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Generation of spectral–temporal response surfaces by combining multispectral satellite and hyperspectral UAV imagery for precision agriculture applications
Gevaert, C. ; Suomalainen, J.M. ; Tang, J. ; Kooistra, L. - \ 2015
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 8 (2015)6. - ISSN 1939-1404 - p. 3140 - 3146.
leaf chlorophyll concentration - remote-sensing data - vegetation indexes - data fusion - reflectance - variability - prediction - landsat
Precision agriculture requires detailed crop status information at high spatial and temporal resolutions. Remote sensing can provide such information, but single sensor observations are often incapable of meeting all data requirements. Spectral–temporal response surfaces (STRSs) provide continuous reflectance spectra at high temporal intervals. This is the first study to combine multispectral satellite imagery (from Formosat-2) with hyperspectral imagery acquired with an unmanned aerial vehicle (UAV) to construct STRS. This study presents a novel STRS methodology which uses Bayesian theory to impute missing spectral information in the multispectral imagery and introduces observation uncertainties into the interpolations. This new method is compared to two earlier published methods for constructing STRS: a direct interpolation of the original data and a direct interpolation along the temporal dimension after imputation along the spectral dimension. The STRS derived through all three methods are compared to field measured reflectance spectra, leaf area index (LAI), and canopy chlorophyll of potato plants. The results indicate that the proposed Bayesian approach has the highest correlation (r = 0.953) and lowest RMSE (0.032) to field spectral reflectance measurements. Although the optimized soil-adjusted vegetation index (OSAVI) obtained from all methods have similar correlations to field data, the modified chlorophyll absorption in reflectance index (MCARI) obtained from the Bayesian STRS outperform the other two methods. A correlation of 0.83 with LAI and 0.77 with canopy chlorophyll measurements are obtained, compared to correlations of 0.27 and 0.09, respectively, for the directly interpolated STRS.
River flow regime and snow cover of the Pamir Alay (Central Asia) in a changing climate
Chevallier, P. ; Pouyaud, B. ; Mojaisky, M. ; Bolgov, M. ; Olsson, O. ; Bauer, M. ; Froebrich, J. - \ 2014
Hydrological Sciences Journal 59 (2014)8. - ISSN 0262-6667 - p. 1491 - 1506.
remote-sensing data - northern tien-shan - hydrological regime - water availability - glacier retreat - historical data - stereo imagery - aster imagery - mass balances - runoff
The Vakhsh and Pyandj rivers, main tributaries of the Amu Darya River in the mountainous region of the Pamir Alay, play an important role in the water resources of the Aral Sea basin (Central Asia). In this region, the glaciers and snow cover significantly influence the water cycle and flow regime, which could be strongly modified by climate change. The present study, part of a project funded by the European Commission, analyses the hydrological situation in six benchmark basins covering areas of between 1800 and 8400km(2), essentially located in Tajikistan, with a variety of topographical situations, precipitation amounts and glacierized areas. Four types of parameter are discussed: temperature, glaciation, snow cover and river flows. The study is based mainly on a long-time series that ended in the 1990s (with the collapse of the Soviet Union) and on field observations and data collection. In addition, a short, more recent period (May 2000 to May 2002) was examined to better understand the role of snow cover, using scarce monitored data and satellite information. The results confirm the overall homogeneous trend of temperature increase in the mountain range and its impacts on the surface water regime. Concerning the snow cover, significant differences are noted in the location, elevation, orientation and morphology of snow cover in the respective basins. The changes in the river flow regime are regulated by the combination of the snow cover dynamics and the increasing trend of the air temperature.
Derivation of Land Surface Albedo at High Resolution by Combining HJ-1A/B Reflectance Observations with MODIS BRDF Products
Gao, B. ; Jia, L. ; Wang, T.X. - \ 2014
Remote Sensing 6 (2014)9. - ISSN 2072-4292 - p. 8966 - 8985.
remote-sensing data - bidirectional reflectance - retrieval - algorithm - meteosat - polder/adeos - simulation - models
Land surface albedo is an essential parameter for monitoring global/regional climate and land surface energy balance. Although many studies have been conducted on global or regional land surface albedo using various remote sensing data over the past few decades, land surface albedo product with a high spatio-temporal resolution is currently very scarce. This paper proposes a method for deriving land surface albedo with a high spatio-temporal resolution (space: 30 m and time: 2-4 days). The proposed method works by combining the land surface reflectance data at 30 m spatial resolution obtained from the charge-coupled devices in the Huanjing-1A and -1B (HJ-1A/B) satellites with the Moderate Resolution Imaging Spectroradiometer (MODIS) land surface bidirectional reflectance distribution function (BRDF) parameters product (MCD43A1), which is at a spatial resolution of 500 m. First, the land surface BRDF parameters for HJ-1A/B land surface reflectance with a spatial-temporal resolutions of 30 m and 2-4 day are calculated on the basis of the prior knowledge from the MODIS BRDF product; then, the calculated high resolution BRDF parameters are integrated over the illuminating/viewing hemisphere to produce the white-and black-sky albedos at 30 m resolution. These results form the basis for the final land surface albedo derivation by accounting for the proportion of direct and diffuse solar radiation arriving at the ground. The albedo retrieved by this novel method is compared with MODIS land surface albedo products, as well as with ground measurements. The results show that the derived land surface albedo during the growing season of 2012 generally achieved a mean absolute accuracy of +/- 0.044, and a root mean square error of 0.039, confirming the effectiveness of the newly proposed method.
