Estimation of spruce needle-leaf chlorophyll content based on DART and PARAS canopy reflectance models
Yanez Rausell, L. ; Malenovsky, Z. ; Rautiainen, M. ; Clevers, J.G.P.W. ; Lukes, P. ; Hanus, J. ; Schaepman, M.E. - \ 2015
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 8 (2015)4. - ISSN 1939-1404 - p. 1534 - 1544.
photon recollision probability - area index - spectral invariants - forest - prospect - stands - simulations - resolution - retrieval - lai-2000
Needle-leaf chlorophyll content (Cab) of a Norway spruce stand was estimated from CHRIS-PROBA images using the canopy reflectance simulated by the PROSPECT model coupled with two canopy reflectance models: 1) discrete anisotropic radiative transfer model (DART); and 2) PARAS. The DART model uses a detailed description of the forest scene, whereas PARAS is based on the photon recollision probability theory and uses a simplified forest structural description. Subsequently, statistically significant empirical functions between the optical indices ANCB670-720 and ANMB670-720 and the needle-leaf Cab content were established and then applied to CHRIS-PROBA data. The Cab estimating regressions using ANMB670_720 were more robust than using ANCB670-720 since the latter was more sensitive to LAI, especially in case of PARAS. Comparison between Cab estimates showed strong linear correlations between PARAS and DART retrievals, with a nearly perfect one-to-one fit when using ANMB670-720 (slope = 1.1, offset = 11 µg · cm-2). Further comparison with Cab estimated from an AISA Eagle image of the same stand showed better results for PARAS (RMSE = 2.7 µg · cm-2 for ANCB670-720; RMSE = 9.5 µg · cm-2 for ANMB670_720) than for DART (RMSE = 7.5 µg · cm-2 for ANCB670-720; RMSE = 23 µg · cm-2 for ANMB670-720). Although these results show the potential for simpler models like PARAS in estimating needle-leaf Cab from satellite imaging spectroscopy data, further analyses regarding parameterization of radiative transfer models are recommended.
Yield estimation using SPOT-VEGETATION products: A case study of wheat in European countries
Kowalik, W. ; Dabrowska-Zielinska, K. ; Meroni, M. ; Raczka, T.U. ; Wit, A.J.W. de - \ 2014
International Journal of applied Earth Observation and Geoinformation 32 (2014). - ISSN 0303-2434 - p. 228 - 239.
ndvi time-series - crop yield - modis data - grain yields - area index - leaf-area - model - prediction - networks - drought
In the period 1999-2009 ten-day SPOT-VEGETATION products of the Normalized Difference Vegetation Index (NDVI) and Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) at 1 km spatial resolution were used in order to estimate and forecast the wheat yield over Europe. The products were used together with official wheat yield statistics to fine-tune a statistical model for each NUTS2 region, based on the Partial Least Squares Regression (PLSR) method. This method has been chosen to construct the model in the presence of many correlated predictor variables (10-day values of remote sensing indicators) and a limited number of wheat yield observations. The model was run in two different modalities: the "monitoring mode", which allows for an overall yield assessment at the end of the growing season, and the "forecasting mode", which provides early and timely yield estimates when the growing season is on-going. Performances of yield estimation at the regional and national level were evaluated using a cross-validation technique against yield statistics and the estimations were compared with those of a reference crop growth model. Models based on either NDVI or FAPAR normalized indicators achieved similar results with a minimal advantage of the model based on the FAPAR product. Best modelling results were obtained for the countries in Central Europe (Poland, North-Eastern Germany) and also Great Britain. By contrast, poor model performances characterize countries as follows: Sweden, Finland, Ireland, Portugal, Romania and Hungary. Country level yield estimates using the PLSR model in the monitoring mode, and those of a reference crop growth model that do not make use of remote sensing information showed comparable accuracies. The largest estimation errors were observed in Portugal, Spain and Finland for both approaches. This convergence may indicate poor reliability of the official yield statistics in these countries. (C) 2014 Elsevier B.V. All rights reserved,
Comparison of remote sensing and plant trait-based modelling to predict ecosystem services in subalpine grasslands
Homolova, L. ; Schaepman, M.E. ; Lamarque, P. ; Clevers, J.G.P.W. ; Bello, F. de; Thuiller, W. ; Lavorel, S. - \ 2014
Ecosphere 5 (2014)8. - ISSN 2150-8925
