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Bayesian object-based estimation of LAI and chlorophyll from a simulated Sentinel-2 top-of-atmosphere radiance image
Laurent, V.C.E. ; Schaepman, M.E. ; Verhoef, W. ; Weyermann, J. ; Chavez Oyanadel, R.O. - \ 2014
Remote Sensing of Environment 140 (2014). - ISSN 0034-4257 - p. 318 - 329.
radiative-transfer models - high-spatial-resolution - remote-sensing data - red-edge bands - reflectance model - global products - leaf-area - inversion - canopy - vegetation
Leaf area index (LAI) and chlorophyll content (Cab) are important vegetation variables which can be monitored using remote sensing (RS). Physically-based approaches have higher transferability and are therefore better suited than empirically-based approaches for estimating LAI and Cab at global scales. These approaches, however, require the inversion of radiative transfer (RT) models, which is an ill-posed and underdetermined problem. Four regularization methods have been proposed, allowing finding stable solutions: 1) model coupling, 2) using a priori information (e.g. Bayesian approaches), 3) spatial constraints (e.g. using objects), and 4) temporal constraints. For mono-temporal data, only the first three methods can be applied. In an earlier study, we presented a Bayesian object-based algorithm for inverting the SLC-MODTRAN4 coupled canopy-atmosphere RT model, and compared it with a Bayesian LUT inversion. The results showed that the object-based approach provided more accurate LAI estimates. This study, however, heavily relied on expert knowledge about the objects and vegetation classes. Therefore, in this new contribution, we investigated the applicability of the Bayesian object-based inversion of the SLC-MODTRAN4 model to a situation where no such knowledge was available. The case study used a 16 × 22 km2 simulated top-of-atmosphere image of the upcoming Sentinel-2 sensor, covering the area near the city of Zurich, Switzerland. Seven APEX radiance images were nadir-normalized using the parametric Li–Ross model, spectrally and spatially resampled to Sentinel-2 specifications, geometrically corrected, and mosaicked. The atmospheric effects between APEX flight height and top-of-atmosphere level were added based on two MODTRAN4 simulations. The vegetation objects were identified and delineated using a segmentation algorithm, and classified in four levels of brightness in the visible domain. The LAI and Cab maps obtained from the Bayesian object-based inversion of the coupled SLC-MODTRAN4 model presented realistic spatial patterns. The impact of the parametric Li–Ross nadir-normalization was evaluated by comparing 1) the angular signatures of the SLC-MODTRAN4 and Li–Ross models, and 2) the LAI and Cab maps obtained from a Li–Ross nadir-normalized image (using nadir viewing geometry) and from the original image (using the original viewing geometry). The differences in angular signatures were small but systematic, and the differences between the LAI and Cab maps increased from the center towards the edges of the across-track direction. The results of this study contribute to preparing the RS community for the arrival of Sentinel-2 data in the near future, and generalize the applicability of the Bayesian object-based approach for estimating vegetation variables to cases where no field data are available.
A Bayesian object-based approach for estimating vegetation biophysical and biochemical variables from APEX at-sensor radiance data
Laurent, V.C.E. ; Verhoef, W. ; Damm, A. ; Schaepman, M.E. ; Clevers, J.G.P.W. - \ 2013
Remote Sensing of Environment 139 (2013). - ISSN 0034-4257 - p. 6 - 17.
radiative-transfer models - leaf-area index - sun-induced fluorescence - remote-sensing data - reflectance data - global products - brdf model - inversion - canopy - lai
Vegetation variables such as leaf area index (LAI) and leaf chlorophyll content (Cab) are important inputs for vegetation growth models. LAI and Cab can be estimated from remote sensing data using either empirical or physically-based approaches. The latter are more generally applicable because they can easily be adapted to different sensors, acquisition geometries, and vegetation types. They estimate vegetation variables through inversion of radiative transfer models. Such inversions are ill-posed but can be regularized by coupling models, by using a priori information, and spatial and/or temporal constraints. Striving to improve the accuracy of LAI and Cab estimates from single remote sensing images, this contribution proposes a Bayesian object-based approach to invert at-sensor radiance data, combining the strengths of regularization by model coupling, as well as using a priori data and object-level spatial constraints. The approach was applied to a study area consisting of homogeneous agricultural fields, which were used as objects for applying the spatial constraints. LAI and Cab were estimated from at-sensor radiance data of the Airborne Prism EXperiment (APEX) imaging spectrometer by inverting the coupled SLC-MODTRAN4 canopy-atmosphere model. The estimation was implemented in two steps. In the first step, up to six variables were estimated for each object using a Bayesian optimization algorithm. In the second step, a look-up-table (WT) was built for each object with only LAI and Cab as free variables, constraining the values of all other variables to the values obtained in the first step. The results indicated that the Bayesian object-based approach estimated LAI more accurately (R-2 = 0.45 and RMSE = 1.0) than a LUT with a Bayesian cost function (LUT-BCF) approach (R-2 = 022 and RMSE = 2.1), and Cab with a smaller absolute bias (-9 versus -23 mu g/cm(2)). The results of this study are an important contribution to further improve the regularization of ill-posed RT model inversions. The proposed approach allows reducing uncertainties of estimated vegetation variables, which is essential to support various environmental applications. The definition of objects and a priori data in cases where less extensive ground data are available, as well as the definition of the observation covariance matrix, are critical issues which require further research. (C) 2013 Elsevier Inc All rights reserved.