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.
Hyperspectral analysis of mangrove foliar chemistry using PLSR and support vector regression
Axelsson, C. ; Skidmore, A.K. ; Schlerf, M. ; Fauzi, A. ; Verhoef, W. - \ 2013
International Journal of Remote Sensing 34 (2013)5. - ISSN 0143-1161 - p. 1724 - 1743.
infrared reflectance spectroscopy - remote-sensing data - band-depth analysis - leaf-area index - nitrogen concentration - continuum removal - absorption features - deciduous forests - canopy nitrogen - pasture quality
Hyperspectral remote sensing enables the large-scale mapping of canopy biochemical properties. This study explored the possibility of retrieving the concentration of nitrogen, phosphorus, potassium, calcium, magnesium, and sodium from mangroves in the Berau Delta, Indonesia. The objectives of the study were to (1) assess the accuracy of foliar chemistry retrieval, (2) compare the performance of models based on support vector regression (SVR), i.e. e-SVR, ¿-SVR, and least squares SVR (LS-SVR), to models based on partial least squares regression (PLSR), and (3) investigate which spectral transformations are best suited. The results indicated that nitrogen could be successfully modelled at the landscape level (R 2 = 0.67, root mean square error (RMSE) = 0.17, normalized RMSE (nRMSE) = 15%), whereas estimations of P, K, Ca, Mg, and Na were less encouraging. The developed nitrogen model was applied over the study area to generate a map of foliar N variation, which can be used for studying ecosystem processes in mangroves. While PLSR attained good results directly using all untransformed bands, the highest accuracy for nitrogen modelling was achieved using a combination of LS-SVR and continuum-removed derivative reflectance. All SVR techniques suffered from multicollinearity when using the full spectrum, and the number of independent variables had to be reduced by singling out the most informative wavelength bands. This was achieved by interpreting and visualizing the structure of the PLSR and SVR models.
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 (
Soil salinity development in the Yellow River Delta in relation to groundwater dynamics
Fan Xiaomei, ; Pedroli, B. ; Liu Gaohuan, ; Liu Qingsheng, ; Liu Hongguang, ; Shu Longcang, - \ 2012
Land Degradation and Development 23 (2012)2. - ISSN 1085-3278 - p. 175 - 189.
remote-sensing data - salt-affected soils - salinization - indicators - vegetation - wetland - land
The Yellow River Delta occupies an important position in the global ecosystem because of its valuable wetland habitat resources for migratory birds on the Eastern Pacific migration route. However, it has suffered from severe land degradation because of soil salinization. This paper assesses the distribution maps of saline soils during the past two decades, using field observations at three points in time using remote sensing images for the same periods, in combination with spatial models. Soil salinization appears to have expanded from the coastline to inland areas of the Yelow River Delta at a surprising speed during that period. The spatio-temporal dynamics of the groundwater table and total dissolved solids (TDS) during the last 20 years were analyzed using maps based on Kriging interpolation. Kriging helped substantially to improve the accurateness of the predicted values of soil salt content, using a random subsample of the observation points as validation basis. Correlation analysis of the spatial data revealed that the distribution and evolution of saline soils are closely related to the dynamics of groundwater: the aggravation of soil salinization is associated with a rising groundwater table and increasing TDS. Insufficient recharge of the groundwater with fresh surface water due to reduced Yellow River discharge and subsequent seawater intrusion are therefore serious environmental problems in the Yellow River Delta ecosystem
Mapping grassland leaf area index with airborne hyperspectral imagery : a comparison study of statistical approaches and inversion of radiative transfer models
Darvishzadeh, R. ; Atzberger, C. ; Skidmore, A.K. ; Schlerf, M. - \ 2011
ISPRS Journal of Photogrammetry and Remote Sensing 66 (2011)6. - ISSN 0924-2716 - p. 894 - 906.
