J. Qi

Leaf area index estimates using remotely sensed data and BRDF models in a semiarid region

The amount and spatial and temporal dynamics of vegetation are important information in environmental studies and agricultural practices. There has been a great deal of interest in estimating vegetation parameters and their spatial and temporal extent using remotely sensed imagery. There are primarily two approaches to estimating vegetation parameters such as leaf area index (LAI). The first one is associated with computation of spectral vegetation indices (SVI) from radiometric measurements. This approach uses an empirical or modeled LAI-SVI relation between remotely sensed variables such as SVI and biophysical variables such as LAI. The major limitation of this empirical approach is that there is no single LAI-SVI equation (with a set of coefficients) that can be applied to remote-sensing images of different surface types. The second approach involves using bidirectional reflectance distribution function (BRDF) models. It inverts a BRDF model with radiometric measurements to estimate LAI using an optimization procedure. Although this approach has a theoretical basis and is potentially applicable to varying surface types, its primary limitation is the lengthy computation time and difficulty of obtaining the required input parameters by the model. In this study, we present a strategy that combines BRDF models and conventional LAI-SVI approaches to circumvent these limitations. The proposed strategy was implemented in three sequential steps. In the first step, a BRDF model was inverted with a limited number of data points or pixels to produce a training data set consisting of leaf area index and associated pixel values. In the second step, the training data set passed through a quality control procedure to remove outliers from the inversion procedure. In the final step, the training data set was used either to fit an LAI-SVI equation or to train a neural fuzzy system. The best fit equation or the trained fuzzy system was then applied to large-scale remote-sensing imagery to map spatial LAI distribution. This approach was applied to Landsat TM imagery acquired in the semiarid southeast Arizona and AVHRR imagery over the Hapex-Sahel experimental sites near Niamy, Niger. The results were compared with limited ground-based LAI measurements and suggested that the proposed approach produced reasonable estimates of leaf area index over large areas in semiarid regions. This study was not intended to show accuracy improvement of LAI estimation from remotely sensed data. Rather, it provides an alternative that is simple and requires little knowledge of study target and few ground measurements.

An interdisciplinary field study of the energy and water fluxes in the atmosphere−biosphere system over semiarid rangelands : description and some preliminary results

Abstract Arid and semiarid rangelands comprise a significant portion of the earths land surface. Yet little is known about the effects of temporal and spatial changes in surface soil moisture on the hydrologic cycle, energy balance, and the feedbacks to the atmosphere via thermal forcing over such environments. Understanding this interrelationship is crucial for evaluating the role of the hydrologic cycle in surface-atmosphere interactions. This study focuses on the utility of remote sensing to provide measurements of surface soil moisture, surface albedo, vegetation biomass, and temperature at different spatial and temporal scales. Remote-sensing measurements may provide the only practical means of estimating some of the more important factors controlling land surface processes over large areas. Consequently, the use of remotely sensed information in biophysical and geophysical models greatly enhances their ability to compute fluxes at catchment and regional scales on a routine basis. However, model cal...

Normalization of sun/view angle effects using spectral albedo-based vegetation indices

Abstract Current vegetation indices are normally computed with directional spectral reflectances and are subjected to many external perturbations such as soil background variations, atmospheric conditions, geometric registration, and especially sensor viewing geometry. Subsequent use of these indices to estimate vegetation amounts would result in substantial uncertainties. To reduce the uncertainties due to sun/ view angle variations, spectral albedds, which are integrated reflectance values over a hemisphere of the surface within the specific spectral waveband, were derived from multidirectional measurements and bidirectional reflectance distribution function (BRDF) models and were subsequently used in vegetation index computations. The albedo-based vegetation indices were then compared with those computed with spectral reflectances using ground-, aircraft-, and satellite-based remote sensing measurements over harvested alfalfa, full-cover cotton canopy, pecan orchards, and bare soil surfaces. The results showed that spectral albedo-based vegetation indices were independent of view angles while the spectral reflectance vegetation indices varied substantially with sensor viewing geometry. Therefore, the view angle effects on spectral vegetation indices can be normalized, and the sun angle effects can be further reduced with a limited number of multidirectional measurements and BRDF models.

Time Course of Radiation Use Efficiency in a Shortgrass Ecosystem: Consequences for Remotely Sensed Estimation of Primary Production