Modelling the spectral response of the desert tree Prosopis tamarugo to water stress
Chávez Oyanadel, R.O. ; Clevers, J.G.P.W. ; Herold, M. ; Ortiz, M. ; Acevedo, E. - \ 2013
International Journal of applied Earth Observation and Geoinformation 21 (2013). - ISSN 0303-2434 - p. 53 - 65.
leaf-area index - radiative-transfer models - atacama desert - red-edge - bidirectional reflectance - absorption features - canopy reflectance - optical-properties - radiance data - plant stress
In this paper, we carried out a laboratory experiment to study changes in canopy reflectance of Tamarugo plants under controlled water stress. Tamarugo (Prosopis tamarugo Phil.) is an endemic and endangered tree species adapted to the hyper-arid conditions of the Atacama Desert, Northern Chile. Observed variation in reflectance during the day (due to leaf movements) as well as changes over the experimental period (due to water stress) were successfully modelled by using the Soil-Leaf-Canopy (SLC) radiative transfer model. Empirical canopy reflectance changes were mostly explained by the parameters leaf area index (LAI), leaf inclination distribution function (LIDF) and equivalent water thickness (EWT) as shown by the SLC simulations. Diurnal leaf movements observed in Tamarugo plants (as adaptation to decrease direct solar irradiation at the hottest time of the day) had an important effect on canopy reflectance and were explained by the LIDF parameter. The results suggest that remote sensing based assessment of this desert tree should consider LAI and canopy water content (CWC) as water stress indicators. Consequently, we tested fifteen different vegetation indices and spectral absorption features proposed in literature for detecting changes of LAI and CWC, considering the effect of LIDF variations. A sensitivity analysis was carried out using SLC simulations with a broad range of LAI, LIDF and EWT values. The Water Index was the most sensitive remote sensing feature for estimating CWC for values less than 0.036 g/cm2, while the area under the curve for the spectral range 910–1070 nm was most sensitive for values higher than 0.036 g/cm2. The red-edge chlorophyll index (CIred-edge) performed the best for estimating LAI. Diurnal leaf movements had an effect on all remote sensing features tested, particularly on those for detecting changes in CWC.
Mapping spatio-temporal variation of grassland quantity and quality using MERIS data and the PROSAIL model
Si, Y. ; Schlerf, M. ; Zurita-Milla, R. ; Skidmore, A.K. ; Wang, T. - \ 2012
Remote Sensing of Environment 121 (2012). - ISSN 0034-4257 - p. 415 - 425.
radiative-transfer models - remote-sensing data - chlorophyll content - vegetation indexes - reflectance data - hyperspectral measurements - heterogeneous grassland - canopy reflectance - leaf - lai
Accurate estimates of the quantity and quality of grasslands, as they vary in space and time and from regional to global scales, furthers our understanding of grassland ecosystems. The Medium Resolution Imaging Spectrometer (MERIS) is a promising sensor for measuring and monitoring grasslands due to its high spectral resolution, medium spatial resolution and a two- to three-day repeat cycle. However, thus far the multi-biome MERIS land products have limited consistency with in-situ measurements of leaf area index (LAI), while the multi-biome canopy chlorophyll content (CCC) has not been validated yet with in-situ data. This study proposes a single-biome approach to estimate grassland LAI (a surrogate of grass quantity) and leaf chlorophyll content (LCC) and CCC (surrogates of grass quality) using the inversion of the PROSAIL model and MERIS reflectance. Both multi-biome and single-biome approaches were validated using two-season in-situ data sets and the temporal consistency was analyzed using time-series of MERIS data. The single-biome approach showed a consistently better performance for estimating LAI (R 2=0.70, root mean square error (RMSE)=1.02, normalized RMSE (NRMSE)=16%) and CCC (R 2=0.61, RMSE=0.36, NRMSE=23%) compared with the multi-biome approach (LAI: R 2=0.36, RMSE=1.77, NRMSE=28%; CCC: R 2=0.47, RMSE=1.33, NRMSE=84%). However, both single-biome and multi-biome approaches failed to retrieve LCC. The multi-biome LAI was overestimated at lower LAI values (
Potential performances of remotely sensed LAI assimilation in WOFOST model based on an OSS experiment
Curnel, Y. ; Wit, A.J.W. de; Duveiller, G. ; Defourny, P. - \ 2011
Agricultural and Forest Meteorology 151 (2011)12. - ISSN 0168-1923 - p. 1843 - 1855.
