. With distributed maps of EF and R_n, it is now possible to explore the feasibility and robustness of ET estimation from multiple satellites. Here we present the results of an extensive inter-comparison of spatially distributed ET and related variables (NDVI, T_o, EF and R_n) derived from MODIS and AVHRR sensors onboard EOS Terra, NOAA14 and NOAA16 satellites respectively. Our results show that although, NDVI and T_o differ with the sensor response functions and overpass times, contextual space of NDVI-T_o diagram gives comparable estimates of EF. The utility of different sensors is demonstrated by validating the estimated ET results to ground flux stations over the Southern Great Plains with a root mean square error of 53, 51 and 56.24 Wm^− 2, and a correlation of 0.84, 0.79 and 0.77 from MODIS, NOAA16 and NOAA14 sensors respectively.     

Mapping micro-urban heat islands using NOAA/AVHRR images and CORINE Land Cover: an application to coastal cities of Greece

Land Surface Temperature (LST) is a significant parameter for identifying micro-climatic changes, their spatial distribution and intensities in relation to the urban environment. In this study, LST is estimated using thermal infrared data as acquired by the Advanced Very High Resolution Radiometer (AVHRR) instrument onboard the National Oceanic and Atmospheric Administration (NOAA) satellite and by using a split window algorithm that is adjusted to account for the region of Greece. For the assignment of the surface emissivity, a new methodology based on the Coordination of Information on the Environment (CORINE) Land Cover database for Greece is used. The algorithm is applied to a night-time series of NOAA/AVHRR images of Greece in order to produce surface temperature maps of an enhanced spatial resolution of 250?m for the cities of Thessaloniki, Patra, Volos and Iraklion, which are the most significant harbour cities of Greece. Results indicate the presence of urban heat islands (UHIs) in each case study, with highest temperatures detected along the coastal zone of the harbour cities resulting from denser urban fabric and road network as well as intense human activity.     

Daily net radiation estimated from air temperature and NOAA-AVHRR data: A case study for the Iberian Peninsula

In this work we present a methodology to obtain the daily net radiation flux from NOAA-AVHRR data. To achieve this we need first to obtain shortwave net radiation flux from the solar global radiation flux and the albedo map. Secondly, we need to obtain the upward longwave radiation flux from surface temperature and emissivity and the downward longwave radiation flux from air temperature and vapour pressure. A daily net radiation flux image corresponding to flat areas of the Iberian Peninsula is presented as an example in which we show that daily net radiation flux can be obtained with an uncertainty of approximately 1.5 mm day^-1.     

Cloud top phase determination from the fusion of signatures in daytime AVHRR imagery and HIRS data

An improved methodology for the retrieval of water vapour profiles from DMSP SSM /T-2 microwave sounder data has been demonstrated using cloud-top temperatures derived from NOAA AVHRR imagery as a constraint. However, the automated analysis of cloud-top temperature in AVHRR imagery is complicated by the presence of optically-thin cirrus clouds, since a component of the upwelling radiation from below passes unatttenuated to space. Therefore, cloud-top phase must first be determined to ensure the accurate specification of cloud-top temperature. In this paper, a new approach is presented for the specification of cloud-top phase in an operational environment. The methodology combines results from bi-spectral cloud tests for ice and water clouds in daytime AVHRR imagery with cloud-top pressure analyses based upon the CO2 slicing of HIRS data. The accuracy of the automated cloud-top phase analyses is measured quantitatively against manual analyses of the AVHRR imagery. It is concluded that the fusion of cloud signatures in AVHRR imagery and HIRS data improves the specification of cloud-top phase in the higher resolution imagery and reduces the ambiguity inherent in analyses based solely upon bi-spectral techniques.     