land-use change - leaf chlorophyll content - imaging spectroscopy - water-content - aviris data - spectral reflectance - hyperspectral data - species richness - area index - vegetation
There is a growing demand for spatially explicit assessment of multiple ecosystem services (ES) and remote sensing (RS) can provide valuable data to meet this challenge. In this study, located in the Central French Alps, we used high spatial and spectral resolution RS images to assess multiple ES based on underpinning ecosystem properties (EP) of subalpine grasslands. We estimated five EP (green biomass, litter mass, crude protein content, species diversity and soil carbon content) from RS data using empirical RS methods and maps of ES were calculated as simple linear combinations of EP. Additionally, the RS-based results were compared with results of a plant trait-based statistical modelling approach that predicted EP and ES from land use, abiotic and plant trait data (modelling approach). The comparison between the RS and the modelling approaches showed that RS-based results provided better insight into the fine-grained spatial distribution of EP and thereby ES, whereas the modelling approach reflected the land use signal that underpinned trait-based models of EP. The spatial agreement between the two approaches at a 20-m resolution varied between 16 and 22% for individual EP, but for the total ecosystem service supply it was only 7%. Furthermore, the modelling approach identified the alpine grazed meadows land use class as areas with high values of multiple ES (hot spots) and mown-grazed permanent meadows as areas with low values and only few ES (cold spots). Whereas the RS-based hot spots were a small subset of those predicted by the modelling approach, cold spots were rather scattered, small patches with limited overlap with the modelling results. Despite limitations associated with timing of assessment campaigns and field data requirements, RS offers valuable data for spatially continuous mapping of EP and can thus supply RS-based proxies of ES. Although the RS approach was applied to a limited area and for one type of ecosystem, we believe that the broader availability of high fidelity airborne and satellite RS data will promote RS-based assessment of ES to larger areas and other ecosystems.
Retrieval of spruce leaf chlorophyll content from airborne image data using continuum removal and radiative transfer
Malenovsky, Z. ; Homolova, L. ; Zurita-Milla, R. ; Lukes, P. ; Kaplan, V. ; Hanus, J. ; Gastellu-Etchegorry, J.P. ; Schaepman, M.E. - \ 2013
Remote Sensing of Environment 131 (2013). - ISSN 0034-4257 - p. 85 - 102.
canopy reflectance models - optical-properties model - area index - hyperspectral data - forest canopies - precision agriculture - vegetation canopies - red - band - absorption
We investigate combined continuum removal and radiative transfer (RT) modeling to retrieve leaf chlorophyll a & b content (Cab) from the AISA Eagle airborne imaging spectrometer data of sub-meter (0.4 m) spatial resolution. Based on coupled PROSPECT-DART RT simulations of a Norway spruce (Picea abies (L.) Karst.) stand, we propose a new Cab sensitive index located between 650 and 720 nm and termed ANCB650–720. The performance of ANCB650–720 was validated against ground-measured Cab of ten spruce crowns and compared with Cab estimated by a conventional artificial neural network (ANN) trained with continuum removed RT simulations and also by three previously published chlorophyll optical indices: normalized difference between reflectance at 925 and 710 nm (ND925&710), simple reflectance ratio between 750 and 710 nm (SR750/710) and the ratio of TCARI/OSAVI indices. Although all retrieval methods produced visually comparable Cab spatial patterns, the ground validation revealed that the ANCB650–720 and ANN retrievals are more accurate than the other three chlorophyll indices (R2 = 0.72 for both methods). ANCB650–720 estimated Cab with an RMSE = 2.27 µg cm- 2 (relative RRMSE = 4.35%) and ANN with an RMSE = 2.18 µg cm- 2 (RRMSE = 4.18%), while SR750/710 with an RMSE = 4.16 µg cm- 2 (RRMSE = 7.97%), ND925&710 with an RMSE = 9.07 µg cm- 2 (RRMSE = 17.38%) and TCARI/OSAVI with an RMSE = 12.30 µg cm- 2 (RRMSE = 23.56%). Also the systematic RMSES was lower than the unsystematic one only for the ANCB650–720 and ANN retrievals. Our results indicate that the newly proposed index can provide the same accuracy as ANN except for Cab values below 30 µg cm- 2, which are slightly overestimated (RMSE = 2.42 µg cm- 2). The computationally efficient ANCB650–720 retrieval provides accurate high spatial resolution airborne Cab maps, considerable as a suitable reference data for validating satellite-based Cab products.
