View angle effects in the radiometric measurement of plant canopy temperatures

The thermal infrared sensor response from a wheat canopy was extremely non-Lambertian because of spatial variations in energy flow processes; the effective radiant temperature of the sensor varied as much as 13°C with changing view angle. This variation of sensor response was accurately quantified (root-mean-square of deviations between theoretical and measured responses reduced to 1.1°C) as a function of vegetation canopy geometry, vertical temperature distribution of canopy components, and sensor view angle. The results have important implications for optimizing sensor view angles for remote sensing missions.     

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

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

Comparative study of suits and sail canopy reflectance models

In order to fully exploit the potential of remote sensing from aircraft and spacecraft altitude to map vegetation and estimate key agronomic parameters such as leaf area index (LAI) and biomass, the relationships between the canopy reflectance and properties of canopy elements must be well understood. A number of canopy reflectance models exist in the literature. Much of the model(s) verifications have been done primarily with observations from nadir direction. This was partly due to near nadir view observations of the Landsat series of satellites and partly due to the convenience of such measurements. The most significant development in these models, their ability to predict angular reflectance properties could not, however, be evaluated. The Laboratory for Applications of Remote Sensing, Purdue University, with strong support from modeling community, collected two excellent data sets, one on corn and one on soybeans, that contain the full hemisphere of off-nadir reflectance measurements. These data sets have been used to verify the multilayer one-component (leaves) Suits and SAIL canopy reflectance models. Software to evaluate multilayer multicomponent canopies was developed, but could not be evaluated because of the lack of information on stems. This evaluation suggests that these and similar other models have generic deficiency. The SAIL model, because of a more realistic inclusion of the leaf angle distribution, is in better agreement with observations than the Suits model. Further improvement and additional evaluation of these models is needed.     

Canopy directional emissivity: Comparison between models

Land surface temperature plays an important role in many environmental studies, as for example the estimation of heat fluxes and evapotranspiration. In order to obtain accurate values of land surface temperature, atmospheric, emissivity and angular effects should be corrected. This paper focuses on the analysis of the angular variation of canopy emissivity, which is an important variable that has to be known to correct surface radiances and obtain surface temperatures. Emissivity is also involved in the atmospheric corrections since it appears in the reflected downwelling atmospheric term. For this purpose, five different methods for simulating directional canopy emissivity have been analyzed and compared. The five methods are composed of two geometrical models, developed by Sobrino et al. [J. A. Sobrino, V. Caselles, & F. Becker (1990). Significance of the remotely sensed thermal infrared measurements obtained over a citrus orchard. ISPRS Photogrammetric Engineering and Remote Sensing 44, 343-354] and Snyder and Wan [W. C. Snyder & Z. Wan, (1998). BRDF models to predict spectral reflectance and emissivity in the thermal infrared. IEEE Transactions on Geoscience and Remote Sensing 36, 214-225], in which the vegetation is considered as an opaque medium, and three are based on radiative transfer models, developed by François et al. [C. François, C. Ottlé, & L. Prévot (1997). Analytical parametrisation of canopy emissivity and directional radiance in the thermal infrared: Application on the retrieval of soil and foliage temperatures using two directional measurements. International Journal of Remote Sensing 12, 2587-2621], Snyder and Wan [W. C. Snyder & Z. Wan (1998). BRDF models to predict spectral reflectance and emissivity in the thermal infrared. IEEE Transactions on Geoscience and Remote Sensing 36, 214-225.] and Verhoef et al. [W. Verhoef, Q. Xiao, L. Jia, & Z. Su (submitted for publication). Extension of SAIL to a 4-component optical-thermal radiative transfer model simulating thermodynamically heterogenous canopies. IEEE Transactions on Geoscience and Remote Sensing], in which the vegetation is considered as a turbid medium. Over surfaces with sparse and low vegetation cover, high angular variations of canopy emissivity are obtained, with differences between at-nadir view and 80° of 0.03. Over fully vegetated surfaces angular effects on emissivity are negligible when radiative transfer models are applied, so in these situations the angular variations on emissivity are not critical on the retrieved land surface temperature from remote sensing data. Angular variations on emissivity are lower when the emissivity of the soil and the emissivity of the vegetation are closer. All the models considered assume Lambertian behaviour for the soil and the leaves. This assumption is also discussed, showing a different behaviour of directional canopy emissivity when a non-Lambertian soil is considered.     