A comparison of experimental energy balance components data and SEBAL model results in Dourados,Brazil

The understanding of surface energy flux is important as regards weather and climate models. Therefore, analysis of remotely sensed data, estimation of surface energy balance components (fluxes), validating experiments and results are discussed in this work. Data extracted from NOAA–AVHRR (National Oceanic and Atmospheric Administration–Advanced Very High Resolution Radiometer) satellite images were used to estimate the fluxes based on the SEBAL (Surface Energy Balance for Land) algorithm, as suggested by Bastiaanssen [Bastiaanssen, W.G.M., 1995, Regionalization of surface flux densites and moisture indicators in composite terrain. PhD thesis, Wageningen Agricultural University, The Netherlands.] To validate the results, one wide-ranging field experiment was organized near Dourados, municipality in the Brazilian state of Mato Grosso do Sul, during the summer of 1999. The experiment involved simultaneous taking of satellite images and in-situ measurements. SEBAL results are shown and discussed. The obtained average errors are less than 4%, 6% and 7% for net surface radiation, surface heat and latent flux estimations, respectively, as compared to the in-situ measurements.     

Atmospheric optical depth and water vapour effects on the angular characteristics of surface reflectance in NOAA AVHRR

The effects of atmospheric optical depth and water vapour content on the bidirectional surface reflectance in channel 1 (visible) and channel 2 (near-infrared) of NOAA AVHRR have been analysed using a coupled surface atmosphere reflectance model. Two different cases of surface: (i) bare soil, and (ii) vegetation cover have been considered. In the case of bare soil, both the amplitude and angular distribution of the bidirectional reflectance of the surface are modified at satellite altitude due to scattering caused by atmospheric molecules and aerosols in the two channels and thereby, the directional properties of the surface are smoothed. Whereas, in the case of lawn, in channel 1, the angular variation of surface reflectance is enlarged together with a large augmentation in reflectance amplitude, and in channel 2, a small reduction in amplitude as well as a variation in angular distribution of reflectances are caused due to scattering particularly over large viewing angles and thereby, the directional variations are smoothed. In channel 1, atmospheric scattering reduces the contrast between the soil and vegetation and is very much significant for medium to high aerosol loadings. Atmospheric water vapour reduces the amplitude of the surface bidirectional reflectance without introducing any significant changes in angular distribution of the surface reflectance for both bare soil and vegetation canopy in channel 2.     

Mapping the regional extent of tropical forest regeneration stages in the Brazilian Legal Amazon using NOAA AVHRR data

The study aimed to map several stages of tropical forest regeneration across the Brazilian Legal Amazon using 1.1 km NOAA AVHRR data. Regenerating forest extent was defined using an unsupervised classification of AVHRR channels 1, 2 and 3 and the Global Environment Monitoring Index (GEMI). A method for discriminating four forest regeneration stages was then developed, based on relationships between AVHRR channels 1, 2 and 3 and forest age. This method was applied to AVHRR data to map forests associated with Stages I (early colonization phase, open canopy, < 5 years), II (closed, singlelayered canopy, 5-9 years), III (closed canopy with structural development, 9-20 years) and IV (closed multilayered canopy, > 20 years). The maps provided new regional estimates of regenerating forest for the Legal Amazon and indicated that, over the period 1991 to 1994, approximately 35.8% (157 973 km2) of the total deforested area of 440 186 km2 (estimated for 1992) supported regenerating forest, with 48% of these forests aged at less than 5 years. The study concluded that AVHRR data has an important role in mapping and monitoring tropical forest regeneration. The datasets generated provide valuable input to models of regional carbon flux. For example, Grace et al . (1995a, b) reported net annual CO2 absorption 8.5 2.0 moles m 2 for mature forests in south-west Amazonia suggesting     

NOAA AVHRR land surface albedo algorithm development

The primary objective of this research is to develop a surface albedo model for the National Oceanic and Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR). The primary test site is the Konza prairie, Kansas (U.S.A.), used by the International Satellite Land Surface Climatology Project (ISLSCP) in the First ISLSCP Field Experiment (FIFE). In this research, high spectral resolution field spectrometer data was analyzed to simulate AVHRR wavebands and to derive surface albedos. Development of a surface albedo algorithm was completed by analysing a combination of satellite, field spectrometer, and ancillary data. Estimated albedos from the field spectrometer data were compared to reference albedos derived using pyranometer data. Variations from surface anisotropy of reflected solar radiation were found to be the most significant albedo-related error. Additional error or sensitivity came from estimation of a shortwave mid-IR reflectance (1.3-4.0 mu m) using the AVHRR red and near-IR bands. Errors caused by the use of AVHRR spectral reflectance to estimate both a total visible (0.4-0.7 mu m) and near-IR (0.7-1.3 mu m) reflectance were small. The solar spectral integration, using the derived ultraviolet, visible, near-IR and SW mid-IR reflectivities, was not sensitive to many clear-sky changes in atmospheric properties and illumination conditions.     