|Evaluation of spectral reflectance of seven Iranian rice varieties canopies
Darvishsefat, A.A. ; Abbasi, M. ; Schaepman, M.E. - \ 2011
Journal of Agricultural Science and Technology (JAST) 13 (2011)Supplement. - ISSN 1680-7073 - p. 1091 - 1104.
leaf chlorophyll content - photosynthetic efficiency - hyperspectral reflectance - vegetation indexes - nitrogen status - area index - paddy rice - leaves - water - parameters
Rice cultivated areas and yield information is indispensable for sustainable management and economic policy making for this strategic food crop. Introduction of high spectral and special resolution satellite data has enabled production of such information in a timely and accurate manner. Knowledge of the spectral reflectance of various land covers is a prerequisite for their identification and study. Evaluation of the spectral reflectance of plants using field spectroradiometry provides the possibility to identify and map different rice varieties especially while using hyperspectral remote sensing. This paper reports the results of the first attempt to evaluate spectral signatures of seven north Iranian rice varieties (Fajr, Hybrid, Khazar, Nemat, Neda, Shiroudi and Tarom plots) in the experimental station of the Iranian Rice Research Institute (main station in Amol, Mazanderan Province). Measurements were carried out using a field spectroradiometer in the range of 350-2,500 nm under natural light and environmental conditions. In order to eliminate erroneous data and also experimental errors in spectral reflectance curves, all curves were individually quality controlled. A set of important vegetation indices sensitive to canopy chlorophyll content, photosynthesis intensity, nitrogen and water content were employed to enhance probable differences in spectral reflectance among various rice varieties. Analysis of variance and Tukey's paired test were then used to compare rice varieties. Using Datt and PRI1 indices, significant differences (a=0.01) were found among rice varieties reflectances in 19 out of 21 cases. This promises the possibility of accurate mapping of rice varieties cultivated areas based on hyperspectral remotely sensed data.
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
Mapping beech (Fagus sylvatica L.) forest structure with airborne hyperspectral imagery
Cho, M.A. ; Skidmore, A.K. ; Sobhan, I. - \ 2009
International Journal of applied Earth Observation and Geoinformation 11 (2009)3. - ISSN 0303-2434 - p. 201 - 211.
