TY - JOUR ID - cloutis1996 AU - Cloutis, E. A. AU - Connery, D. R. AU - Major, D. J. AU - Dover, F. J. TI - Airborne multi-spectral monitoring of agricultural crop status: effect of time of year, crop type and crop condition parameter UR - http://www.tandfonline.com/doi/abs/10.1080/01431169608949094 DO - 10.1080/01431169608949094 T2 - International Journal of Remote Sensing PY - 1996 DA - 1996/09/01 SN - 0143-1161 VL - 17 IS - 13 SP - 2579-2601 ER - TY - JOUR ID - eyers2004 AU - Eyers, R. D. AU - Mills, J. P. TI - Subsidence Detection Using Integrated Multi Temporal Airborne Imagery T2 - INTERNATIONAL ARCHIVES OF PHOTOGRAMMETRY REMOTE SENSING AND SPATIAL INFORMATION SCIENCES PY - 2004 SN - 1682-1750 VL - 35 IS - 7 SP - 714-719 ER - TY - CONF ID - eyers2004-2 AU - Eyers, R. D. AU - Mills, J. P. AU - Cutler, M. E. J. TI - The topographic and spectral expression of mining subsidence T2 - Annual Conference of the Remote Sensing and Photogrammetry Society RSPSoc2004) CY - Aberdeen PY - 2004 DA - 7-10 September 2004 ER - TY - JOUR ID - gitelson2002 AU - Gitelson, A. A. AU - Kaufman, Y. J. AU - Stark, R. AU - Rundquist, D. TI - Novel algorithms for remote estimation of vegetation fraction UR - http://www.ingentaconnect.com/content/els/00344257/2002/00000080/00000001/art00289 http://dx.doi.org/10.1016/S0034-4257(01)00289-9 DO - 10.1016/s0034-4257(01)00289-9 T2 - Remote Sensing of Environment PY - 2002 VL - 80 IS - 1 SP - 76-87 AB -

Spectral properties of a wheat canopy with vegetation fraction (VF) from 0% to 100% in visible and near-infrared (NIR) ranges of the spectrum were studied in order to devise a technique for remote estimation of VF. When VF was <60%, from emergence till middle of the elongation stage, four distinct, and quite independent, spectral bands of reflectance existed in the visible range of the spectrum: 400 to 500 nm, 530 to 600 nm, near 670 nm, and around 700 nm. When VF was between 60% and 100%, reflectance in the NIR leveled off or even decreases with an increase of VF. The decreased reflectance in the NIR, occurring at or near the midseason, can be a limiting factor in the use of that spectral region for VF estimation. It was found that for VF>60%, the information content of reflectance spectra in visible range can be expressed by only two independent pairs of spectral bands: (1) the blue from 400 to 500 nm and the red near 670 nm; (2) the green around 550 nm and the red edge region near 700 nm. We propose using only the visible range of the spectrum to quantitatively estimate VF. The green (as well as a 700-nm band) and the red (near 670 nm) reflectances were used in developing new indices, which were linearly proportional to wheat VF ranging from 0% to 100%. The Atmospherically Resistant Vegetation Index (ARVI) concept was used to correct indices for atmospheric effects. Visible Atmospherically Resistant Index in the form VARI=(Rgreen-Rred)/(Rgreen+Rred-Rblue) was found to be minimally sensitive to atmospheric effects allowing estimation of VF with an error of <10% in a wide range of atmospheric optical thickness. Validation of the newly suggested technique was carried out using wheat independent data sets and reflectance data obtained for cornfields in Nebraska. Predicted green VF was compared with retrieved from digital images. Despite the fact that the reflectance contrast among the visible channels is much smaller than between the visible and NIR, the sensitivity of suggested indices to moderate to high values of VF is much higher than for the Normalized Difference Vegetation Index (NDVI), and the error in VF prediction did not exceed 10%. Suggested indices will complement the widely used NDVI, ARVI, Soil Adjusted Vegetation Index (SAVI) and others, which are based on the red and the NIR bands in VF estimation, and also Green Atmospherically Resistant Index (GARI), which is based on the green and the NIR bands.