ensemble kalman filter - leaf-area index - sensing data assimilation - radiative-transfer models - band vegetation indexes - crop model - simulation experiments - precision agriculture - chlorophyll content - reflectance data
An Observing System Simulation Experiment (OSSE) has been defined to assess the potentialities of assimilating winter wheat leaf area index (LAI) estimations derived from remote sensing into the crop growth model WOFOST. Two assimilation strategies are considered: one based on Ensemble Kalman Filter (EnKF) and the second on recalibration/re-initialisation of uncertain model parameters and initial state conditions. The main objective of the OSS Experiment is to estimate the requisites for the remotely sensed LAI, in terms of accuracy and sampling frequency, to reach target of either 25 or 50% reduction of errors on the final estimation of grain yields. Our results demonstrate that EnKF is not suitable for assimilating LAI in WOFOST as the average error on final grain yields estimation globally increases. These poor results can be explained by the possible differences of phenological development existing between assimilated and modelled LAI values (difference called “phenological shift” in our study) which is not corrected by the EnKF-based assimilation strategy. On the contrary, a recalibration-based assimilation approach globally improves the estimation of final grain yields in a significant way. On average, such improvement can reach up to approximately 65% when observations are available all along the growing season. Improvements on the order of 20% can be already be attained early in the season, which is of great interest in a crop yield forecasting perspective. If the first objective (25%) of error reduction on final grain yields can be reached in a quite high number of assimilated LAI observations availabilities and uncertainty levels, the field of possibilities is significantly restricted for the second objective (50%) and implies to have LAI observations available all along the growing season, at least on a weekly basis and with an uncertainty level equal or ideally lower than 10%. These requirements are not currently met from neither a technological nor an operational point of view but the results presented here can provide guidelines for future missions dedicated to crop growth monitoring. --------------------------------------------------------------------------------
Inversion of a coupled canopy–atmosphere model using multi-angular top-of-atmosphere radiance data: A forest case study
Laurent, V.C.E. ; Verhoef, W. ; Clevers, J.G.P.W. ; Schaepman, M.E. - \ 2011
Remote Sensing of Environment 115 (2011)10. - ISSN 0034-4257 - p. 2603 - 2612.
leaf-area index - radiative-transfer models - remote-sensing data - vegetation structure - misr data - reflectance - retrieval - variables - products - prospect
Since the launch of sensors with angular observation capabilities, such as CHRIS and MISR, the additional potential of multi-angular observations for vegetation structural and biochemical variables has been widely recognised. Various methods have been successfully implemented to estimate forest biochemical and biophysical variables from atmospherically-corrected multi-angular data, but the use of physically based radiative transfer (RT) models is still limited. Because both canopy and atmosphere have an anisotropic behaviour, it is important to understand the multi-angular signal measured by the sensor at the top of the atmosphere (TOA). Coupled canopy–atmosphere RT models allow linking surface variables directly to the TOA radiance measured by the sensor and are therefore very interesting tools to use for estimating forest variables from multi-angular data. We investigated the potential of TOA multi-angular radiance data for estimating forest variables by inverting a coupled canopy–atmosphere physical RT model. The case study focussed on three Norway spruce stands located at the Bily Kriz experimental site (Czech Republic), for which multi-angular CHRIS and field data were acquired in September 2006. The soil–leaf–canopy RT model SLC and the atmospheric model MODTRAN4 were coupled using a method allowing to make full use of the four canopy angular reflectance components provided by SLC. The TOA radiance simulations were in good agreement with the spectral and angular signatures measured by CHRIS. Singular value decompositions of the Jacobian matrices showed that the dimensionality of the variable estimation problem increased from 3 to 6 when increasing the number of observation angles from 1 to 4. The model inversion was conducted for two cases: 4 and 7 variables. The most influential parameters were chosen as free variables in the look-up tables, namely: vertical crown cover (Cv), fraction of bark material (fB), needle chlorophyll content (needleCab), needle dry matter content (needleCdm) for the 4-variable case, and additionally, tree shape factor (Zeta), dissociation factor (D), and needle brown pigments content (needleCs) in the 7-variable case. All angular combinations were tested, and the best estimates were obtained with combinations using two or three angles, depending on the number of variables and on the stand used. Overall, this case study showed that, although making use of its full potential is still a challenge, TOA multi-angular radiance data do have a higher potential for variable estimation than mono-angular data.
Effects of woody elements on simulated canopy reflectance: implications for forest chlorophyll content retrieval
Verrelst, J. ; Schaepman, M.E. ; Malenovsky, Z. ; Clevers, J.G.P.W. - \ 2010
Remote Sensing of Environment 114 (2010)3. - ISSN 0034-4257 - p. 647 - 656.