Abstract A reliable estimation of primary production of terrestrial ecosystems is often a prerequisite for land survey and management, while being important also in ecological and climatological studies. At a regional scale, grassland primary production estimates are increasingly being made with the use of satellite data. In a currently used approach, regional gross, net, and aboveground net primary productivity (GPP, NPP, and ANPP) are derived from the parametric model of Monteith and are calculated as the product of the fraction of incident photosynthetically active radiation absorbed by the canopy (fAPAR) and gross, net, and aboveground net production (radiation-use) efficiencies (ϵg, ϵn, and ϵan); fAPAR being derived from indices calculated from satellite-measured reflectances in the red and near infrared. The accuracy and realism of the primary production values estimated by this approach therefore largely depend on an accurate estimation of ϵg, ϵn, and ϵan. However, data are scarce for production efficiencies of semiarid grasslands, and their time and spatial variations are poorly documented, often leading to large errors for the estimates. In this paper, a modeling approach taking into account relevant ecosystem processes and based on extensive field data was used to estimate time variations of ϵg, ϵn and ϵan of a shortgrass site in Arizona. These variations were explained by variations in plant water stress, temperature, leaf aging, and processes such as respiration and changes in allocation pattern between above- and below-ground compartments. Over the 3 study years, averaged values of ϵg, ϵn, and ϵan were found to be 1.92, 0.74, and 0.29 g DM (MJ IPAR)−1, respectively. ϵg and ϵn exhibited large interannual and seasonal variations mainly due to changes in water limitations during the growing season. Interannual variations of ϵan were much less important. However, for shorter periods, ϵan exhibited very contrasting values from regrowth to senescence. The calculation of ANPP seems less prone to errors due to environmental effects when computed on an annual basis. When estimating GPP and NPP, better results are expected if water limitations are taken into account. This could be possible through the estimation of a water-stress factor by using surface temperature or other indices derived from thermal infrared remote sensing data. The limitations due to temporally varying efficiencies, shown here for shortgrass ecosystems, are also relevant to all drought-exposed ecosystems, particularly those with abundant evergreen or perennial species.

Combining multifrequency microwave and optical data for crop management

Abstract The potential for the combined use of microwave and optical data for crop management is explored with the use of images acquired in the visible, near-infrared, and thermal spectrum and the synthetic aperture radar (SAR) wavelengths in the Ku (14.85 GHz) and C (5.3 GHz) bands. The images were obtained during June 1994 and covered an agricultural site composed of large fields of partial-cover cotton, near-full-cover alfalfa, and bare soil fields of varying roughness. Results showed that the SAR Ku backscatter coefficient (Ku-band σ†) was sensitive to soil roughness and insensitive to soil moisture conditions when vegetation was present. When soil roughness conditions were relatively similar (e.g., for cotton fields of similar row direction and for all alfalfa fields), Ku-band σ† was sensitive to the fraction of the surface covered by vegetation. Under these conditions, the Ku-band σ° and the optical normalized difference vegetation index (NDVI) were generally correlated. The SAR C backscatter coefficient (C-z.sbnd;band σ°) was found to be sensitive to soil moisture conditions for cotton fields with green leaf area index (GLAI) less than 1.0 and alfalfa fields with GLAI nearly 2.0. For both low-GLAI cotton and alfalfa, Cband σ° was correlated with measurements of surface temperature (T s ). A theoretical basis for the relations between Kuband σ° and NDVI and between C-band gs0 and T s was presented and supported with on-site measurements. On the basis of these findings, some combined optical and radar approaches are suggested for crop management applications

Biophysical parameter estimations using multidirectional spectral measurements

Abstract There has been a great deal of interest in estimation of terrestrial biophysical parameters such as vegetation with remotely sensed data. Quantitative estimation of vegetation properties with existing algorithms has been based on empirical relationships established by simple regression. The problem in applying these empirical relationships is that those coefficients proposed vary with vegetation type. To investigate the possible development of an algorithm to quantitatively estimate vegetation properties independent of vegetation type, a model-to-model approach is proposed. This approach first inverts a simple bidirectional reflectance distribution function (BRDF) model with limited data points and simulates multidirectional data. The simulated data are then used in the inversion of a physically based BRDF model to estimate vegetation optical properties (leaf reflectance and transmittance) and leaf area index (LAI). This approach is validated with data. collected from three experiments conducted in cotton, alfalfa, wheat, and pecan fields. A sensitivity analysis and demonstration with multitemporal remote sensing data were performed, and the results show that estimated LAI values agree well with field observations and there is a potential in applying this approach on an operational basis in practice with multitemporal remote sensing data.

Reflectance factor retrieval from Landsat TM and SPOT HRV data for bright and dark targets

