Estimating net surface longwave radiation from net surface shortwave radiation for cloudy skies

This work addresses the estimation of net surface longwave radiation (NSLR) from net surface shortwave radiation (NSSR) by analysing the Surface Radiation Budget Network (SURFRAD) radiation data under cloudy conditions. A general model is developed to estimate NSLR from the NSSR for cloudy skies with a root mean square error (RMSE) of 23.16 W m^?2 compared with in situ data. The model is applied to AmeriFlux data. The results show that the mean error and RMSE are –2.31 W m^?2 and 29.25 W m^?2, respectively, compared with the measurement of AmeriFlux. To examine the significance of the influence of seasons on the estimated NSLR, the model is proposed as a function of seasonal variation. The results show a slight improvement for winter and spring, whereas a larger error is found for autumn compared with the results obtained by the general model. The influences of land cover and elevation on the model are also investigated. The results show that the model is slightly sensitive to the normalized difference vegetation index (NDVI) and the elevation. The RMSE of the model decreases from 23.16 W m^?2 to 21.04 W m^?2 when the NDVI and the elevation are considered in the model.     

Estimation of net surface shortwave radiation from MODIS data

Net surface shortwave radiation (NSSR) is a key quantity for the estimation of surface energy budget and is used in various land-surface models. In this article, two different methodologies, including three empirical algorithms and one advanced simplified theoretical algorithm for estimating instantaneous NSSR from Moderate Resolution Imaging Spectroradiometer (MODIS) data are explored and summarized. An advanced simplified theoretical algorithm is developed by combining two simplified radiative-transfer models with various MODIS atmosphere and land products. To comprehensively evaluate these algorithms, ground measurements from seven stations widely distributed in different climatic regions of China are used. The results indicate that under clear-sky conditions, the three empirical algorithms present appreciable difference in accuracy, while under cloudy skies, they produce similar, but not very good, predictions. Compared with these empirical methods, the simplified theoretical algorithm we adopt can significantly improve accuracy. The root mean square difference (RMSD) yielded by this algorithm is approximately 54 W?m^?2 under clear skies and 83 W?m^?2 under cloudy skies, respectively. Since the utility of instantaneous NSSR estimates is limited compared to that of the daily average value, a simple scheme to acquire the daily average NSSR is established, which is based on instantaneous estimations from two satellite MODIS sensors (Terra: AM and Aqua: PM), and the daily average NSSR over the Beijing area is also mapped.     

Evaluating one- and two-source energy balance models in estimating surface evapotranspiration from Landsat-derived surface temperature and field measurements

This article compares one- and two-source energy balance (OSEB and TSEB) models in the estimates of surface energy components using Landsat imagery and surface measurements acquired from an experimental field at Yucheng Station in Northern China. Compared to surface measurements, similar performance between the TSEB and OSEB models has been observed for estimated surface net radiation and soil heat flux. The root mean square difference (RMSD) is within 14–39 W m^?2 in both the TSEB and OSEB models. The residual energy (E _R) correction method yields the best agreement in comparisons of the sensible (H) and latent (LE) heat fluxes estimated using both the TSEB and OSEB models to the eddy covariance (EC) system measurements. The TSEB model is shown to greatly outperform the OSEB model in reproducing surface H and LE measurements. Cirrus clouds are likely responsible for the surface temperature retrieved from the enhanced thematic mapper plus (ETM+) sensor being lower than air temperature on days of the year (DOYs) 178 and 218 of 2009. This atmospheric stability is contrary to the unstable atmosphere that the EC measurements observe. If data on these two days are excluded and the E _R correction method is applied, when comparing the estimated H and LE to the EC measurements, RMSD is within 55 W m^?2 in the TSEB model and is larger than 97 W m^?2 in the OSEB model.     