Canopy blockage and scattering effects on apparent soil spectral reflectance and its consequence in spectral mixture analysis of vegetated surfaces

Remote sensing technique has become the most efficient and common approach to estimate surface vegetation cover. Among various remote sensing algorithms, spectral mixture analysis (SMA) is the most common approach to obtain sub‐pixel surface coverage. In the SMA, spectral endmembers (the number of endmembers may vary), with invariant spectral reflectance across the whole image, are needed to conduct the mixture procedure. Although the nonlinear effect in quantifying vegetation spectral reflectance was noticed and sometimes addressed in the SMA analysis, the nonlinear effect in soil spectral reflectance is seldom discussed in the literature. In this paper, we investigate the effects of vegetation canopy on the inter‐canopy soil spectral reflectance via mathematical modelling and field measurements. We identify two mechanisms that lead to the difference between remotely sensed apparent soil spectral reflectance and actual soil spectral reflectance. One is a canopy blockage effect, leading to a reduced apparent soil spectral reflectance. The other is a canopy scattering effect, leading to an increased apparent soil spectral reflectance. Without correction, the first (second) mechanism causes an overestimated (underestimated) areal coverage of the low‐spectral‐reflectance endmember. The overall effect of canopy to soil, however, tends to overestimate fractional vegetation cover due to the relative significance of the canopy blockage effect, even though the two mechanisms vary with spectral wavelengths and spectral difference between different vegetation and soil. For the SMA of vegetated surface using multiple‐spectral remote sensing imagery (e.g., LandSat), it is recommended that infrared bands of low vegetation spectral reflectance (e.g. band 7) be first considered; if both visible and infrared bands are used, combination of bands 3, 4, and 5 is appropriate, while use of all six bands could overestimate fraction vegetation cover.     

Estimation of canopy parameters for inhomogeneous vegetation canopies from reflectance data. II. Estimation of leaf area index and percentage of ground cover for row canopies

The canopy reflectance (CR) model for row-planted vegetation proposed earlier has been tested for soybean canopies in three different stages of growth and for corn canopies at early and full growth stages. The model fits the field-measured bidirectional CR data quite well. It is shown that, by inverting this model, one could estimate the leaf area index as well as the percentage of ground cover quite accurately from measured canopy reflectances.     

Use of coupled canopy structure dynamic and radiative transfer models to estimate biophysical canopy characteristics

Leaf area index (LAI) is a key variable for the understanding of several eco-physiological processes within a vegetation canopy. The LAI could thus provide vital information for the management of the environment and agricultural practices when estimated continuously over time and space thanks to remote sensing sensors.This study proposed a method to estimate LAI spatial and temporal variation based on multi-temporal remote sensing observations processed using a simple semi-mechanistic canopy structure dynamic model (CSDM) coupled with a radiative transfer model (RTM). The CSDM described the temporal evolution of the LAI as function of the accumulated daily air temperature as measured from classical ground meteorological stations.The retrieval performances were evaluated for two different data sets: first, a data set simulated by the RTM but taking into account realistic measurement conditions and uncertainties resulting from different error sources; second, an experimental data set acquired over maize crops the Blue Earth City area (USA) in 1998. Results showed that the proposed approach improved significantly the retrieval performances for LAI mainly by smoothing the residual errors associated to each individual observation. In addition it provides a way to describe in a continuous manner the LAI time course from a limited number of observations during the growth cycle.     

Analytical parameterization of canopy directional emissivity and directional radiance in the thermal infrared. Application on the retrieval of soil and foliage temperatures using two directional measurements

This work is aimed at deriving canopy component (soil and foliage) temperatures from remote sensing measurements. A simulation study above sparse, partial and dense vegetation canopies has been performed to improve the knowledge of the behaviour of the composite radiative temperature and emissivity. Canopy structural parameters have been introduced in the analytical parameterization of the directional canopy emissivity and directional canopy radiance:namely, the leaf area index (LAI), directional gap fraction and angular cavity effect coefficient. The parameterization has been physically defined allowing its extension to a wide range of Leaf Inclination Distribution Functions (LIDF). When single values are used as leaves and soil temperatures, they prove to be retrieved with insignificant errors from two directional measurements of the canopy radiance (namely at 0 and 55 from nadir), provided that the canopy structure parameters are known. A sensitivity study to the different parameters shows the great importance of the accuracy on LAI estimation (an accuracy of 10 per cent is required to retrieve the leaves temperature with an accuracy better than 0.5 degK, the same requirement being 5 per cent for the retrieval of soil temperature). The radiometric noise is important too, but its effects may be limited by using very different angles for the measurements: for 0 and 55, the effect of a Gaussian noise (NEDeltaT 0.05deg K) is lower than 0.5degK on the retrieved soil and foliage temperatures). Uncertainties on the leaf and soil emissivities (Delta epsilon 0.01) cause little errors in the retrieval (lower than 0.5degK). If the inclination dependence of the leaves temperature is considered, a 1 degK error is observed in the retrieved soil and foliage temperatures. This error is due to the fact that the effective foliage temperature varies with the view angle (a few 10 -1 deg K at 55 ), which implies errors in the inversion scheme. This effect may be corrected for by using an angular corrective term delta depending only on the off-nadir angle used.     