ER - TY - JOUR ID - goel2003 AU - Goel, P. K. AU - Prasher, S. O. AU - Landry, J. A. AU - Patel, R. M. AU - Viau, A. A. TI - Hyperspectral image classification to detect weed infestations and nitrogen status in corn UR - ://WOS:000182949700039 T2 - Transactions of the Asae PY - 2003 DA - Mar-Apr SN - 0001-2351 VL - 46 IS - 2 SP - 539-550 N1 - ISI Document Delivery No.: 679YN Times Cited: 9 Cited Reference Count: 47 Goel, PK Prasher, SO Landry, JA Patel, RM Viau, AA Amer soc agricultural engineers St joseph AB - The potential of hyperspectral aerial imagery for the detection of weed infestation and nitrogen fertilization level in a corn (Zea mays L.) crop was evaluated. A Compact Airborne Spectrographic Imager (CASI) was used to acquire hyperspectral data over a field experiment laid out at the Lods Agronomy Research Centre of Macdonald Campus, McGill University, Quebec, Canada. Corn was grown under four weed management strategies (no weed control, control of grasses, control of broadleaf weeds, and full weed control) factorally combined with nitrogen fertilization rates of 60, 120, and 250 N kg/ha. The aerial image was acquired at the tasseling stage, which was 66 days after planting. For the classification of remote sensing imagery, various widely used supervised classification algorithms (maximum likelihood, minimum distance, Mahalanobis distance, parallelepiped, and binary coding) and more sophisticated classification approaches (spectral angle mapper and linear spectral unmixing) were investigated. It was difficult to distinguish the combined effect of both weed and nitrogen treatments simultaneously. However, higher classification accuracies were obtained when only one factor, either weed or nitrogen treatment, was considered. With different classifiers, depending on the factors considered for the classification, accuracies ranged from 65.84% to 99.46%. No single classifier was found useful for all the conditions. KW - corn KW - hyperspectral KW - image classification KW - nitrogen KW - remote sensing KW - weeds KW - multispectral digital imagery KW - no-till corn KW - light reflectance KW - glycine-max KW - remote KW - yield KW - management KW - variability KW - growth KW - field ER - TY - JOUR ID - haboudane2004 AU - Haboudane, Driss AU - Miller, John R. AU - Pattey, Elizabeth AU - Zarco-Tejada, Pablo J. AU - Strachan, Ian B. TI - Hyperspectral vegetation indices and novel algorithms for predicting green LAI of crop canopies: Modeling and validation in the context of precision agriculture UR - http://www.sciencedirect.com/science/article/pii/S0034425704000264 DO - 10.1016/j.rse.2003.12.013 T2 - Remote Sensing of Environment PY - 2004 SN - 0034-4257 VL - 90 IS - 3 SP - 337-352 AB - A growing number of studies have focused on evaluating spectral indices in terms of their sensitivity to vegetation biophysical parameters, as well as to external factors affecting canopy reflectance. In this context, leaf and canopy radiative transfer models are valuable for modeling and understanding the behavior of such indices. In the present work, PROSPECT and SAILH models have been used to simulate a wide range of crop canopy reflectances in an attempt to study the sensitivity of a set of vegetation indices to green leaf area index (LAI), and to modify some of them in order to enhance their responsivity to LAI variations. The aim of the paper was to present a method for minimizing the effect of leaf chlorophyll content on the prediction of green LAI, and to develop new algorithms that adequately predict the LAI of crop canopies. Analyses based on both simulated and real hyperspectral data were carried out to compare performances of existing vegetation indices (Normalized Difference Vegetation Index [NDVI], Renormalized Difference Vegetation Index [RDVI], Modified Simple Ratio [MSR], Soil-Adjusted Vegetation Index [SAVI], Soil and Atmospherically Resistant Vegetation Index [SARVI], MSAVI, Triangular Vegetation Index [TVI], and Modified Chlorophyll Absorption Ratio Index [MCARI]) and to design new ones (MTVI1, MCARI1, MTVI2, and MCARI2) that are both less sensitive to chlorophyll content variations and linearly related to green LAI. Thorough analyses showed that the above existing vegetation indices were either sensitive to chlorophyll concentration changes or affected by saturation at high LAI levels. Conversely, two of the spectral indices developed as a part of this study, a modified triangular vegetation index (MTVI2) and a modified chlorophyll absorption ratio index (MCARI2), proved to be the best predictors of green LAI. Related predictive algorithms were tested on CASI (Compact Airborne Spectrographic Imager) hyperspectral images and, then, validated using ground truth measurements. The latter