leaf optical-properties - light-use efficiency - resolution satellite imagery - radiative-transfer models - high-spatial-resolution - spectral reflectance - imaging spectroscopy - vegetation indexes - hyperspectral reflectance - sensitivity-analysis
An important bio-indicator of actual plant health status, the foliar content of chlorophyll a and b (Cab), can be estimated using imaging spectroscopy. For forest canopies, however, the relationship between the spectral response and leaf chemistry is confounded by factors such as background (e.g. understory), canopy structure, and the presence of non-photosynthetic vegetation (NPV, e.g. woody elements)—particularly the appreciable amounts of standing and fallen dead wood found in older forests. We present a sensitivity analysis for the estimation of chlorophyll content in woody coniferous canopies using radiative transfer modeling, and use the modeled top-of-canopy reflectance data to analyze the contribution of woody elements, leaf area index (LAI), and crown cover (CC) to the retrieval of foliar Cab content. The radiative transfer model used comprises two linked submodels: one at leaf level (PROSPECT) and one at canopy level (FLIGHT). This generated bidirectional reflectance data according to the band settings of the Compact High Resolution Imaging Spectrometer (CHRIS) from which chlorophyll indices were calculated. Most of the chlorophyll indices outperformed single wavelengths in predicting Cab content at canopy level, with best results obtained by the Maccioni index ([R780 - R710] / [R780 - R680]). We demonstrate the performance of this index with respect to structural information on three distinct coniferous forest types (young, early mature and old-growth stands). The modeling results suggest that the spectral variation due to variation in canopy chlorophyll content is best captured for stands with medium dense canopies. However, the strength of the up-scaled Cab signal weakens with increasing crown NPV scattering elements, especially when crown cover exceeds 30%. LAI exerts the least perturbations. We conclude that the spectral influence of woody elements is an important variable that should be considered in radiative transfer approaches when retrieving foliar pigment estimates in heterogeneous stands, particularly if the stands are partly defoliated or long-lived
Estimating canopy water content using hyperspectral remote sensing data
Clevers, J.G.P.W. ; Kooistra, L. ; Schaepman, M.E. - \ 2010
International Journal of applied Earth Observation and Geoinformation 12 (2010)2. - ISSN 0303-2434 - p. 119 - 125.
leaf optical-properties - radiative-transfer models - imaging spectrometer data - dynamic vegetation model - reflectance data - indexes - retrieval - information - variables - products
Hyperspectral remote sensing has demonstrated great potential for accurate retrieval of canopy water content (CWC). This CWC is defined by the product of the leaf equivalent water thickness (EWT) and the leaf area index (LAI). In this paper, in particular the spectral information provided by the canopy water absorption feature at 970 nm for estimating and predicting CWC was studied using a modelling approach and in situ spectroradiometric measurements. The relationship of the first derivative at the right slope of the 970 nm water absorption feature with CWC was investigated with the PROSAIL radiative transfer model and tested for field spectroradiometer measurements on two test sites. The first site was a heterogeneous floodplain with natural vegetation like grasses and various shrubs. The second site was an extensively grazed fen meadow. PROSAIL simulations (using coupled SAIL/PROSPECT-5 models) showed a linear relationship between the first derivative over the 1015–1050 nm spectral interval and CWC (R2 = 0.97). For 8 plots at the floodplain site the spectral derivative over the 1015–1050 nm interval obtained with an ASD FieldSpec spectroradiometer yielded an R2 of 0.51 with CWC. For 40 plots at the fen meadow ASD FieldSpec spectral measurements yielded an R2 of 0.68 for the derivative over the 1015–1050 nm interval with CWC. Consistency of the results confirmed the potential of using simulation results for calibrating the relationship between this first derivative and CWC
PROSPECT and SAIL models: a review of use for vegetation characterization
Jacquemond, S. ; Verhoef, W. ; Baret, F. ; Bacour, C. ; Zarco-Tejada, P. ; Asner, G.P. ; Francois, C. ; Ustin, S.L. - \ 2009
Remote Sensing of Environment 113 (2009)Suppl 1. - ISSN 0034-4257 - p. S56 - S66.
radiative-transfer models - remote-sensing data - leaf optical-properties - canopy reflectance models - cyclopes global products - sugar-beet canopies - chlorophyll content - water-content - area index - bidirectional reflectance
The combined PROSPECT leaf optical properties model and SAIL canopy bidirectional reflectance model, also referred to as PROSAIL, has been used for about sixteen years to study plant canopy spectral and directional reflectance in the solar domain. PROSAIL has also been used to develop new methods for retrieval of vegetation biophysical properties. It links the spectral variation of canopy reflectance, which is mainly related to leaf biochemical contents, with its directional variation, which is primarily related to canopy architecture and soil/vegetation contrast. This link is key to simultaneous estimation of canopy biophysical/structural variables for applications in agriculture, plant physiology, or ecology, at different scales. PROSAIL has become one of the most popular radiative transfer tools due to its ease of use, general robustness, and consistent validation by lab/field/space experiments over the years. However, PROSPECT and SAIL are still evolving: they have undergone recent improvements both at the leaf and the plant levels. This paper provides an extensive review of the PROSAIL developments in the context of canopy biophysics and radiative transfer modeling
An integrated model of soil-canopy spectral radiances, photosynthesis, fluorescence, temperature and energy balance
Tol, C. van der; Verhoef, W. ; Timmermans, J. ; Verhoef, A. ; Su, Z. - \ 2009
Biogeosciences 6 (2009)12. - ISSN 1726-4170 - p. 3109 - 3129.
