TY - JOUR ID - gitelson1996 AU - Gitelson, Anatoly A. AU - Kaufman, Yoram J. AU - Merzlyak, Mark N. TI - Use of a green channel in remote sensing of global vegetation from EOS-MODIS DO - 10.1016/s0034-4257(96)00072-7 T2 - Remote Sensing of Environment PY - 1996 SN - 0034-4257 VL - 58 IS - 3 SP - 289-298 AB - Most animals use a “green” spectral range to remotely sense the presence and vitality of vegetation. While humans possess the same ability in their eyes, man-made space-borne sensors that sense evolution of global vegetation, have so far used a combination of the red and near infrared channels instead. In this article we challenge this approach, using measurements of reflectance spectra from 400 nm to 750 nm with spectral resolution of 2 nm, with simultaneous determination of pigment concentrations of mature and autumn senescing leaves. We show that, for a wide range of leaf greenness, the maximum sensitivity of reflectance coincides with the red absorption maximum of chlorophyll-a (Chl-a) at 670 nm. However, for yellow-green to green leaves (with Chl-a more than 3–5 μg/cm2), the reflectance near 670 nm is not sensitive to chlorophyll concentration because of saturation of the relationship of absorptions versus chlorophyll concentration. Maximum sensitivity of Chl-a concentration for a wide range of its variation (0.3–45 μg/cm2) was found, not surprisingly so, around the green band from 520 nm to 630 nm and also near 700 nm. We found that the inverse of the reflectance in the green band was proportional to Chl-a concentration with correlation r2 > 0.95. This band will be present on several future satellite sensors with a global view of vegetation (SeaWiFS to be launched in 1996, Polder on ADEOS-1 also in 1996, and MODIS on EOS in 1998 and 2000). New indexes that use the green channel and are resistant to atmospheric effects are developed. A green NDVI = (ϱnir − ϱgreen(ϱnir + ϱgreen) was tested for a range of Chl-a from 0.3 μg/cm2 to 45 μg/cm2, and found to have an error in the chlorophyll a derivation at leaf level of less than 3 μg/cm2. The new index has wider dynamic range than the NDVI and is, on average, at least five times more sensitive to Chl-a concentration. A green atmospherically resistant vegetation index (GARI), tailored on the concept of ARVI (Kaufman and Tanré, 1992), is developed and is expected to be as resistant to atmospheric effects as ARVI but more sensitive to a wide range of Chl-a concentrations. While NDVI and ARVI are sensitive to vegetation fraction and to rate of absorption of photosynthetic solar radiation, a green vegetation index like GARI should be added to sense the concentration of chlorophyll, to measure the rate of photosynthesis and to monitor plant stress. ER - TY - JOUR ID - le_maire2004 AU - le Maire, G. AU - Francois, C. AU - Dufrene, E. TI - Towards universal broad leaf chlorophyll indices using PROSPECT simulated database and hyperspectral reflectance measurements DO - 10.1016/j.rse.2003.09.004 T2 - Remote Sensing of Environment PY - 2004 SN - 0034-4257 VL - 89 IS - 1 SP - 1-28 AB - Fifty-three leaves were randomly sampled on different deciduous tree species, representing a wide range of chlorophyll contents, tree ages, and leaf structural features. Their reflectance was measured between 400 and 800 nm with a 1-nm,step, and their chlorophyll content determined by extraction. A larger simulated database (11,583 spectra) was built using the PROSPECT model, in order to test, calibrate, and obtain universal indices, i.e., indices applicable to a wide range of species and leaf structure. To our knowledge, almost all leaf chlorophyll indices published in the literature since 1973 have been tested on both databases. Fourteen canonical types of indices (published ones and new ones) were identified, and their wavelengths calibrated on the simulated database as well as on the experimental database to determine the best wavelengths and, hence, the best performances in chlorophyll estimation for each index types. These indices go from simple reflectance ratios to more sophisticated indices Using reflectance first derivatives (using the Savitzky and Golay method). We also tested other nondestructive methods to obtain total chlorophyll concentration: SPAD (Minolta Camera, Osaka, Japan) and neural networks. The validity of the actual