least-squares regression - red-edge - area index - chlorophyll estimation - spatial heterogeneity - imaging spectrometry - canopy reflectance - vegetation indexes - biomass - stand
Estimating forest structural attributes using multispectral remote sensing is challenging because of the saturation of multispectral indices at high canopy cover. The objective of this study was to assess the utility of hyperspectral data in estimating and mapping forest structural parameters including mean diameter-at-breast height (DBH), mean tree height and tree density of a closed canopy beech forest (Fagus sylvatica L.). Airborne HyMap images and data on forest structural attributes were collected from the Majella National Park, Italy in July 2004. The predictive performances of vegetation indices (VI) derived from all possible two-band combinations (VI(i,j) = (Ri - Rj)/(Ri + Rj), where Ri and Rj = reflectance in any two bands) were evaluated using calibration (n = 33) and test (n = 20) data sets. The potential of partial least squares (PLS) regression, a multivariate technique involving several bands was also assessed. New VIs based on the contrast between reflectance in the red-edge shoulder (756-820 nm) and the water absorption feature centred at 1200 nm (1172-1320 nm) were found to show higher correlations with the forest structural parameters than standard VIs derived from NIR and visible reflectance (i.e. the normalised difference vegetation index, NDVI). PLS regression showed a slight improvement in estimating the beech forest structural attributes (prediction errors of 27.6%, 32.6% and 46.4% for mean DBH, height and tree density, respectively) compared to VIs using linear regression models (prediction errors of 27.8%, 35.8% and 48.3% for mean DBH, height and tree density, respectively). Mean DBH was the best predicted variable among the stand parameters (calibration R2 = 0.62 for an exponential model fit and standard error of prediction = 5.12 cm, i.e. 25% of the mean). The predicted map of mean DBH revealed high heterogeneity in the beech forest structure in the study area. The spatial variability of mean DBH occurs at less than 450 m. The DBH map could be useful to forest management in many ways, e.g. thinning of coppice to promote diameter growth, to assess the effects of management on forest structure or to detect changes in the forest structure caused by anthropogenic and natural factors
The effects of high soil CO2 concentrations on leaf reflectance of maize plants
Noomen, M.F. ; Skidmore, A.K. - \ 2009
International Journal of Remote Sensing 30 (2009)2. - ISSN 0143-1161 - p. 481 - 497.
red edge position - carbon-dioxide - chlorophyll concentration - nitrogen concentration - spectral reflectance - model simulation - root respiration - wheat genotypes - area index - leaves
Carbon dioxide gas at higher concentrations is known to kill vegetation and can also lead to asphyxiation in humans and animals. The objective of this study is to test whether soil CO2 concentrations ranging from 2% to 50% can be detected using vegetative spectral reflectance. A greenhouse experiment was performed to measure the reflectance of maize plants growing in soil contaminated with high concentrations of CO2. The correlation between leaf chlorophyll and reflectance in both the red edge and the yellow region was studied using different methods. The method that resulted in the strongest correlation between leaf reflectance and chlorophyll was subsequently used to study the effects of CO2 on plant health. The results showed that the method developed by Cho and Skidmore (2006) was the most accurate in predicting leaf chlorophyll (R 2 of 0.72). This index in combination with a new index proposed in this study¿named the yellow edge position or YEP¿showed that an increase in CO2 concentration corresponds to a decrease in leaf chlorophyll. Two first derivative water absorption features at 1400 and 1900 nm indicate that a concentration of 50% CO2 decreased leaf water content. Although upscaling to canopy reflectance is necessary, this experiment shows that leaf reflectance can be used to detect high soil CO2 concentrations, particularly halfway through the growing season.
Towards red-edge positions less sensitive to canopy biophysical parameters for leaf chlorophyll estimation using properties optique spectrales des feuilles (PROSPECT) and scattering by arbitrarily inclined leaves (SAILH) simulated data
Cho, M.A. ; Skidmore, A.K. ; Atzberger, C. - \ 2008
International Journal of Remote Sensing 29 (2008)8. - ISSN 0143-1161 - p. 2241 - 2255.