canopy biophysical variables - crop chlorophyll content - band vegetation indexes - remote-sensing data - red edge position - reflectance data - optical-properties - heterogeneous grassland - ancillary information - precision agriculture
Statistical and physical models have seldom been compared in studying grasslands. In this paper, both modeling approaches are investigated for mapping leaf area index (LAI) in a Mediterranean grassland (Majella National Park, Italy) using HyMap airborne hyperspectral images. We compared inversion of the PROSAIL radiative transfer model with narrow band vegetation indices (NDVI-like and SAVI2-like) and partial least squares regression (PLS). To assess the performance of the investigated models, the normalized RMSE (nRMSE) and R2 between in situ measurements of leaf area index and estimated parameter values are reported. The results of the study demonstrate that LAI can be estimated through PROSAIL inversion with accuracies comparable to those of statistical approaches (R2 = 0.89, nRMSE = 0.22). The accuracy of the radiative transfer model inversion was further increased by using only a spectral subset of the data (R2 = 0.91, nRMSE = 0.18). For the feature selection wavebands not well simulated by PROSAIL were sequentially discarded until all bands fulfilled the imposed accuracy requirements.
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.
Crop Reflectance as a tool for the online monitoring of LAI and PAR interception in two different greenhouse Crops
Sarlikioti, V. ; Meinen, E. ; Marcelis, L.F.M. - \ 2011
Biosystems Engineering 108 (2011)2. - ISSN 1537-5110 - p. 114 - 120.
leaf-area index - remote-sensing data - photosynthesis - vegetation - models - yield
Methods for the online monitoring of Leaf Area Index (LAI) and photosynthetically active radiation (PAR) interception of the canopy, in greenhouse conditions, using reflectance measurements of the PAR part of the spectrum for tomato and sweet pepper were investigated. LAI and PAR interception were measured at the same moments as reflectance at six wavelengths at different plant developmental stages in greenhouses. The normalised difference vegetation index (NDVI) was also calculated. Relationships between the measured parameters were established in experimental greenhouses and subsequently tested in commercial greenhouses. The best estimates for LAI and PAR interception for both tomato and sweet pepper were obtained from reflectance at 460 nm. The goodness of the fit was validated with data from the commercial greenhouses was also tested. The divergence of the results from those reported from field experiments can be explained by the special environment that occurs in the greenhouse, where there are more sources of reflectance due to the greenhouse frame and the white plastic that covered the floor. Thus, this new approach to estimating LAI and PAR interception using 460 nm is promising and could play a role in the decision support systems that are used in modern greenhouse management.
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
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.
Inversion of a radiative transfer model for estimating vegetation LAI and chlorophyll in a heterogeneous grassland
Darvishzadeh, R. ; Skidmore, A.K. ; Schlerf, M. ; Atzberger, C. - \ 2008
Remote Sensing of Environment 112 (2008)5. - ISSN 0034-4257 - p. 2592 - 2604.
leaf-area index - canopy biophysical variables - remote-sensing data - reflectance data - optical-properties - neural-network - ancillary information - precision agriculture - satellite data - retrieval
Radiative transfer models have seldom been applied for studying heterogeneous grassland canopies. Here, the potential of radiative transfer modeling to predict LAI and leaf and canopy chlorophyll contents in a heterogeneous Mediterranean grassland is investigated. The widely used PROSAIL model was inverted with canopy spectral reflectance measurements by means of a look-up table (LUT). Canopy spectral measurements were acquired in the field using a GER 3700 spectroradiometer, along with simultaneous in situ measurements of LAI and leaf chlorophyll content. We tested the impact of using multiple solutions, stratification (according to species richness), and spectral subsetting on parameter retrieval. To assess the performance of the model inversion, the normalized RMSE and R-2 between independent in situ measurements and estimated parameters were used. Of the three investigated plant characteristics, canopy chlorophyll content was estimated with the highest accuracy (R-2 = 0.70, NRMSE = 0.18). Leaf chlorophyll content, on the other hand, could not be estimated with acceptable accuracy, while LAI was estimated with intermediate accuracy (R-2 = 0.59, NRMSE = 0.18). When only sample plots with up to two species were considered (n = 107), the estimation accuracy for all investigated variables (LAI, canopy chlorophyll content and leaf chlorophyll content) increased (NRMSE=0.14, 0.16, 0.19, respectively). This shows the limits of the PROSAIL radiative transfer model in the case of very heterogeneous conditions. We also found that a carefully selected spectral subset contains sufficient information for a successful model inversion. Our results confirm the potential of model inversion for estimating vegetation biophysical parameters at the canopy scale in (moderately) heterogeneous grasslands using hyperspectral measurements. (C) 2008 Elsevier Inc. All rights reserved.