Soil water and plant canopy effects on remotely measured surface temperatures

Abstract.

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

Coupling forest canopy and understory reflectance in the Arctic latitudes of Finland

The Arctic region is predicted to experience considerable climatic and environmental changes as the global atmospheric CO₂ increases. Growing awareness of the role of tundra and taiga ecosystems and their transition zone in the climate change process has resulted in a recent increase in remote sensing studies focusing on the Arctic latitudes. Remote sensing of biophysical properties of the canopy layer in the forested part of the region is often, however, challenged by the dominating role of the understory in the spectral signal. In this paper, we examine the influence of understory vegetation on forest reflectance in the Arctic region of Finland during no-snow conditions. The study is based on SPOT HRVIR images, field goniospectrometry, 300 ground reference plots and a physically-based forest reflectance model (PARAS). The results indicate that lichen-dominated forest site types can be distinguished from sites dominated by dwarf shrubs. The paper also contains results from applying an analytical method for calculating photon recollision probability from canopy transmittance data for forest stands, and then using it to simulate the reflectance of the same stands.     

Remote sensing of row crop structure and component temperatures using directional radiometric temperatures and inversion techniques

A physically based sensor response model of a row crop was used as the mathematical framework from which several inversion strategies were tested for extracting row structure information and component temperatures using a series of sensor view angles. The technique was evaluated on ground-based radiometric thermal infrared data of a cotton row crop that covered 48% of the ground in the vertical projection. The results showed that the accuracies of the predicted row heights and widths, vegetation temperatures, and soil temperatures of the cotton row crop were on the order of 5 cm (± 10% of mean values), 1°, and 2°C, respectively. The inversion techniques can be applied to directional sensor data from aircraft platforms and even space platforms if the effects of atmospheric absorption and emission can be corrected. In theory, such inversion techniques can be applied to a wide variety of vegetation types and thus can have significant implications for remote sensing research and applications in disciplines that deal with incomplete vegetation canopies.     

Patterns of covariance between forest stand and canopy structure in the Pacific Northwest

In the past decade, lidar (light detection and ranging) has emerged as a powerful tool for remotely sensing forest canopy and stand structure, including the estimation of aboveground biomass and carbon storage. Numerous papers have documented the use of lidar measurements to predict important aspects of forest stand structure, including aboveground biomass. Other papers have documented the ability to transform lidar measurements to approximate common field measures, such as cover, stand height, and vertical distributions of foliage density and light transmittance. However, only a small number of existing works have thoroughly examined relationships between comprehensive assemblages of forest canopy and forest stand structure indices. In this work, canonical correlation analysis of coincident lidar and field datasets in western Oregon and Washington is used to define seven statistically significant pairs of canonical variables, each defining an axis of variation that stand and canopy structure have in common. The first major axis relates mean stand height, and related variables, to aboveground biomass. The second relates canopy cover and volume to leaf area index and stem density. The third relates canopy height variability to mean stem diameter and the basal area of deciduous species. Of the four remaining axes, three are related to contrasts between mature and old-growth stands. Canonical correlation analysis provides a method for ranking the importance of these effects, and for placing both canopy and stand structure indices within the overall covariance structure of the two datasets. In this sense, and for the study area involved, the first three factors (mean height, cover or leaf index area, height variability) represent the same kind of enhancement of lidar data that the tasseled cap indices [Crist, C.P., R.C. Cicone, 1984. A physically-based transformation of thematic mapper data—the TM tasseled cap. IEEE Transactions on Geoscience and Remote Sensing 22, 256-263.] represent for optical remote sensing.     