radiative-transfer models - leaf optical-properties - chlorophyll fluorescence - stomatal conductance - co2 assimilation - carbon-dioxide - reflectance - leaves - plants - water
This paper presents the model SCOPE (Soil Canopy Observation, Photochemistry and Energy fluxes), which is a vertical (1-D) integrated radiative transfer and energy balance model. The model links visible to thermal infrared radiance spectra (0.4 to 50 µm) as observed above the canopy to the fluxes of water, heat and carbon dioxide, as a function of vegetation structure, and the vertical profiles of temperature. Output of the model is the spectrum of outgoing radiation in the viewing direction and the turbulent heat fluxes, photosynthesis and chlorophyll fluorescence. A special routine is dedicated to the calculation of photosynthesis rate and chlorophyll fluorescence at the leaf level as a function of net radiation and leaf temperature. The fluorescence contributions from individual leaves are integrated over the canopy layer to calculate top-of-canopy fluorescence. The calculation of radiative transfer and the energy balance is fully integrated, allowing for feedback between leaf temperatures, leaf chlorophyll fluorescence and radiative fluxes. Leaf temperatures are calculated on the basis of energy balance closure. Model simulations were evaluated against observations reported in the literature and against data collected during field campaigns. These evaluations showed that SCOPE is able to reproduce realistic radiance spectra, directional radiance and energy balance fluxes. The model may be applied for the design of algorithms for the retrieval of evapotranspiration from optical and thermal earth observation data, for validation of existing methods to monitor vegetation functioning, to help interpret canopy fluorescence measurements, and to study the relationships between synoptic observations with diurnally integrated quantities. The model has been implemented in Matlab and has a modular design, thus allowing for great flexibility and scalability
Estimation of vegetation LAI from hyperspectral reflectance data: Effects of soil type and plant architecture
Darvishzadeh, R. ; Skidmore, A.K. ; Atzberger, C. ; Wieren, S.E. van - \ 2008
International Journal of applied Earth Observation and Geoinformation 10 (2008)3. - ISSN 0303-2434 - p. 358 - 373.
leaf-area index - canopy chlorophyll density - radiative-transfer models - satellite data - broad-band - biophysical variables - spectral reflectance - thematic mapper - wheat canopies - winter-wheat
The retrieval of canopy biophysical variables is known to be affected by confounding factors such as plant type and background reflectance. The effects of soil type and plant architecture on the retrieval of vegetation leaf area index (LAI) from hyperspectral data were assessed in this study. In situ measurements of LAI were related to reflectances in the red and near-infrared and also to five widely used spectral vegetation indices (VIs). The study confirmed that the spectral contrast between leaves and soil background determines the strength of the LAI¿reflectance relationship. It was shown that within a given vegetation species, the optimum spectral regions for LAI estimation were similar across the investigated VIs, indicating that the various VIs are basically summarizing the same spectral information for a given vegetation species. Cross-validated results revealed that, narrow-band PVI was less influenced by soil background effects (0.15 ¿ RMSEcv ¿ 0.56). The results suggest that, when using remote sensing VIs for LAI estimation, not only is the choice of VI of importance but also prior knowledge of plant architecture and soil background. Hence, some kind of landscape stratification is required before using hyperspectral imagery for large-scale mapping of vegetation biophysical variables.
Space-based spectro-directional measurements for the improved estimation of ecosystem variables
Kneubühler, M. ; Koetz, B. ; Huber, S. ; Zimmerman, N.E. ; Schaepman, M.E. - \ 2008
Canadian Journal of Remote Sensing 34 (2008)3. - ISSN 1712-7971 - p. 192 - 205.
remote-sensing data - leaf-area index - canopy biophysical variables - radiative-transfer models - nitrogen concentration - surface heterogeneity - absorption features - foliar chemistry - polder data - reflectance
In this paper, four unique information sources of the Compact High Resolution Imaging Spectrometer (CHRIS) onboard the Project for On-Board Autonomy (PROBA-1) are exploited, namely, the spectral, directional, spatial, and temporal dimensions. Based on the results of three case studies in Switzerland, the use of multi-angular CHRIS-PROBA data for monitoring complex and dynamic vegetation canopies of forests and agricultural crops is demonstrated. We conclude that simultaneous exploitation of the spectrodirectional and temporal behaviours of various vegetation canopies allows for assessing the biochemical and biophysical properties on the one hand and provides additional information on canopy structure via the directional component on the other hand. The study cases focus on various aspects of combining these information dimensions for improved retrieval of vegetation characteristics, namely, (i) the vegetation heterogeneity measurements that use the Minnaert function parameter k, (ii) an improved assessment of foliar water content (CW) and nitrogen concentration (CN) based on multi-angular data, and (iii) continuous leaf area index (LAI) time-profiles lead to more accurate estimates of ecosystem processes and inventorying studies. The first study¿s assessment of canopy structure and heterogeneity from multi-angular data using Minnaert¿s k successfully demonstrates the distinction between closed and medium-density canopies. The second case study shows that the assessment of plant biochemistry from remotely sensed data profits from the information gained from multi-angular datasets. A synergistic approach that integrates multiple sources of information for the estimation of LAI over the season produces promising results for crop growth monitoring in the third case study. CHRIS-PROBA¿s multi-angular observations at the regional scale, while having a comparable spatial resolution of Landsat satellites, can significantly contribute to a better understanding of regional surface anisotropy. This strengthens the link between field observations and canopy scale applications. The results of the three case studies clearly demonstrate the potential and value of spectrodirectional Earth observations at regional scales for ecological monitoring and modeling studies.