PROSPECT model is challenged by our results: Important discordances are found when the indices are calculated with PROSPECT compared to experimental data, especially for some indices and wavelengths. The discordance is even greater when the indices are determined with PROSPECT and applied on the experimental database. A new calibration of PROSPECT is therefore necessary for any study aiming at using simulated spectra to determine or to calibrate indices. The "peak jump" and the multiple-peak feature observed on the first derivative of the reflectances (e.g., in the Red-Edge Inflection Point [REIP] index) has been investigated. It was shown that chlorophyll absorption alone can explain this feature. The peak jump disqualifies' the REIP to be a valuable chlorophyll index. A simple modified difference ratio gave the best results among all published indices (cross-validated RMSE = 2.1 mug/cm(2) on the experimental database). After calibration on the experimental database, modified Simple Ratio (mSR) and modified Normalized Difference (mND) indices gave the best 2 performances (RMSECV = 1.8 mug/cm(2) on the experimental database). The new Double Difference (DD) index, although not the best on the 2), 2 experimental database (RMSECV 2.9 mug/cm(2)), has the best results on the larger simulated database (RMSE = 3.7 mug/cm(2)) and is expected to give good results on larger experimental databases. The best reflectance-based indices give better performances than the current commercial nondestructive device SPAD (RMSECV = 4.5 mug/cm(2)). In This leaf-level study, the best indices are very near from each other, so that complex methods are useless: REIP-like, neural networks, and derivative-based indices are not necessary and give worst results than simpler properly chosen indices. These conclusions will certainly be different for. a canopy-level study, where the derivative-based indices may perform significantly better than the other ones. (C) 2003 Elsevier Inc. All rights reserved. KW - universal broad leaf chlorophyll indices. PROSPECT. hyperspectral. reflectance measurements. neural-network classification. radiative-transfer models. remote-sensing. data. red-edge. vegetation indexes. spectral reflectance. optical-properties. bidirectio ER - TY - JOUR ID - lymburner2000 AU - Lymburner, L. AU - Beggs, PJ. AU - Jacobson, CR. TI - Estimation of canopy-average surface-specific leaf area using Landsat TM data T2 - Photogrammetric Engineering and Remote Sensing PY - 2000 SN - 0099-1112 VL - 66 IS - 2 SP - 183-191 AB - Specific leaf area (SLA) is an important ecological variable because of its links with plant ecophysiology and leaf biochemistry. Variations in SLA are associated with variations in leaf optical properties, and these changes in leaf optical properties have been found to result in changes in canopy reflectance. This paper utilizes these changes to explore the potential of estimating SLA using Landsat TM data. Fourteen sites with varying vegetation were sampled on the Lambert Peninsula in Ku-ring-gai Chase National Park to the north of Sydney, Australia. A sampling strategy that facilitated the calculation of canopy-average surface SLA (SLA(CS)) was developed. The relationship between SLA(CS), reflectance in Landsat TM bands, and a number of vegetation indices, were explored using univariate regression. The observed relationships between SLA(CS) and canopy reflectance are also discussed in terms of trends observed in a pre-existing leaf optical properties dataset (LOPEX 93). Field data indicate that there is a strong correlation between SLA(CS) and red, near-infrared, and the second midinfrared bands of Landsat TM data. A strong correlation between SLA(CS) and the following vegetation indices: Soil and Atmosphere Resistant Vegetation Index (SARVI2), Normalized Difference Vegetation Index (NDVI), and Ratio Vegetation Index (RVI), suggests that these vegetation indices could be used to estimate SLA(CS) using Landsat TM data. ER - TY - JOUR ID - wang2007 AU - Wang, Fu-min AU - Huang, Jing-feng AU - Tang, Yan-lin AU - Wang, Xiu-zhen TI - New Vegetation Index and Its Application in Estimating Leaf Area Index of Rice UR - http://www.sciencedirect.com/science/article/pii/S1672630807600274 DO - 10.1016/s1672-6308(07)60027-4 T2 - Rice Science PY - 2007 SN - 1672-6308 VL - 14 IS - 3 SP - 195-203 AB - Leaf area index (LAI) is an important characteristic of land surface vegetation system, and is also a key parameter for the models of global water balancing and carbon circulation. By using the reflectance values of Landsat-5 blue, green and red channels simulated from rice reflectance spectrum, the sensitivities of the bands to LAI were analyzed, and the response and capability to estimate LAI of various NDVIs (normalized difference vegetation indices), which were established by substituting the red band of general NDVI with all possible combinations of red, green and blue bands, were assessed. Finally, the conclusion was tested by rice data at different conditions. The sensitivities of red, green and blue bands to LAI were different under various conditions. When LAI was less than 3, red and blue bands were more sensitive to LAI. Though green band in the circumstances was less sensitive to LAI than red and blue bands, it was sensitive to LAI in a wider range. When the vegetation indices were constituted by all kinds of combinations of red, green and blue bands, the premise for making the sensitivity of these vegetation indices to LAI be meaningful was that the value of one of the combinations was greater than 0.024, i.e. visible reflectance (VIS)>0.024. Otherwise, the vegetation indices would be saturated, resulting in lower estimation accuracy of LAI. Comparison on the capabilities of the vegetation indices derived from all kinds of combinations of red, green and blue bands to LAI estimation showed that GNDVI (Green NDVI) and GBNDVI (Green-Blue NDVI) had the best relations with LAI. The capabilities of GNDVI and GBNDVI to LAI estimation were tested under different circumstances, and the same result was acquired. It suggested that GNDVI and GBNDVI performed better to predict LAI than the conventional NDVI. KW - vegetation index KW - rice KW - leaf area index KW - reflectance spectrum KW - remote sensing ER - TY - JOUR ID - yang2004 AU - Yang, Chenghai AU - Everitt, James H. AU - Bradford, Joe M. TI - Airborne Hyperspectral Imagery and Yield Monitor Data for Mapping Cotton Yield Variability T2 - Precision Agriculture PY - 2004 VL - 5 SP - 445–461 AB - Increased availability of hyperspectral imagery necessitates the evaluation of its potential for precision agriculture applications. This study examined airborne hyperspectral imagery for mapping cotton (Gossypium hirsutum L.) yield variability as compared with yield monitor data. Hyperspectral images were acquired using an airborne imaging system from two cotton fields during the 2001 growing season, and yield data were collected from the fields using a cotton yield monitor. The raw hyperspectral images contained 128 bands between 457 and 922 nm. The raw images were geometrically corrected, georeferenced and resampled to 1 m resolution, and then converted to reflectance. Aggregation functions were then applied to each of the 128 bands to reduce the cell resolution to 4 m (close to the cotton picker’s cutting width) and 8 m. The yield data were also aggregated to the two grids. Correlation analysis showed that cotton yield was significantly related to the image data for all the bands except for a few bands in the transitional range from the red to the near-infrared region. Stepwise regression performed on the yield and hyperspectral data identified significant bands and band combinations for estimating yield variability for the two fields. Narrow band normalized difference vegetation indices derived from the significant bands provided better yield estimation than most of the individual bands. The stepwise regression models based on the significant narrow bands explained 61% and 69% of the variability in yield for the two fields, respectively. To demonstrate if narrow bands may be better for yield estimation than broad bands, the hyperspectral bands were aggregated into Landsat-7 ETM+ sensor’s bandwidths. The stepwise regression models based on the four broad bands explained only 42% and 58% of the yield variability for the two fields, respectively. These results indicate that hyperspectral imagery may be a useful data source for mapping crop yield variability. KW - cotton, hyperspectral imagery, remote sensing, yield monitor, yield mapping 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 -