Upscaling ground observations of vegetation water content, canopy height, and leaf area index during SMEX02 using aircraft and Landsat imagery

Microwave-based remote sensing algorithms for mapping soil moisture are sensitive to water contained in surface vegetation at moderate levels of canopy cover. Correction schemes require spatially distributed estimates of vegetation water content at scales comparable to that of the microwave sensor footprint (10¹ to 10⁴ m). This study compares the relative utility of high-resolution (1.5 m) aircraft and coarser-resolution (30 m) Landsat imagery in upscaling an extensive set of ground-based measurements of canopy biophysical properties collected during the Soil Moisture Experiment of 2002 (SMEX02) within the Walnut Creek Watershed. The upscaling was accomplished using expolinear relationships developed between spectral vegetation indices and measurements of leaf area index, canopy height, and vegetation water content. Of the various indices examined, a Normalized Difference Water Index (NDWI), derived from near- and shortwave-infrared reflectances, was found to be least susceptible to saturation at high levels of leaf area index. With the aircraft data set, which did not include a short-wave infrared water absorption band, the Optimized Soil Adjusted Vegetation Index (OSAVI) yielded best correlations with observations and highest saturation levels. At the observation scale (10 m), LAI was retrieved from both NDWI and OSAVI imagery with an accuracy of 0.6, vegetation water content at 0.7 kg m⁻², and canopy height to within 0.2 m. Both indices were used to estimate field-scale mean canopy properties and variability for each of the intensive soil-moisture-sampling sites within the watershed study area. Results regarding scale invariance over the SMEX02 study area in transformations from band reflectance and vegetation indices to canopy biophysical properties are also presented.     

Determining forest canopy characteristics using airborne laser data

A pulsed laser system was flown over a forested area in Pennsylvania which exhibited a wide range of canopy closure conditions. The lasing system acts as the ultraviolet light equivalent of radar, sensing not only the distance to the top of the forest canopy, but also the range to the forest floor. The data were analyzed to determine which components of the laser data could explain the variability in crown closure along the flight transect. Results indicated that canopy closure was most strongly related to the penetration capability of the laser pulse. Pulses were attenuated more quickly in a dense canopy. Hence the inability to find a strong ground return in the laser data after initially sensing the top of the canopy connoted dense canopy cover. Photogrammetrically acquired tree heights were compared to laser estimates; average heights differed by less than 1 m. The results indicated that the laser system may be used to remotely sense the vertical forest canopy profile. Elements of this profile are linearly related to crown closure and may be used to assess tree height.     

Influence of wind on crop canopy reflectance measurements

Remote sensing techniques of measuring red and far-red crop canopy reflectance are frequently used to estimate crop canopy characteristics. The variability introduced in reflectance data from nonvegetative factors such as wind decreases the usefulness of the techniques. The objective of this study was to quantify and minimize the variability from wind on spectral reflectances. Red and far-red reflectances were acquired above wheat, barley, and alfalfa canopies throughout days of changing wind conditions. Periods of 312 s with little changes in irradiance values were used for the analysis. Wind had negligible effect on reflectances of a short canopy such as cut alfalfa, while it had a significant effect on reflectances from canopies with a higher vertical structure, particularly during gusty conditions. Within the windy and calm periods, extreme values of spectral reflectance differed by 60% and 12%, respectively, in the red, and by 40% and 8% in the far-red for the barley canopy. For the compact and dense canopy structure of alfalfa, these differences reached a maximum of 10% under windy conditions in both spectral regions. The plant canopy architecture, the wind conditions, and the spectral regions all affected the magnitude of the influence of wind on crop canopy spectral reflectances. The mean reflectance of a canopy overestimated the true reflectance by 2–4% while the use of the median reduced this overestimation. Sampling requirements for this sensor are evaluated, and the possibility of decreasing either the sampling rate or the sampling period is discussed.     

Remote sensing in ecological botany

At the XIIth International Botanical Congress, on July 4, 1975, a new direction in scientific methodology was evaluated for the first time within the framework of an International Union of Biological Sciences—remote sensing of vegetation and the environment. Remote sensing is a method of studying the composition, structure, dynamics, and productivity of ecosystems and the state of the biosphere by means of reflectance and emittance characteristics of the earth's surface measureable from aircraft and spacecraft, and the interpretation of such remotely sensed imagery. Remote cartography is conducted with aerial and space images with a scale of from 1: 1000 to 1:30 000 000. Phytomass can be measured by comparing the dependence of the phytocenometric characteristics with the magnitude of the remotely obtained signal. Phenology and dynamics are revealed by means of optical comparison of successive images. Structural ecological investigations can be based on spatial and factoral integration of ecosystems on single, remotely-sensed images. Remote sensors record spatial and temporal variability of the reflective and emissive characteristics of vegetative ground cover. Anthropogeneous effects are recognized by indication of vegetation clearing, fires, ploughing, overgrazing, water and air pollution, and water and wind erosion.     