LAI and chlorophyll estimation for a heterogeneous grassland using hyperspectral measurements
Darvishzadeh, R. ; Skidmore, A.K. ; Schlerf, M. ; Atzberger, C. ; Corsi, F. ; Cho, M.A. - \ 2008
ISPRS Journal of Photogrammetry and Remote Sensing 63 (2008)4. - ISSN 0924-2716 - p. 409 - 426.
leaf-area index - radiative-transfer models - multiple linear-regression - resolution satellite data - band vegetation indexes - remote-sensing data - red edge position - nitrogen status - reflectance data - canopy reflectance
The study shows that leaf area index (LAI), leaf chlorophyll content (LCC) and canopy chlorophyll content (CCC) can be mapped in a heterogeneous Mediterranean grassland from canopy spectral reflectance measurements. Canopy spectral measurements were made in the field using a GER 3700 spectroradiometer, along with concomitant in situ measurements of LAI and LCC. We tested the utility of univariate techniques involving narrow band vegetation indices and the red edge inflection point, as well as multivariate calibration techniques, including stepwise multiple linear regression and partial least squares regression. Among the various investigated models, CCC was estimated with the highest accuracy (Rcv2 = 0.74, nRMSEcv = 0.35). All methods failed to estimate LCC (Rcv2 ¿ 0.40), while LAI was estimated with intermediate accuracy (Rcv2 values ranged from 0.49 to 0.69). Compared with narrow band indices and red edge inflection point, stepwise multiple linear regression generally improved the estimation of LAI. The estimations were further improved when partial least squares regression was used. When a subset of wavelengths was analyzed, it was found that partial least squares regression had reduced the error in the retrieved parameters. The results of the study highlight the significance of multivariate techniques, such as partial least squares regression, rather than univariate methods such as vegetation indices in estimating heterogeneous grass canopy characteristics
Influence of woody elements of a Norway spruce canopy on nadir reflectance simulated by the DART model at very high spatial resolution
Malenovsky, Z. ; Martin, E. ; Homolova, L. ; Gastellu-Etchegory, J.P. ; Zurita Milla, R. ; Schaepman, M.E. ; Pokorny, R. ; Clevers, J.G.P.W. ; Cudlin, P. - \ 2008
Remote Sensing of Environment 112 (2008)1. - ISSN 0034-4257 - p. 1 - 18.
leaf-area index - radiative-transfer models - net primary production - gross primary production - remotely-sensed data - modis-lai product - vegetation indexes - bidirectional reflectance - biophysical variables - spectral properties
A detailed sensitivity analysis investigating the effect of woody elements introduced into the Discrete Anisotropic Radiative Transfer (DART) model on the nadir bidirectional reflectance factor (BRF) for a simulated Norway spruce canopy was performed at a very high spatial resolution (modelling resolution 0.2 m, output pixel size 0.4 m). We used such a high resolution to be able to parameterize DART in an appropriate way and subsequently to gain detailed understanding of the influence of woody elements contributing to the radiative transfer within heterogeneous canopies. Three scenarios were studied by modelling the Norway spruce canopy as being composed of i) leaves, ii) leaves, trunks and first order branches, and finally iii) leaves, trunks, first order branches and small woody twigs simulated using mixed cells (i.e. cells approximated as composition of leaves and/or twigs turbid medium, and large woody constituents). The simulation of each scenario was performed for 10 different canopy closures (CC = 50¿95%, in steps of 5%), 25 leaf area index (LAI = 3.0¿15.0 m2 m¿ 2, in steps of 0.5 m2 m¿ 2), and in four spectral bands (centred at 559, 671, 727, and 783 nm, with a FWHM of 10 nm). The influence of woody elements was evaluated separately for both, sunlit and shaded parts of the simulated forest canopy, respectively. The DART results were verified by quantifying the simulated nadir BRF of each scenario with measured Airborne Imaging Spectroradiometer (AISA) Eagle data (pixel size of 0.4 m). These imaging spectrometer data were acquired over the same Norway spruce stand that was used to parameterise the DART model.
Estimating grassland biomass using SVM band shaving of hyperspectral data
Clevers, J.G.P.W. ; Heijden, G.W.A.M. van der; Verzakov, S. ; Schaepman, M.E. - \ 2007
Photogrammetric Engineering and Remote Sensing 73 (2007)10. - ISSN 0099-1112 - p. 1141 - 1148.
leaf-area index - radiative-transfer models - vegetation indexes - narrow-band - broad-band - precision agriculture - biophysical variables - spectrometer data - water index - reflectance
In this paper, the potential of a band shaving algorithm based on support vector machines (SVM) applied to hyperspectral data for estimating biomass within grasslands is studied. Field spectrometer data and biomass measurements were collected from a homogeneously managed grassland field. The SVM band shaving technique was compared with a partial least squares (PLS) and a stepwise forward selection analysis. Using their results, a range of vegetation indices was used as predictors for grassland biomass. Results from the band shaving showed that one band in the near-infrared region from 859 to 1,006 nm and one in the red-edge region from 668 to 776 nm used in the weighted difference vegetation index (WDVI) had the best predictive power, explaining 61 percent of grassland biomass variation. Indices based on short-wave infrared bands performed worse. Results could subsequently be applied to larger spatial extents using a high-resolution airborne digital camera (for example, Vexcel¿s UltraCamTM).