Following a site-specific secondary succession in the Amazon using the Landsat CDR product and field inventory data

Secondary forests cover large areas and are strong carbon sinks in tropical regions. They are important for ecosystem functioning, biodiversity conservation, watershed protection, and recovery of soil fertility. In this study, we used the Surface Reflectance Climate Data Record (CDR) product from 16 Thematic Mapper (TM)/Landsat-5 images (1984–2010) to continuously track the secondary succession (SS) of a forest following land abandonment in 1980. Changes in canopy structure and floristic composition were analysed using data from four field inventories (1995, 2002, 2007, and 2012). To characterize variations in brightness, greenness, spectral reflectance, and shadows with the natural regeneration of vegetation, we applied tasselled cap transformations, principal component analysis (PCA), and linear spectral mixture models to the TM datasets. Shade fractions were plotted over time and correlated with the enhanced vegetation index (EVI) and the normalized difference vegetation index (NDVI). Because image texture may reflect the variability of the successional process, eight co-occurrence-based filter metrics were calculated for selected TM bands and plotted as a function of time since abandonment. The successional forest was compared to a nearby primary reference forest (PF) and had differences in the spectral and textural means evaluated using analysis of variance (ANOVA). The results showed increases of 35% and 10.4% over time in basal area and tree height, respectively. Species richness within the assemblage of sampling units increased from 14 to 71 between 1995 and 2012, and this trend was also confirmed using an individual-based rarefaction analysis. Species richness in 2012 was still lower than that observed in the PF site, which presented greater amounts of aboveground biomass (336.4 ± 17.0 ton ha^?1 for PF versus 98.5 ± 21.4 ton ha^?1 for SS in 2012). Brightness and greenness tasselled cap differences between the SS and PF rapidly decreased from 1984 (SS at the age of 4 years) to 1991 (age of 11 years). Brightness also decreased from 1997 to 2003, as indicated by PC1 scores and surface reflectance of the TM bands 4 (near infrared) and 5 (shortwave infrared). Spectral mixture shade fraction increased from young to old successional stages with strata composition and canopy structure development, whereas NDVI and EVI decreased over time. Because EVI was strongly dependent on near infrared reflectance (r = + 0.96), it was also much more strongly correlated with the shade fraction (r = ?0.93) than NDVI. Except for the image texture mean that decreased from young to old successional stages in TM bands 4 and 5, no clear trend was observed in the remaining texture metrics over the time period of vegetation regeneration. Overall, due to structural-floristic and spectral/textural differences with the PF, the SS site was still distinguishable using Landsat data 30 years after land abandonment. Most of the spectral metric means between PF and SS were significantly different over time at 0.01 significance level, as indicated by ANOVA.     

Comparison of two types of forest disturbance using multitemporal Landsat TM/ETM+ imagery and field vegetation data

Forest disturbances influence many landscape processes, including changes in microclimate, hydrology, and soil erosion. We analyzed the spectral response and temporal progress of two types of disturbances of spruce forest (bark beetle outbreak and clear-cuts) in the central part of Šumava Mountains at the border between the Czech Republic and Germany, Central Europe. The bark beetle (Ips typographus [L.]) outbreak in this region in the last 20 years resulted in regional-scale spruce forest decay. Clear-cutting was done here to prevent further bark-beetle propagation in the buffer zones.The aim of the study is to identify the differences in spectral response between the two types of forest disturbances and their temporal dynamics. General trends were analyzed throughout the study area, with sampled disturbance areas selected to assess the relationship between field vegetation data and their spectral response. Thirteen Landsat TM/ETM+ scenes from 1985 to 2007 were used for the assessment. The following spectral indices were estimated: NDMI, Tasseled Cap (Brightness, Greenness, Wetness), DI, and DI′. The DI′, Wetness, and Brightness indices show the highest sensitivity to forest disturbance for both disturbance types (clear-cuts and bark beetle outbreak). The multitemporal analysis distinguished three different stages of development. The highest spectral differences between the clear-cuts and the bark beetle disturbances were found in the period between 1996 and 2004 with increased levels of forest disturbance (repeated measures ANOVA, Scheffé post hoc test; p ≤ 0.05). Clear-cut disturbance resulted in significantly higher spectral differences from the original forest and occurred as a more discrete event in comparison to bark beetle outbreak.     