Abstract In recent years, there have been many land-surface studies based on visible and near-infrared reflectance values retrieved from the Landsat Thematic Mapper (TM) and SPOT High Resolution Visible (HRV) sensors. Retrieval of reflectance from satellite sensor digital count requires knowledge of the atmospheric conditions and the sensor absolute calibration. In most cases, atmospheric conditions are simulated with a radiative transfer code and sensor calibration coefficients are obtained from preflight sensor calibrations or in-flight calibrations over bright surfaces (such as White Sands, New Mexico, USA, or La Crau, France). Though these procedures are well accepted, there have been few studies specifically designed to validate the accuracy of such reflectance factor retrievals (RFR) for both bright and dark targets. Data from two experiments conducted in an agricultural region in central Arizona were analyzed to quantify the accuracy of RFR from the Landsat TM and SPOT HRV sensors. These data included measurements made with groundbased and aircraft-based four-band radiometers and the NASA Advanced Solid-State Array Spectrometer (ASAS) aboard a C130 aircraft, and TM and HRV images acquired at nadir and off-nadir viewing angles. Results showed that the off-nadir reflectance factors measured using ground- and aircraft-based instruments, including ASAS, were comparable. The RFR from the satellite-based TM and HR V sensors generally resulted in an overestimation of dark target reflectance (up to 0.05 reflectance in the visible) and an underestimation of bright target reflectance (up to 0.1 reflectance in the near-infrared). Even greater error was possible when RFR was based on outdated sensor calibrations, particularly those conducted prelaunch. There was supporting evidence from studies at three sites (White Sands, New Mexico; Maricopa, Arizona; and Walnut Gulch, Arizona) that the Landsat-5 TM sensor sensitivity may have degraded by as much as 20% from the prelaunch calibration. Regarding the potential error in RFR related to recent changes in the processing of Landsat TM data (Level-0 and Level-1) by EOSAT Corporation, we found that the Level-0 data was slightly greater (∼2 digital counts) than the Level-1 data for all bands and all targets in our study.

Combining remote sensing and plant growth modeling to describe the carbon and water budget of semi-arid grasslands

The authors investigate the opportunity of coupling a vegetation growth model developed for semi-arid perennial grasslands, with a soil/vegetation reflectance model in order to use remote sensing data to improve the model simulations. The vegetation functioning model developed for this purpose has been validated in Southeastern Arizona and Northeastern Sonora on several semiarid grassland sites. The assimilation of radiometric data into the shortgrass prairie ecosystem model is based on an iterative numerical procedure that recalibrates the combined model until model simulations match radiometric observations. For this purpose, a prior sensitivity analysis was carried out for the vegetation growth model in order to determine the most important input parameters or initial conditions on which to base the recalibration procedure. The results obtained and the potential of such an approach are discussed.

Time variation of radiation use efficiency of a semiarid grassland: consequences for remotely sensed estimation of primary production

A reliable estimation of primary production of terrestrial ecosystems is often a prerequisite for carrying out land management, while being important also in ecological and climatological studies. At a regional scale, grassland primary production estimates are increasingly being made using satellite data. In a currently used approach, regional Gross, Net and Above-ground Net Primary Productivity (GPP, NPP and ANPP) are derived from the parametric model of Monteith and are calculated as the product of the fraction of incident photosynthetically active radiation absorbed by the canopy (fAPAR) and gross, net and above-ground net production (radiation-use) efficiencies ((epsilon) g, (epsilon) n, (epsilon) an); fAPAR being derived from indices calculated from satellite measured reflectances in the red and near infrared. The accuracy and realism of the primary production values estimated by this approach therefore largely depend on an accurate estimation of (epsilon) g, (epsilon) n and (epsilon) an. However, data are scarce for production efficiencies of semi-arid grasslands, and their time and spatial variations are poorly documented, leading to often large errors on the estimates. In this paper a modeling approach taking into account relevant ecosystem processes and based on extensive field data, is used to estimate sub- seasonal and inter-annual variations of (epsilon) g, (epsilon) n and (epsilon) an of a shortgrass site of Arizona, and to quantitatively explain these variations by these of plant water stress, temperature, leaf aging, and processes such as respiration and changes in allocation pattern. For example, over the 3 study years, the mean (epsilon) g, (epsilon) n, and (epsilon) an were found to be 1.92, 0.74 and 0.29 g DM (MJ APAR)-1 respectively. (epsilon) g and epsilonn exhibited very important inter- annual and seasonal variations mainly due to different water stress conditions during the growing season. Inter-annual variations of (epsilon) an were much less important, while for periods shorter than a growing season, (epsilon) an exhibits very contrasting values from re-growth to senescence. Therefore the calculation of ANPP based on Monteiths approach seems less prone to errors due to environmental effects when computed on an annual basis, whereas for periods shorter than the growing season the computation of either GPP, NPP or ANPP is delicate.

Biophysical parameter retrievals using bidirectional measurements

Measurements with oblique viewing angles can provide complementary information about land surfaces since these measurements reveal structural aspects while nadir measurements do not. Consequently, bidirectional measurements can be used to characterize surface biophysical properties. In this study, the authors developed an algorithm that combined bidirectional measurements with three existing bidirectional reflectance distribution function (BRDF) models to retrieve surface physical parameters such as leaf area index (LAI). The algorithm was applied to a multidirectional reflectance data set collected during an experiment at Maricopa Agricultural Center in 1991 over three different types of land surfaces (recently harvested alfalfa, pecan, and cotton). It was further validated with ground-based multitemporal reflectance measurements over a growing wheat canopy in 1983. Preliminary results showed that the algorithm was promising in retrieval of LAI and had potential to monitor vegetation growth on an operational basis.<<ETX>>