Spectrodirectional Remote Sensing: From Pixels to Processes
Schaepman, M.E. - \ 2007
International Journal of applied Earth Observation and Geoinformation 9 (2007)2. - ISSN 0303-2434 - p. 204 - 223.
radiative-transfer models - net primary productivity - imaging spectrometer aviris - comparing global-models - leaf-area index - terrestrial ecosystem - multiangular measurements - spectral reflectance - forest ecosystems - data assimilation
This paper discusses the historical evolution of imaging spectroscopy in Earth observation as well as directional (or multiangular) research leading to current achievements in spectrodirectional remote sensing. It elaborates on the evolution from two separate research areas into a common approach to quantify the interaction of light with the Earth surface. The contribution of spectrodirectional remote sensing towards an improved understanding of the Earth System is given by discussing the benefits of converging from individual pixel analysis to process models in the land-biosphere domain. The paper concludes with an outlook of research focus and upcoming areas of interest emphasizing towards multidisciplinary approaches using integrated system solutions based on remote and in situ sensing, data assimilation, and state space estimation algorithms.
A review on reflective remote sensing and data assimilation techniques for enhanced agroecosystem modeling
Dorigo, W.A. ; Zurita Milla, R. ; Wit, A.J.W. de; Brazile, J. ; Singh, R. ; Schaepman, M.E. - \ 2007
International Journal of applied Earth Observation and Geoinformation 9 (2007)2. - ISSN 0303-2434 - p. 165 - 193.
radiative-transfer models - leaf-area index - hyperspectral vegetation indexes - hydrologic data assimilation - multiple linear-regression - canopy chlorophyll density - ensemble kalman filter - bidirectional reflectance - soil-moisture - crop models
During the last 50 years, the management of agroecosystems has been undergoing major changes to meet the growing demand for food, timber, fibre and fuel. As a result of this intensified use, the ecological status of many agroecosystems has been severely deteriorated. Modeling the behavior of agroecosystems is, therefore, of great help since it allows the definition of management strategies that maximize (crop) production while minimizing the environmental impacts. Remote sensing can support such modeling by offering information on the spatial and temporal variation of important canopy state variables which would be very difficult to obtain otherwise. In this paper, we present an overview of different methods that can be used to derive biophysical and biochemical canopy state variables from optical remote sensing data in the VNIR-SWIR regions. The overview is based on an extensive literature review where both statistical¿empirical and physically based methods are discussed. Subsequently, the prevailing techniques of assimilating remote sensing data into agroecosystem models are outlined. The increasing complexity of data assimilation methods and of models describing agroecosystem functioning has significantly increased computational demands. For this reason, we include a short section on the potential of parallel processing to deal with the complex and computationally intensive algorithms described in the preceding sections. The studied literature reveals that many valuable techniques have been developed both for the retrieval of canopy state variables from reflective remote sensing data as for assimilating the retrieved variables in agroecosystem models. However, for agroecosystem modeling and remote sensing data assimilation to be commonly employed on a global operational basis, emphasis will have to be put on bridging the mismatch between data availability and accuracy on one hand, and model and user requirements on the other. This could be achieved by integrating imagery with different spatial, temporal, spectral, and angular resolutions, and the fusion of optical data with data of different origin, such as LIDAR and radar/microwave.
Monitoring wheat phenology and irrigation in Central Morocco: On the use of relationships between evapotranspiration, crops coefficients, leaf area index and remotely-sensed vegetation indices
Duchemin, B. ; Hadria, R. ; Erraki, S. ; Boulet, G. ; Maisongrande, P. ; Chehbouni, A. ; Escadafal, R. ; Ezzahar, J. ; Hoedjes, J.C.B. ; Kharrou, M.H. ; Khabba, S. ; Mougenot, B. ; Olioso, A. ; Rodriguez, J.C. ; Simonneaux, V. - \ 2006
Agricultural Water Management 79 (2006)1. - ISSN 0378-3774 - p. 1 - 27.