Bare surface soil moisture retrieval from the synergistic use of optical and thermal infrared data

Land surface soil moisture (SSM) is crucial to research and applications in hydrology, ecology, and meteorology. To develop a SSM retrieval model for bare soil, an elliptical relationship between diurnal cycles of land surface temperature (LST) and net surface shortwave radiation (NSSR) is described and further verified using data that were simulated with the Common Land Model (CoLM) simulation. In addition, with a stepwise linear regression, a multi-linear model is developed to retrieve daily average SSM in terms of the ellipse parameters x₀ (horizontal coordinate of the ellipse centre), y₀ (vertical coordinate of the ellipse centre), a (semi-major axis), and θ (rotation angle), which were acquired from the elliptical relationship. The retrieval model for daily average SSM proved to be independent of soil type for a given atmospheric condition. Compared with the simulated daily average SSM, the proposed model was found to be of higher accuracy. For eight cloud-free days, the root mean square error (RMSE) ranged from 0.003 to 0.031 m³ m^?3, while the coefficient of determination (R²) ranged from 0.852 to 0.999. Finally, comparison and validation were conducted using simulated and measured data, respectively. The results indicated that the proposed model showed better accuracy than a recently reported model using simulated data. A simple calibration decreased RMSE from 0.088 m³ m^?3 to 0.051 m³ m^?3 at Bondville Companion site, and from 0.126 m³ m^?3 to 0.071 m³ m^?3 at the Bondville site. Coefficients of determination R² = 0.548 and 0.445 were achieved between the estimated daily average SSM and the measured values at the two sites, respectively. This paper suggests a promising avenue for retrieving regional SSM using LST and NSSR derived from geostationary satellites in future developments.     

The relationship of surface air temperature,heat balance at the surface,and radiative balance at the top of atmosphere over the Asian territory of Russia using reanalysis and remote-sensing data

In this study over the Asian territory of Russia (ATR) (45° N–80° N, 60° E–180° E) for the period of 1979–2010 the temporal variability of the surface air temperature field was investigated. There are several climatic factors which can influence temperature variability including radiative balance at the top of atmosphere (TOA), heat balance at the surface, total cloud cover, and large-scale atmospheric circulation in the Northern Hemisphere. The contribution of these factors to temperature variability is also investigated. It was found that during the past decade, over the ATR, the process of warming prevails mainly in the warm season, but in the cold season it is either not as marked or there is cooling instead. In the winter season there is a positive relationship between temperature anomalies and anomalies in cloudiness, effective radiation, and the Arctic Oscillation (AO) index. During the same period, a negative relationship between anomalies of temperature and anomalies of net radiation at the TOA, net shortwave radiation at the surface, and the Scandinavian (SCAND) index was observed. In the summer season, the relationship between temperature and cloudiness becomes negative and the relationship between temperature and atmospheric circulation indices decreases. For the period 2001–2010, radiative fluxes obtained from reanalysis data sets Japanese Reanalysis Data (JRA-25) and Modern Era-Retrospective Analysis for Research and Applications (MERRA) were compared to satellite remotely sensed data project Clouds and the Earth’s Radiant Energy System (CERES). It was found that there is a good agreement between estimates of the net radiation at the TOA calculated using reanalysis data and satellite data: the difference is about 1.5 W m^?2 and the correlation coefficient is more than 0.7. As for the comparison of radiative fluxes estimates at the surface for clear sky, there is less difference between MERRA and CERES. So, during the period 2001–2010 the relation between atmospheric circulation and surface air temperature variability increased in winter months. Obtained regression models allow us to describe from 27% to 82% of temperature variability in different months if we take into account both circulation and radiative factors.     

