TY - JOUR ID - dash2004 AU - Dash, J. AU - Curran, P. J. TI - The MERIS terrestrial chlorophyll index UR - http://dx.doi.org/10.1080/0143116042000274015 DO - 10.1080/0143116042000274015 PR - Taylor & Francis T2 - International Journal of Remote Sensing PY - 2004 DA - 2004/12/01 SN - 0143-1161 VL - 25 IS - 23 SP - 5403-5413 AB - The long wavelength edge of the major chlorophyll absorption feature in the spectrum of a vegetation canopy moves to longer wavelengths with an increase in chlorophyll content. The position of this red-edge has been used successfully to estimate, by remote sensing, the chlorophyll content of vegetation canopies. Techniques used to estimate this red-edge position (REP) have been designed for use on small volumes of continuous spectral data rather than the large volumes of discontinuous spectral data recorded by contemporary satellite spectrometers. Also, each technique produces a different value of REP from the same spectral data and REP values are relatively insensitive to chlorophyll content at high values of chlorophyll content. This paper reports on the design and indirect evaluation of a surrogate REP index for use with spectral data recorded at the standard band settings of the Medium Resolution Imaging Spectrometer (MERIS). This index, termed the MERIS terrestrial chlorophyll index (MTCI), was evaluated using model spectra, field spectra and MERIS data. It was easy to calculate (and so can be automated), was correlated strongly with REP but unlike REP was sensitive to high values of chlorophyll content. As a result this index became an official MERIS level-2 product of the European Space Agency in March 2004. Further direct evaluation of the MTCI is proposed, using both greenhouse and field data. ER - TY - JOUR ID - dorigo2007 AU - Dorigo, W. A. AU - Zurita-Milla, R. AU - de Wit, A. J. W. AU - Brazile, J. AU - Singh, R. AU - Schaepman, M. E. TI - A review on reflective remote sensing and data assimilation techniques for enhanced agroecosystem modeling UR - ://WOS:000246320700008 DO - 10.1016/j.jag.2006.05.003 T2 - International Journal of Applied Earth Observation and Geoinformation PY - 2007 DA - May SN - 0303-2434 VL - 9 IS - 2 SP - 165-193 N1 - ISI Document Delivery No.: 165QP Times Cited: 39 Cited Reference Count: 181 Dorigo, W. A. Zurita-Milla, R. de Wit, A. J. W. Brazile, J. Singh, R. Schaepman, M. E. Conference on Advances in Airborne Electromagnetics and Remote Sensing of Agro-Ecosystems 2004 Wageningen, NETHERLANDS Elsevier science bv Amsterdam AB - 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. (c) 2006 Elsevier B.V. All rights reserved. KW - data assimilation KW - agroecosystem modeling KW - vegetation indices KW - canopy KW - reflectance modeling KW - biophysical variables KW - biochemical variables KW - parallel processing KW - radiative-transfer models KW - leaf-area index KW - hyperspectral vegetation KW - indexes KW - hydrologic data assimilation KW - multiple linear-regression KW - canopy chlorophyll density KW - ensemble kalman filter KW - bidirectional KW - reflectance KW - soil-moisture KW - crop models ER - TY - GEN ID - hunt_jr_2011 AU - Hunt Jr., E. Raymond AU - Daughtry, C. S. T. AU - Eitel, Jan U. H. AU - Long, D. S. TI - Remote Sensing Leaf Chlorophyll Content Using a Visible Band Index T2 - Agronomy Journal PY - 2011 VL - 103 SP - 1090-1099 AB - Leaf chlorophyll content is an important variable for agricultural remote sensing because of its close relationship to leaf nitrogen content. We propose the triangular greenness index (TGI), which calculates the area of a triangle with three vertices: ('r, Rr), ('g, Rg), and ('b, Rb), where ' is the wavelength (nm) and R is the reflectance for bands in red (r), green (g), and blue (b) wavelengths. TGI was correlated with chlorophyll content using a variety of leaf and plot reflectance data. Generally, indices using the chlorophyll red-edge (710-730 nm) had higher correlations with chlorophyll content compared to TGI. However, with broad bands, correlations between TGI and chlorophyll content were equal or higher than other indices for corn and wheat. Simulations using the Scattering by Arbitrarily Inclined Leaves canopy model indicate an interaction among TGI, leaf area index (LAI) and soil type at low crop LAI, whereas at high crop LAI, TGI was only affected by leaf chlorophyll content. TGI will enable the use of low-cost sensors, including digital cameras, for nitrogen management by remote sensing. ER - TY - JOUR ID - main2011 AU - Main, Russell AU - Cho, Moses Azong AU - Mathieu, Renaud AU - O’Kennedy, Martha M. AU - Ramoelo, Abel AU - Koch, Susan TI - An investigation into robust spectral indices for leaf chlorophyll estimation UR - http://www.sciencedirect.com/science/article/pii/S092427161100089X DO - 10.1016/j.isprsjprs.2011.08.001 T2 - ISPRS Journal of Photogrammetry and Remote Sensing PY - 2011 SN - 0924-2716 VL - 66 IS - 6 SP - 751-761 AB - Quantifying photosynthetic activity at the regional scale can provide important information to resource managers, planners and global ecosystem modelling efforts. With increasing availability of both hyperspectral and narrow band multispectral remote sensing data, new users are faced with a plethora of options when choosing an optical index to relate to their chosen or canopy parameter. The literature base regarding optical indices (particularly chlorophyll indices) is wide ranging and extensive, however it is without much consensus regarding robust indices. The wider spectral community could benefit from studies that apply a variety of published indices to differing sets of species data. The consistency and robustness of 73 published chlorophyll spectral indices have been assessed, using leaf level hyperspectral data collected from three crop species and a variety of savanna tree species. Linear regression between total leaf chlorophyll content and bootstrapping were used to determine the leafpredictive capabilities of the various indices. The indices were then ranked based on the prediction error (the average root mean square error (RMSE)) derived from the bootstrapping process involving 1000 iterative resampling with replacement. The results show two red-edge derivative based indices (red-edge position via linear extrapolation index and the modified red-edge inflection point index) as the most consistent and robust, and that the majority of the top performing indices (in spite of species variability) were simple ratio or normalised difference indices that are based on off-chlorophyll absorption centre wavebands (690–730 nm). KW - Leaf level reflectance KW - Leaf chlorophyll KW - Red-edge KW - Vegetation indices KW - Photosynthetic activity ER - TY - JOUR ID - wu2009 AU - Wu, Chaoyang AU - Niu, Zheng AU - Tang, Quan AU - Huang, Wenjiang AU - Rivard, Benoit AU - Feng, Jilu TI - Remote estimation of gross primary production in wheat using chlorophyll-related vegetation indices UR - http://www.sciencedirect.com/science/article/pii/S0168192308003560 DO - 10.1016/j.agrformet.2008.12.007 T2 - Agricultural and Forest Meteorology PY - 2009 SN - 0168-1923 VL - 149 IS - 6–7 SP - 1015-1021 AB - A number of recent studies have focused on estimating gross primary production (GPP) using vegetation indices (VIs). In this paper, GPP is retrieved as a product of incident light use efficiency (LUE), defined as GPP/PAR, and the photosynthetically active radiation (PAR). As a good correlation is found between canopy chlorophyll content and incident LUE for six types of wheat canopy (R2 = 0.87, n = 24), indices aimed for chlorophyll assessment can be used as an indicator of incident LUE and the product of chlorophyll indices and PAR will be a proxy of GPP. In a field experiment, we investigated four canopy chlorophyll content related indices (Red edge Normalized Difference Vegetation Index [Red Edge NDVI], modified Chlorophyll Absorption Ratio Index [MCARI710], Red Edge Chlorophyll Index [CIred edge] and the MERIS Terrestrial Chlorophyll Index [MTCI]) for GPP estimation during the growth cycle of wheat. These indices are validated for leaf and canopy chlorophyll estimation with ground truth data of canopy chlorophyll content. With ground truth data, a strong correlation is observed for canopy chlorophyll estimation with correlation coefficients R2 of 0.79, 0.84, 0.85 and 0.87 for Red Edge NDVI, MCARI710, CIred edge and MTCI, respectively (n = 24). As evidence of the existence of a relationship between canopy chlorophyll and GPP/PAR, these indices are shown to be a good proxy of GPP/PAR with R2 ranging from 0.70 for Red Edge NDVI and 0.75 for MTCI (n = 240). Remote estimation of GPP from canopy chlorophyll content Ã— PAR is proved to be relatively successful (R2 of 0.47, 0.53, 0.65 and 0.66 for Red edge NDVI, MCARI710, CIred edge and MTCI respectively, n = 240). These results open up a new possibility to estimate GPP and should inspire new models for remote sensing of GPP. KW - GPP KW - LUE KW - Vegetation indices KW - Canopy chlorophyll content KW - Sensitivity KW - Validation ER - TY - ELEC ID - idb AU - Henrich, V. AU - Krauss, G. AU - Götze, C. AU - Sandow, C. TI - The IndexDatabase UR - https://www.indexdatabase.de/ CY - Bonn PY - 2011 DA - 2011 ER -