were collected simultaneously with image acquisition for different crop types (soybean, corn, and wheat), at different growth stages, and under various fertilization treatments. Prediction power analysis of proposed algorithms based on MCARI2 and MTVI2 resulted in agreements between modeled and ground measurement of non-destructive LAI, with coefficients of determination (r2) being 0.98 for soybean, 0.89 for corn, and 0.74 for wheat. The corresponding RMSE for LAI were estimated at 0.28, 0.46, and 0.85, respectively. KW - Hyperspectral KW - Spectral indices KW - Green LAI KW - Prediction algorithms KW - Chlorophyll content KW - Precision agriculture ER - TY - JOUR ID - haboudane2002 AU - Haboudane, Driss AU - Miller, John R. AU - Tremblay, Nicolas AU - Zarco-Tejada, Pablo J. AU - Dextraze, Louise TI - Integrated narrow-band vegetation indices for prediction of crop chlorophyll content for application to precision agriculture UR - http://www.sciencedirect.com/science/article/pii/S0034425702000184 DO - 10.1016/s0034-4257(02)00018-4 T2 - Remote Sensing of Environment PY - 2002 SN - 0034-4257 VL - 81 IS - 2–3 SP - 416-426 AB - Recent studies have demonstrated the usefulness of optical indices from hyperspectral remote sensing in the assessment of vegetation biophysical variables both in forestry and agriculture. Those indices are, however, the combined response to variations of several vegetation and environmental properties, such as Leaf Area Index (LAI), leaf chlorophyll content, canopy shadows, and background soil reflectance. Of particular significance to precision agriculture is chlorophyll content, an indicator of photosynthesis activity, which is related to the nitrogen concentration in green vegetation and serves as a measure of the crop response to nitrogen application. This paper presents a combined modeling and indices-based approach to predicting the crop chlorophyll content from remote sensing data while minimizing LAI (vegetation parameter) influence and underlying soil (background) effects. This combined method has been developed first using simulated data and followed by evaluation in terms of quantitative predictive capability using real hyperspectral airborne data. Simulations consisted of leaf and canopy reflectance modeling with PROSPECT and SAILH radiative transfer models. In this modeling study, we developed an index that integrates advantages of indices minimizing soil background effects and indices that are sensitive to chlorophyll concentration. Simulated data have shown that the proposed index Transformed Chlorophyll Absorption in Reflectance Index/Optimized Soil-Adjusted Vegetation Index (TCARI/OSAVI) is both very sensitive to chlorophyll content variations and very resistant to the variations of LAI and solar zenith angle. It was therefore possible to generate a predictive equation to estimate leaf chlorophyll content from the combined optical index derived from above-canopy reflectance. This relationship was evaluated by application to hyperspectral CASI imagery collected over corn crops in three experimental farms from Ontario and Quebec, Canada. The results presented here are from the L'Acadie, Quebec, Agriculture and Agri-Food Canada research site. Images of predicted leaf chlorophyll content were generated. Evaluation showed chlorophyll variability over crop plots with various levels of nitrogen, and revealed an excellent agreement with ground truth, with a correlation of r2=.81 between estimated and field measured chlorophyll content data. ER - TY - JOUR ID - jago1999 AU - Jago, Rosemary A. AU - Cutler, Mark E. J. AU - Curran, Paul J. TI - Estimating Canopy Chlorophyll Concentration from Field and Airborne Spectra UR - http://www.sciencedirect.com/science/article/pii/S0034425798001138 DO - 10.1016/s0034-4257(98)00113-8 T2 - Remote Sensing of Environment PY - 1999 SN - 0034-4257 VL - 68 IS - 3 SP - 217-224 AB - This article investigates the effects of both soil contamination and nitrogen application on the red edge–chlorophyll concentration relationship for a vegetation canopy. Field based canopy reflectance and chlorophyll concentration data were collected at a grassland field site affected by soil contamination and a winter wheat field site affected by different levels of nitrogen fertilisation. The correlation between red edge position (REP) and canopy chlorophyll concentration was r=0.84 and 0.80 for the grassland and winter wheat field sites, respectively. Airborne imaging spectrometry was used to generate REP images (units, nm) of the grassland and winter wheat field sites. Strong correlations were observed between REP and canopy chlorophyll concentration at both field sites. Predictive regression equations were developed to map canopy chlorophyll