Scaling of impervious surface area and vegetation as indicators to urban land surface temperature using satellite data

Vegetation and impervious surface as indicators of urban land surface temperature (LST) across a spatial resolution from 30 to 960 m were investigated in this study. Enhanced thematic mapper plus (ETM+) data were used to retrieve LST in Nanjing, China. A land cover map was generated using a decision tree method from IKONOS imagery. Taking the normalized difference vegetation index (NDVI) and percent vegetation area (V) to present vegetated cover, and the normalized difference building index (NDBI) and percent impervious surface area (I) to present impervious surface, the correlation coefficients and linear regression models between the LST and the indicators were simulated. Comparison results indicated that vegetation had stronger correlation with the LST than the impervious surface at 30 and 60 m, a similar magnitude of correlation at 120 and 240 m, and a much lower correlation at 480 and 960 m. In total, the impervious surface area was a slightly better indicator to the LST than the vegetation because all of the correlation coefficients were relatively high (>0.5000) across the spatial resolution from 30 to 960 m. The indicators of LST, V and I are slightly better than the NDVI and NDBI, respectively, based on the correlation coefficients between the LST and the four indices. The strongest correlation of the LST and vegetation at the resolution of 120 m, and the strongest correlation between the LST and impervious surface at 120, 480 and 960 m, denoted the operational scales of LST variations.     

Satellite observations of the aerosol direct radiative effect during a pollution episode in Nanjing,China, using high-resolution HJ-1 CCD imagery

The charge-coupled device imageries from the Chinese environmental satellites (HJ-1) with 30 m spatial resolution are used in this study to investigate the shortwave aerosol direct radiative effect (ADRE) during a pollution episode in March 2013 over Nanjing City. The Deep Blue algorithm is used to retrieve the aerosol optical depth (AOD) and the results are then validated using ground-based data. The mean values of the AOD in Nanjing at 550 nm are 0.41, 0.54, and 0.79 on 3, 4, and 7 March 2013, respectively, with fitting degree 0.796. The spatial aerosol distribution indicates that the pollution is induced by local emission, accumulated as a result of suitable weather conditions, and then spread by the wind. The ADRE results at the surface and at the top of the atmosphere (TOA) are then calculated using radiative transfer model. The mean values and standard deviations of the ADRE on 3, 4, and 7 March are ?75.6 ± 21.3, ?98.8 ± 16.6, and ?138.1 ± 26.0 W m^?2 at the surface and ?3.7 ± 3.8, ?5.2 ± 4.6, and ?8.1 ± 5.9 W m^?2 at the TOA, respectively. The results show that although aerosols can cause warming at the TOA over a highly reflectivity surface, the total radiative effect corresponds to the cooling of both the Earth-atmosphere system and the surface at the expense of heating the atmosphere.     

Water body delineation using index composition and HIS transformation

The use of satellite data for mapping water bodies is important for environmental management. Previous approaches exhibit limited applicability in southeastern China, given its complex and heterogeneous landscapes as well as the difficulty to obtain cloud-free images. To overcome these problems, we proposed an approach using index composition and HIS (hue, intensity and saturation) transformation. First, a colour image was generated using three indices: the Normalized Difference Built-up Index (NDBI), the Normalized Difference Vegetation Index (NDVI) and the Modified Normalized Difference Water Index (MNDWI). Then, HIS transformation was employed to extract water bodies and remove hill shadows. Another colour image, composed by Landsat TM (Thematic Mapper) 4, NDVI, TM1 and HIS transformation, was further applied to separate water bodies from residual shadows. This approach was tested and verified with Landsat TM images in the Tiaoxi watershed, southeastern China. The results indicated the high accuracy and promising applicability of this new approach for fragmented landscapes, given its insensitivity to seasonal and subjective factors.     