high-resolution radiometer - radiative-transfer models - sensing data assimilation - bidirectional reflectance - winter-wheat - command area - svat models - water - region - information
The monitoring of crop production and irrigation at a regional scale can be based on the use of ecosystem process models and remote sensing data. The former simulate the time courses of the main biophysical variables which affect crop photosynthesis and water consumption at a fine time step (hourly or daily); the latter allows to provide the spatial distribution of these variables over a region of interest at a time span from 10 days to a month. In this context, this study investigates the feasibility of using the normalised difference vegetation index (NDVI) derived from remote sensing data to provide indirect estimates of: (1) the leaf area index (LAI), which is a key-variable of many crop process models; and (2) crop coefficients, which represent the ratio of actual (AET) to reference (ET0) evapotranspiration. A first analysis is performed based on a dataset collected at field in an irrigated area of the Haouz plain (region of Marrakesh, Central Morocco) during the 2002-2003 agricultural season. The seasonal courses of NDVI, LAI, AET and ETO have been compared, then crop coefficients have been calculated using a method that allows roughly to separate soil evaporation from plant transpiration. This allows to compute the crop basal coefficient (K-cb) restricted to the plant transpiration process. Finally, three relationships have been established. The relationships between LAI and NDVI as well as between LAI and Kcb were found both exponential, with associated errors of 30% and 15%, respectively. Because the NDVI saturates at high LAI values (>4), the use of remotely-sensed data results in poor accuracy of LAI estimates for well-developed canopies. However, this inaccuracy was not found critical for transpiration estimates since AET appears limited to ETO for well-developed canopies. As a consequence, the relationship between NDVI and Kcb was found linear and of good accuracy (15%). Based on these relationships, maps of LAI and transpiration requirements have been derived from two Landsat7-ETM+ images acquired at the beginning and the middle of the agricultural season. These maps show the space and time variability in crop development and water requirements over a 3 km x 3 km irrigated area that surrounds the fields of study. They may give an indication on how the water should be distributed over the area of interest in order to improve the efficiency of irrigation. The availability, in the near future, of Earth Observation Systems designed to provide both high spatial resolution (10 m) and frequent revisit (day) would make it feasible to set up such approaches for the operational monitoring of crop phenology and irrigation at a regional scale.
Applicability of the PROSPECT model for Norway spruce needles
Malenovsky, Z. ; Albrechtova, J. ; Lhotakova, Z. ; Zurita Milla, R. ; Clevers, J.G.P.W. ; Schaepman, M.E. ; Cudlin, P. - \ 2006
International Journal of Remote Sensing 27 (2006)24/20. - ISSN 0143-1161 - p. 5315 - 5340.
chlorophyll content estimation - canopy reflectance models - radiative-transfer models - remote-sensing data - leaf-area index - optical-properties - conifer needles - forest - vegetation - inversion
The potential applicability of the leaf radiative transfer model PROSPECT (version 3.01) was tested for Norway spruce (Picea abies (L.) Karst.) needles collected from stress resistant and resilient trees. Direct comparison of the measured and simulated leaf optical properties between 450¿1000 nm revealed the requirement to recalibrate the PROSPECT chlorophyll and dry matter specific absorption coefficients kab(¿) and km(¿). The subsequent validation of the modified PROSPECT (version 3.01.S) showed close agreement with the spectral measurements of all three needle age¿classes tested; the root mean square error (RMSE) of all reflectance (¿) values within the interval of 450¿1000 nm was equal to 1.74%, for transmittance (¿) it was 1.53% and for absorbance (¿) it was 2.91%. The total chlorophyll concentration, dry matter content, and leaf water content were simultaneously retrieved by a constrained inversion of the original PROSPECT 3.01 and the adjusted PROSPECT 3.01.S. The chlorophyll concentration estimated by inversion of both model versions was similar, but the inversion accuracy of the dry matter and water content was significantly improved. Decreases in RMSE from 0.0079 g cm¿2 to 0.0019 g cm¿2 for dry matter and from 0.0019 cm to 0.0006 cm for leaf water content proved the improved performance of the recalibrated PROSPECT version 3.01.S.
Spectrodirectional remote sensing for the improved estimation of biophysical and -chemical variables: two case studies
Schaepman, M.E. ; Koetz, B. ; Schaepman-Strub, G. ; Itten, K.I. - \ 2005
International Journal of applied Earth Observation and Geoinformation 6 (2005)3-4. - ISSN 0303-2434 - p. 271 - 282.
leaf-area index - radiative-transfer models - surface albedo retrieval - water-content - land-surface - directional reflectance - vicarious calibration - data assimilation - alfalfa canopy - sail model
Over the past few years, significant advancements are made in the acquisition, processing, analysis and interpretation of quantitative directional and high spectral resolution data. In particular, the broader availability of air- and spaceborne directional imaging spectrometer data supports the estimation of biophysical and -chemical variables with unprecedented accuracy and in calibrated physical units. We describe in this paper two experiments that we carried out to demonstrate regional performance of spectral and directional-based retrieval approaches in vegetated areas. In the first case study, we focus on a mountain forest located in South-Eastern Switzerland representing a boreal forest like ecosystem. DAIS7915 imaging spectrometer data have been acquired with simultaneous ground measurements. We describe the soil–vegetation–atmosphere radiative transfer using a combination of the PROSPECT, GeoSAIL, and ATCOR models. In the second case study, we acquired spectrodirectional data on ground using a field goniometer in parallel with several HyMap imaging spectrometer overflights. Both cases demonstrate conditions for the estimation of biophysical and -chemical canopy properties with reduced uncertainties by respecting the full spectral coverage and directionality of the data. We conclude that the derived canopy variables represent the actual spatial distribution of properties as they occur in the landscape.