Comparing the utility of microwave and thermal remote-sensing constraints in two-source energy balance modeling over an agricultural landscape

A two-source (soil + vegetation) energy balance model using microwave-derived near-surface soil moisture as a key boundary condition (TSM_SM) and another scheme using thermal-infrared (radiometric) surface temperature (TSM_TH) were applied to remote sensing data collected over a corn and soybean production region in central Iowa during the Soil Moisture Atmosphere Coupling Experiment (SMACEX)/Soil Moisture Experiment of 2002 (SMEX02). The TSM_SM was run using fields of near-surface soil moisture from microwave imagery collected by aircraft on six days during the experiment, yielding a root mean square difference (RMSD) between model estimates and tower measurements of net radiation (Rn) and soil heat flux (G) of approximately 20 W m^− 2, and 45 W m^− 2 for sensible (H) and latent heating (LE). Similar results for H and LE were obtained at landscape/regional scales when comparing model output with transect-average aircraft flux measurements. Flux predictions from the TSM_SM and TSM_TH models were compared for two days when both airborne microwave-derived soil moisture and radiometric surface temperature (T_R) data from Landsat were available. These two days represented contrasting conditions of moderate crop cover/dry soil surface and dense crop cover/moist soil surface. Surface temperature diagnosed by the TSM_SM was also compared directly to the remotely sensed T_R fields as an additional means of model validation. The TSM_SM performed well under moderate crop cover/dry soil surface conditions, but yielded larger discrepancies with observed heat fluxes and T_R under the high crop cover/moist soil surface conditions. Flux predictions from the thermal-based two-source model typically showed biases of opposite sign, suggesting that an average of the flux output from both modeling schemes may improve overall accuracy in flux predictions, in effect incorporating multiple remote-sensing constraints on canopy and soil fluxes.     

Determination of land surface spectral reflectances using Meteosat and NOAAA/AVHRR Shortwave Channel Data

Abstract

A method to derive surface spectral reflectances from currently available Meteosat geostationary and NOAA/AVHRR polar orbiting satellite data is described. Broadband reflectance was derived from Meteosat measurements while NOAA/AVHRR vegetation index provided a spectral weighting which enabled the spectral reflectances on either side of 0-7 μm to be estimated. The method takes into account satellite calibrations, viewing geometry, and correction of some atmospheric effects. Conversion from narrow-band to broadband reflectances is discussed. The method was applied to a month of data to obtain the surface spectral reflectances of Africa which are compared with some data sets used by climate modellers, in order to assess them and to monitor their seasonal and interannual changes on a global scale.     

Quantitative study of net radiation from MODIS data in the lower boundary layer in Poyang Lake area of Jiangxi Province,China

This paper focuses on quantitative calculation of longwave radiation and shortwave radiation from MODIS data in the Poyang Lake area of Jiangxi Province, China. The sum of the net longwave radiation and the shortwave radiation is the net radiation. These parameters are critical for the study of energy exchange in the lower boundary layer on land surface. Two of the most important factors for the retrieval of longwave radiation are the land surface temperature and emissivity. In this paper, the land surface temperature and emissivity were calculated from MODIS data using the regional self‐iterative split‐window method. The most important factor in the determination of the shortwave radiation is Earth surface albedo. The spectral reflectance and surface albedo were derived from MODIS data using the Synergy of TERRA and AQUA MODIS data (SYNTAM) algorithm. The net shortwave and longwave radiation were calculated and compared with the in situ measurement data. Our results indicate that the methods for quantitative calculation of net longwave radiation, shortwave radiation and net radiation from MODIS data can have a good accuracy. The relative errors are between 2.1% to 9.72% for longwave radiation, 0.15% to 10.48% for shortwave radiation and 0.64% to 13.7% for net radiation. We can conclude that a good accuracy can be achieved for deriving longwave radiation, shortwave radiation and net radiation, which are helpful for heat exchange, environmental, hydrology and ecology research in land areas.