concentration across the field sites. The rms error of estimated chlorophyll concentration was 0.42 mg g-1 (±12.69% of mean) and 2.09 mg g-1(±16.4% of mean) at the grassland and winter wheat field sites respectively. Results demonstrated the use of remotely sensed estimates of the REP from both field and airborne spectrometers for estimating chlorophyll concentration and indicated the potential of this technique for inferring both land contamination and grain yield. ER - TY - JOUR ID - malthus1993 AU - Malthus, Tim J. AU - Andrieu, Bruno AU - Danson, F. Mark AU - Jaggard, Keith W. AU - Steven, Michael D. TI - Candidate high spectral resolution infrared indices for crop cover UR - http://www.sciencedirect.com/science/article/pii/003442579390095F DO - 10.1016/0034-4257(93)90095-f T2 - Remote Sensing of Environment PY - 1993 SN - 0034-4257 VL - 46 IS - 2 SP - 204-212 ER - TY - JOUR ID - moran1997 AU - Moran, M. S. AU - Inoue, Y. AU - Barnes, E. M. TI - Opportunities and limitations for image-based remote sensing in precision crop management UR - http://www.sciencedirect.com/science/article/pii/S003442579700045X DO - 10.1016/s0034-4257(97)00045-x T2 - Remote Sensing of Environment PY - 1997 SN - 0034-4257 VL - 61 IS - 3 SP - 319-346 AB - This review addresses the potential of image-based remote sensing to provide spatially and temporally distributed information for precision crop management (PCM). PCM is an agricultural management system designed to target crop and soil inputs according to within, field requirements to optimize profitability and protect the environment. Progress in. PCM has been hampered by a lack of timely, distributed information on crop and soil conditions. Based on a review of the information requirements of PCM, eight areas were identified in which image-based remote sensing technology could provide information that is currently lacking or inadequate. Recommendations were made for applications with potential for near-term implementation with available remote sensing technology and instrumentation. We found that both aircraft- and satellite-based re-trote sensing could provide valuable information for PCM applications. Images from aircraft-based sensors have a unique role for monitoring seasonally variable crop/soil conditions and for time specific and time-critical crop management; current satellitebased sensors have limited, but important, applications; and upcoming commercial Earth observation satellites may provide the resolution, timeliness, and high quality required for many PCM operations. The current limitations for image-based remote sensing applications are mainly due to sensor attributes, such as restricted spectral range, coarse spatial resolution, slow turnaround time, and inadequate repeat coverage. According to experts in PCM, the potential market for remote sensing products in PCM is good. Future work should be focused on assimilating remotely sensed infonna- tion into existing decision support systems (DSS), and conducting economic and technical analysis of remote sensing applications with season-long pilot projects. ER - TY - JOUR ID - pe_uelas1998 AU - Peñuelas, J. AU - Filella, I. TI - Visible and near-infrared reflectance techniques for diagnosing plant physiological status UR - http://dx.doi.org/10.1016/S1360-1385(98)01213-8 DO - 10.1016/S1360-1385(98)01213-8 T2 - Trends in Plant Science PY - 1998 SN - 13601385 VL - 3 IS - 4 SP - 151-156 ER - TY - JOUR ID - schlerf2005 AU - Schlerf, M. AU - Atzberger, C. AU - Hill, J. TI - Remote sensing of forest biophysical variables using HyMap imaging spectrometer data T2 - Remote Sensing of Environment PY - 2005 VL - 95 SP - 177−194 ER - TY - JOUR ID - thenkabail2002 AU - Thenkabail, P. S. AU - Smith, R. B. AU - De Pauw, E. TI - Evaluation of narrowband and broadband vegetation indices for determining optimal hyperspectral wavebands for agricultural crop characterization T2 - Photogrammetric Engineering And Remote Sensing PY - 2002 VL - 68 IS - 6 SP - 607-621 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 - JOUR ID - zarco-tejada_p_j_sepulcre-cant_2007 AU - Zarco-Tejada P.J. AU - Sepulcre-Cantó, G. TI - REMOTE SENSING OF VEGETATION BIOPHYSICAL PARAMETERS FOR DETECTING STRESS CONDITION AND LAND COVER CHANGES T2 - Estudios de la Zona No Saturada del Suelo PY - 2007 VL - 8 ER - TY - JOUR ID - zarco-tejada2001 AU - Zarco-Tejada, P. J. AU - Miller, J. R. AU - Noland, T. L. AU - Mohammed, G. H. AU - Sampson, P. H. TI - Scaling-up and model inversion methods with narrow-band optical indices for chlorophyll content estimation in closed forest canopies with hyperspectral data T2 - IEEE Transactions on Geoscience and Remote Sensing PY - 2001 VL - 39 SP - 1491−1507 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 -