Sensitivity study of soil moisture on the temporal evolution of surface temperature over bare surfaces

Land surface soil moisture (SSM) is a fundamental variable in the hydrological cycle and is an important parameter in investigations on water and energy balances at the Earth's surface. Many efforts have been made to derive SSM from remotely sensed thermal infrared data. Using the Noah land surface model (LSM) and the Gaussian emulation machine for sensitivity analysis (GEM-SA) software, a sensitivity study was conducted for bare soil to investigate the interrelationship between the evolution of land surface temperature (LST) and SSM. Based on the diurnal cycles of LST and net surface shortwave radiation, eight parameters intuitively related to SSM were defined, and a sensitivity analysis (SA) was performed in the presence and absence of atmospheric variation. The results provided insight into the relationships between the eight parameters and various environmental factors such as soil physical parameters, soil moisture, albedo, and atmospheric parameters. For instance, the results suggested that the surface air temperature had a significant effect on the LST, especially the maximum, minimum, and average daytime temperatures. For a given atmospheric forcing data set, the LST rising rate normalized by the difference in the net surface shortwave radiation during the mid-morning (T _N) was the parameter most sensitive to the SSM, contributing 80.72% to the total variance. In addition, the time at which the daily maximum temperature occurred (t _d), the daily minimum temperature, and the LST nocturnal decay coefficient were strongly related to the soil type. Using a linear combination of T _N and t _d, a method was proposed to retrieve the SSM, and the coefficients of the linear model were found to be independent of the soil type for a given atmospheric condition. Compared with the actual SSM values used in the Noah LSM simulation, the root mean square error (RMSE) of the SSM retrieved from our proposed method was within 0.04 m³ m^?3 for all the 20 clear days evaluated in the present study.     

Estimation of net radiation from the MODIS data under all sky conditions: Southern Great Plains case study

Net radiation is a key component in the surface radiation budget. Numerous studies have developed frameworks to estimate net radiation or its components (upwelling or downwelling longwave and/or shortwave radiation) from remote sensing data for clear sky conditions. Application of existing methodologies to estimate net radiation for cloudy sky conditions from remote sensing sensors remains a significant challenge. In this paper, we present a framework to estimate instantaneous and daily average net radiation under all sky conditions from using the data from the MODerate Resolution Imaging Spectroradiometer (MODIS), onboard from the Terra satellites. Bisht et al. (2005) methodology is used for the clear sky portion of the MODIS overpass; while for cloudy portion of the MODIS overpass an extension of Bisht et al. (2005) methodology is applied. The extension of Bisht et al. (2005) methodology utilizes the MODIS cloud data product (MOD06_L2) for cloud top temperature, cloud fraction, cloud emissivity, cloud optical thickness and land surface temperature for cloudy days. The methodology is applied over the Southern Great Plains (SGP) for a time period covering all seasons of 2006. During the MODIS-Terra overpasses in 2006 over the SGP, only 24% of day-overpasses and 9% of night-overpasses had 75% or more of the study region as cloud free. Thus, this proposed study is applicable to a large portion of the MODIS-Terra overpasses. The root mean square errors (RMSE) of instantaneous and daily average net radiation estimated under cloudy conditions using the MOD06_L2 product, comparing to ground-based measurements are 37 W m^− 2 and 38 W m^− 2, respectively. The strength of the proposed methodology is that it can rely exclusively on remote sensing data in the absence of ancillary ground observations, thus it has a potential to estimate surface energy budget globally.     

Effects of implementing satellite observed aerosols into a mesoscale atmosphere model

This study investigated the performance of the fifth-generation Pennsylvania State University–National Center for Atmospheric Research (PSU-NCAR) Mesoscale Model (MM5) in calculating the aerosol forcing on cloud cover, incoming surface solar radiation, and near-surface air temperature via the implementation of aerosol optical depth in the shortwave radiation parameterization. MM5 simulations with and without aerosol data are performed in the periods of 6–7 August 2003 and 19–21 September 2003 during which strong aerosol forcing was observed with Moderate Resolution Imaging Spectroradiometer (MODIS) data in the mid-Atlantic region. Both periods clearly showed that aerosols had a direct negative effect on surface solar radiation through aerosol scattering. For example, every 0.1 change in MODIS aerosol optical thickness (AOT) results in 44 and 59?W?m^?2 decreases in surface solar radiation for the first and second periods, respectively. A magnitude of 0.1 increment in MODIS AOT reduces air temperature 0.36 and 0.56?K for the first and second periods, respectively. Comparisons with satellite-derived surface solar radiation retrievals showed that aerosol implementation in MM5 consistently showed better incoming surface solar radiation than that of the non-aerosol case. This helps to reduce uncertainties related to the radiation–cloud–aerosol interaction in numerical weather modelling systems.     