hyperspectral vegetation indexes - area index - photosynthetic efficiency - precision agriculture - optical-properties - remote estimation - reflectance data - model - nitrogen - green
Several methods for extracting the chlorophyll sensitive red-edge position (REP) from hyperspectral data are reported in literature. This study is a continuation of a recent paper published as 'A new technique for extracting the red edge position from hyperspectral data: the linear extrapolation method'. The method was validated experimentally for estimation of foliar nitrogen concentrations of rye, maize and mixed grass/herb. The objective of this study was to test the utility of the linear extrapolation method under different conditions including variable canopy biophysical parameters, solar zenith angle, sensor noise and spectral bandwidth. REPs were extracted from synthetic canopy spectra that were simulated using properties optique spectrales des feuilles (PROSPECT) and scattering by arbitrarily inclined leaves (SAILH) radiative transfer models. REPs extracted by the linear extrapolation method involving wavebands at 680, 694, 724 and 760 nm produced the highest correlation (R2=0.75) with leaf chlorophyll content with minimal effects of leaf and canopy biophysical confounders (leaf area index, leaf inclination distribution and leaf dry matter content) compared to traditional techniques including the linear interpolation, inverted Gaussian modelling and polynomial fitting techniques. In addition, the new technique is insensitive to changes in solar zenith angle. However, the advantage of using the linear extrapolation method compared to the various alternative methods diminishes with increasing sensor noise and decreasing spectral resolution. In summary, the linear extrapolation technique confirms its high potential for leaf chlorophyll estimation. The efficacy of the technique under field conditions needs to be established
Estimation of green grass/herb biomass from airborne hyperspectral imagery using spectral indices and partial least squares regression
Cho, M.A. ; Skidmore, A.K. ; Corsi, F. ; Wieren, S.E. van; Sobhan, I. - \ 2007
International Journal of applied Earth Observation and Geoinformation 9 (2007)4. - ISSN 0303-2434 - p. 414 - 424.
vegetation indexes - area index - red edge - biophysical relationships - imaging spectrometry - aboveground biomass - canopy reflectance - nitrogen status - leaves - contamination
The main objective was to determine whether partial least squares (PLS) regression improves grass/herb biomass estimation when compared with hyperspectral indices, that is normalised difference vegetation index (NDVI) and red-edge position (REP). To achieve this objective, fresh green grass/herb biomass and airborne images (HyMap) were collected in the Majella National Park, Italy in the summer of 2005. The predictive performances of hyperspectral indices and PLS regression models were then determined and compared using calibration (n = 30) and test (n = 12) data sets. The regression model derived from NDVI computed from bands at 740 and 771 nm produced a lower standard error of prediction (SEP = 264 g m¿2) on the test data compared with the standard NDVI involving bands at 665 and 801 nm (SEP = 331 g m¿2), but comparable results with REPs determined by various methods (SEP = 261 to 295 g m¿2). PLS regression models based on original, derivative and continuum-removed spectra produced lower prediction errors (SEP = 149 to 256 g m¿2) compared with NDVI and REP models. The lowest prediction error (SEP = 149 g m¿2, 19% of mean) was obtained with PLS regression involving continuum-removed bands. In conclusion, PLS regression based on airborne hyperspectral imagery provides a better alternative to univariate regression involving hyperspectral indices for grass/herb biomass estimation in the Majella National Park.
A new technique for extracting the red edge position from hyperspectral data : the linear extrapolation method
Cho, M.A. ; Skidmore, A.K. - \ 2006
Remote Sensing of Environment 101 (2006)2. - ISSN 0034-4257 - p. 181 - 193.
plant leaf reflectance - chlorophyll concentration - vegetation indexes - nitrogen status - canopy scales - area index - leaves - spectroscopy - variability - stress
There is increasing interest in using hyperspectral data for quantitative characterization of vegetation in spatial and temporal scopes. Many spectral indices are being developed to improve vegetation sensitivity by minimizing the background influence. The chlorophyll absorption continuum index (CACI) is such a measure to calculate the spectral continuum on which the analyses are based on the area of the troughs spanned by the spectral continuum. However, different values of CACI were obtained in this method because different positions of continuums were determined by different users. Furthermore, the sensitivity of CACI to agronomic parameters such as green leaf chlorophyll density (GLCD) has been reduced because the fixed positions of continuums are determined when the red edge shifted with the change in GLCD. A modified chlorophyll absorption continuum index (MCACI) is presented in this article. The red edge inflection point (REIP) replaces the maximum reflectance point (MRP) in near-infrared (NIR) shoulder on the CACI continuum. This MCACI has been proved to increase the sensitivity and predictive power of GLCD.