利用NDVI估算云覆盖地区的植被表面温度研究

干旱监测等实际应用都需要全面掌握地表温度(LST)的空间分布,而云覆盖是这种应用的重要阻碍。试图根据地表温度变化与地表植被之间的相互关系,研究遥感影像中云覆盖区域植被表面温度的估算方法。由于植被的蒸腾作用,植被茂密程度对其表面温度的空间分布有较大影响。这种影响不仅在晴朗无云区域存在,同样适用于云覆盖区域。因此,首先分析云覆盖区域周边无云植被像元的LST与植被指数NDVI之间的关系,建立方程式,然后再利用NDVI在短时间内相对稳定的特点用另一幅图像来获取云覆盖区域的NDVI值,最后根据NDVI与LST之间的关系估计云覆盖植被像元的表面温度。将这一方法应用到山东省聊城市的Landsat ETM+图像,结果表明：当云覆盖范围≤2 000个像元(约1.72 km²)时,通过NDVI来估计云覆盖区域植被表面温度的平均绝对误差<0.7 ℃,均方根误差<1.2 ℃。为了验证其实用性,又将该方法应用于安徽省蚌埠地区的TM图像,云覆盖范围在300个像元以下时,平均绝对误差小于0.1 ℃。因此,可以认为,当云覆盖范围不是很大时,利用NDVI估算云覆盖地区的植被表面温度,具有一定的可行性。     

Relation between vegetation canopy surface temperature and the Sun-surface geometry in a mountainous region of central Italy

Evapotranspiration is the dominant energy exchange process in dense vegetated environments with an adequate water supply. If water is available vegetation canopy temperatures do not respond immediately upon intercepting solar radiation because of the apportionment of absorbed solar radiation into sensible and latent heat. This lag in the thermal conditions of vegetation canopy following the incident solar flux can be even more complex after sunrise because the presence of dew on the foliage requires more available energy investment in evaporating water and less energy spent in warming the foliage. The aim of this Letter, which is based on remotely-sensed thermal data obtained from Landsat Thematic Mapper in the daytime of a clear summer day, is to investigate the relationship between canopy surface temperatures and the incident solar radiation for a forested montainous landscape of central Italy. Results show that, under the conditions of our experiment, a time lag of one hour considerably increases the linear relation between vegetation canopy temperature and local solar illumination angle.     

A new parameterization of canopy spectral response to incident solar radiation: case study with hyperspectral data from pine dominant forest

A small set of independent variables generally seems to suffice when attempting to describe the spectral response of a vegetation canopy to incident solar radiation. This set includes the soil reflectance, the single-scattering albedo, canopy transmittance, reflectance and interception, the portion of uncollided radiation in the total incident radiation, and portions of collided canopy transmittance and interception. All of these are measurable; they satisfy a simple system of equations and constitute a set that fully describes the law of energy conservation in vegetation canopies at any wavelength in the visible and near-infrared part of the solar spectrum. Further, the system of equations specifies the relationship between the optical properties at the leaf and the canopy scales. Thus, the information content of hyperspectral data can be fully exploited if these variables can be retrieved, for they can be more directly related to some of the physical properties of the canopy (e.g. leaf area index). This paper demonstrates this concept through retrievals of single-scattering albedo, canopy absorptance, portions of uncollided and collided canopy transmittance, and interception from hyperspectral data collected during a field campaign in Ruokolahti, Finland, June 14-21, 2000. The retrieved variables are then used to estimate canopy leaf area index, vegetation ground cover, and also the ratio of direct to total incident solar radiation at blue, green, red, and near-infrared spectral intervals.     

Evaluation of terrain and canopy height products in central African tropical forests

Since the introduction of space-based altimetry data into the science community, global products associated with elevation and vegetation metrics have been heavily utilized for a variety of ecological applications. Satellite remote sensing enables the collection of global (or near-global), standardized data sets, which can be used in their original form or used as inputs along with other data sets in generating new products. Recent effort has focused on using available data to generate maps of tree heights at a global scale in the service of a better understanding of above ground biomass distribution and its effects on global carbon storage. However, global data sets, while validated at a global scale, often display local and regional variations in accuracy which must be quantified before applying those data sets to smaller scale studies. This work addresses the need for a better understanding of the quality of the Shuttle Radar Topography Mission (SRTM) 90 m digital elevation model and a global 1 km canopy height model in the dense tropical forests of Gabon by using a small-footprint airborne lidar survey and large-footprint, space-based waveform lidar data from teh National Air and Space Administration’s Ice, Cloud, and land Elevation Satellite (ICESat) for validation. As expected, the study found SRTM elevations to be heavily biased by vegetation in this biome, with elevations consistently located within the canopy volume. In addition, the global canopy height model consistently underestimates maximum canopy height at both local and regional scales.