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.     

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.     

Spatio-temporal coupling of land-surface and energy balance parameters with monsoon rainfall using remote-sensing technology

This research paper focuses on the spatio-temporal coupling of monsoon rainfall with land-surface and energy balance parameters, which are important for understanding hydrological, climatological, and agricultural aspects at local, regional, and global scales. The dynamics of land-surface and energy balance parameters influence summer monsoon over India. Time scales of the land-surface response to monsoon forcing are different for different land-surface conditions due to different physical processes governing the land-surface–atmosphere exchange through energy balance components. A synergy of satellite data from the Moderate Resolution Imaging Spectroradiometer (MODIS) (0.05° × 0.05°) for obtaining land-surface and energy balance parameters, and the Atmospheric Infrared Sounder (AIRS) (1° × 1°) for obtaining atmospheric parameter and gridded rainfall data (1° × 1°) from the Indian Meteorological Department (IMD) during June to September for three consecutive years (2009–2011) representing low to normal rainfall, were used to develop a coupling model in the spatio-temporal domain. Surface energy fluxes were estimated using a surface energy balance model by partitioning available energy at the surface into latent heat flux (LE) and sensible heat flux (H) through the evaporative fraction (EF) concept of a 2D land-surface temperature (LST)-albedo scatter plot. The coupling models were based on statistical methods developed at both temporal and spatial scales to explain the linking of various parameters with monsoon rainfall. A significant positive relationship was obtained between rainfall and land-surface parameters such as normalized difference vegetation indices (NDVIs), and soil wetness/energy balance parameters such as LE and EF, whereas a strong negative relationship was obtained between rainfall and surface radiation parameters (LST and albedo)/energy balance parameters such as soil heat flux (G) and net radiation (R_n). This approach has demonstrated its simplicity with remote sensing technology and could identify ‘at risk’ regions at spatio-temporal scales based on coupling models.     

Estimating aboveground tree biomass and leaf area index in a mountain birch forest using ASTER satellite data

Biomass and leaf area index (LAI) are important variables in many ecological and environmental applications. In this study, the suitability of visible to shortwave infrared advanced spaceborne thermal emission and reflection radiometer (ASTER) data for estimating aboveground tree and LAI in the treeline mountain birch forests was tested in northernmost Finland. The biomass and LAI of the 128 plots were surveyed, and the empirical relationships between forest variables and ASTER data were studied using correlation analysis and linear and non‐linear regression analysis. The studied spectral features also included several spectral vegetation indices (SVI) and canonical correlation analysis (CCA) transformed reflectances. The results indicate significant relationships between the biomass, LAI and ASTER data. The variables were predicted most accurately by CCA transformed reflectances, the approach corresponding to the multiple regression analysis. The lowest RMSEs were 3.45 t ha^?1 (41.0%) and 0.28 m²m^?2 (37.0%) for biomass and LAI respectively. The red band was the band with the strongest correlation against the biomass and LAI. SR and NDVI were the SVIs with the strongest linear and non‐linear relationships. Although the best models explained about 85% of the variation in biomass and LAI, the undergrowth vegetation and background reflectance are likely to affect the observed relationships.     