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
Satellite-based monitoring of tropical seagrass vegetation: current techniques and future developments
Ferwerda, J.G. ; Leeuw, J. de; Atzberger, C. ; Vekerdy, Z. - \ 2007
Hydrobiologia 591 (2007)1. - ISSN 0018-8158 - p. 59 - 71.
kruger-national-park - remote-sensing data - great-barrier-reef - water-quality - hyperspectral imagery - species-composition - posidonia-oceanica - biooptical model - chelonia-mydas - south-africa
Decline of seagrasses has been documented in many parts of the world. Reduction in water clarity, through increased turbidity and increased nutrient concentrations, is considered to be the primary cause of seagrass loss. Recent studies have indicated the need for new methods that will enable early detection of decline in seagrass extent and productivity, over large areas. In this review of current literature on coastal remote sensing, we examine the ability of remote sensing to serve as an information provider for seagrass monitoring. Remote sensing offers the potential to map the extent of seagrass cover and monitor changes in these with high accuracy for shallow waters. The accuracy of mapping seagrasses in deeper waters is unclear. Recent advances in sensor technology and radiometric transfer modelling have resulted in the ability to map suspended sediment, sea surface temperature and below-surface irradiance. It is therefore potentially possible to monitor the factors that cause the decline in seagrass status. When the latest products in remote sensing are linked to seagrass production models, it may serve as an early-warning system for seagrass decline and ultimately allow a better management of these susceptible ecosystems
Crop model data assimilation with the Ensemble Kalman filter for improving regional crop yield forecasts
Wit, A.J.W. de; Diepen, C.A. van - \ 2007
Agricultural and Forest Meteorology 146 (2007)1-2. - ISSN 0168-1923 - p. 38 - 56.
remote-sensing data - soil-moisture - spatial variability - ers scatterometer - united-states - scales - parameters - wheat - reflectances - uncertainty
Uncertainty in spatial and temporal distribution of rainfall in regional crop yield simulations comprises a major fraction of the error on crop model simulation results. In this paper we used an Ensemble Kalman filter (EnKF) to assimilate coarse resolution satellite microwave sensor derived soil moisture estimates (SWI) for correcting errors in the water balance of the world food studies (WOFOST) crop model. Crop model simulations with the EnKF for winter wheat and grain maize were carried out for Spain, France, Italy and Germany for the period 1992¿2000. The results were evaluated on the basis of regression with known crop yield statistics at national and regional level. Moreover, the EnKF filter innovations were analysed to see if any systematic trends could be found that could indicate deficiencies in the WOFOST water balance. Our results demonstrate that the assimilation of SWI has clearly improved the relationship with crop yield statistics for winter wheat for the majority of regions (66%) where a relationship could be established. For grain maize the improvement is less evident because improved relationships could only be found for 56% of the regions. We suspect that partial crop irrigation could explain the relatively poor results for grain maize, because irrigation is not included in the model. Analyses of the filter innovations revealed spatial and temporal patterns, while the distribution of normalised innovations is not Gaussian and has a non-zero mean indicating that the EnKF performs suboptimal. The non-zero mean is caused by differences in the mean value of the forecasted and observed soil moisture climatology, while the excessive spread in the distribution of normalised innovations indicates that the error covariances of forecasts and observations have been underestimated. These results clearly indicate that additional sources of error need to be included in the simulations and observations.
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.
Radiative transfer modeling within a heterogeneous canopy for estimation of forest fire fuel properties
Koetz, B. ; Schaepman, M.E. ; Morsdorf, F. ; Bowyer, P. ; Itten, K.I. ; Allgower, B. - \ 2004
Remote Sensing of Environment 92 (2004)3. - ISSN 0034-4257 - p. 332 - 344.
leaf-area index - airborne imaging spectrometry - remote-sensing data - chlorophyll content estimation - equivalent water thickness - reflectance model - vegetation water - bidirectional reflectance - hyperspectral data - danger assessment
Imaging spectrometer data were acquired over conifer stands to retrieve spatially distributed information on canopy structure and foliage water content, which may be used to assess fire risk and to manage the impact of forest fires. The study relied on a comprehensive field campaign using stratified systematic unaligned sampling ranging from full spectroradiometric characterization of the canopy to conventional measurements of biochemical and biophysical variables. Airborne imaging spectrometer data (DAIS7915 and ROSIS) were acquired parallel to the ground measurements, describing the canopy reflectance of the observed forest. Coniferous canopies are highly heterogeneous and thus the transfer of incident radiation within the canopy is dominated by its structure. We demonstrated the viability of radiative transfer representation and compared the performance of two hybrid canopy reflectance models, GeoSAIL and FLIGHT, within this heterogeneous medium. Despite the different nature and canopy representation of these models, they yielded similar results. Subsequently, the inversion of a hyperspectral GeoSAIL version demonstrated the feasibility of estimating structure and foliage water content of a coniferous canopy based on radiative transfer modeling. Estimates of the canopy variables showed reasonably accurate results and were validated through ground measurements. (C) 2004 Elsevier Inc. All rights reserved.