Estimation of high-spatial resolution clear-sky longwave downward and net radiation over land surfaces from MODIS data

Surface downwelling longwave radiation (LWDN) and surface net longwave radiation (LWNT) are two components in the surface radiation budget. In this study, we developed new linear and nonlinear models using a hybrid method to derive instantaneous clear-sky LWDN over land from the Moderate Resolution Imaging Spectroradiometer (MODIS) TOA radiance at 1 km spatial resolution. The hybrid method is based on extensive radiative transfer simulation (physical) and statistical analysis (statistical). Linear and nonlinear models were derived at 5 sensor view zenith angles (0°, 15°, 30°, 45°, and 60°) to estimated LWDN using channels 27-29 and 31-34. Separate models were developed for daytime and nighttime observations. Surface pressure effect was considered by incorporating elevation in the models. The linear LWDN models account for more than 92% of variations of the simulated data sets, with standard errors less than 16.27 W/m² for all sensor view zenith angles. The nonlinear LWDN models explain more than 93% of variations, with standard errors less than 15.20 W/m². The linear and nonlinear LWDN models were applied to both Terra and Aqua TOA radiance and validated using ground data from six SURFRAD sites. The nonlinear models outperform the linear models at five sites. The averaged root mean squared errors (RMSE) of the nonlinear models are 17.60 W/m² (Terra) and 16.17 W/m² (Aqua), with averaged RMSE ~ 2.5 W/m² smaller than that of the linear models. LWNT was estimated using the nonlinear LWDN models and the artificial neural network (ANN) model method that predicts surface upwelling longwave radiation. LWNT was also validated using the same six SURFRAD sites. The averaged RMSEs are 17.72 (Terra) and 16.88 (Aqua) W/m²; the averaged biases are − 2.08 (Terra) and 1.99 (Aqua) W/m². The LWNT RMSEs are less than 20 W/m² for both Terra and Aqua observations at all sites.     

An intercomparison of three remote sensing-based energy balance models using Large Aperture Scintillometer measurements over a wheat-corn production region

This paper compares three remote sensing-based models for estimating evapotranspiration (ET), namely the Surface Energy Balance System (SEBS), the Two-Source Energy Balance (TSEB) model, and the surface temperature-vegetation index Triangle (TVT). The models used as input MODIS/TERRA products and ground measurements collected during the wheat and corn growth period in a subhumid climate at a measurement station in Yucheng, China. MODIS land surface temperature (LST) and leaf area index (LAI) products, corrected using ground-truth observations, were used in the three models. The TSEB model output of sensible (H) and latent (LE) heat fluxes were in good agreement with Large Aperture Scintillometer (LAS)-measured H and LE derived by residual (RMSD < 45 W/m²). Reasonable agreement was also obtained with the SEBS model output yielding RMSD for H of ~ 40 W/m² and LE ~ 55 W/m². However, the TVT model output resulted in poor agreement with the LAS-estimated H and LE with RMSD-values > 110 W/m². Using the uncorrected MODIS LST and LAI products resulted in a deterioration of the agreement in H and LE with LAS-estimated values for both the TSEB and SEBS models, whereas TVT performance improved marginally. These results indicate that the TSEB model yielded the closest agreement with the LAS-estimated fluxes using either the corrected or uncorrected MODIS inputs (LST and LAI). The SEBS model also computed reasonable H and LE values but was significantly more sensitive to errors in MODIS LST and LAI inputs than the TSEB model. In the TVT model, output of H and LE was unacceptable in either scenario of MODIS input which was attributable to errors in selection of the dry edge. With the TVT method, accurate determination of the dry edge end member is critical in regional ET estimation, but for humid and subhumid regions this end member may often be quite difficult to identify or encompass within a satellite scene.     

区域遥感蒸散发模型方法研究

遥感技术为大面积区域陆面水分蒸散发量估算提供了一种新的手段,分析了国内外应用较好的遥感监测陆面水分蒸发的几种方法,主要包括地表热量平衡方法、互补相关陆面蒸散发、Penman-Montieth模型和区域蒸散发的气候学方法,并给出了可供参考的地面参数(地面反照率、NDVI、陆面温度等)的遥感获取方法,还阐述了各种方法的特点及彼此之间的联系。