TY - CPAPER ID - barnes2000 AU - Barnes, E.M. AU - Clarke, T.R. AU - Richards, S.E. AU - Colaizzi, P.D. AU - Haberland, J. AU - Kostrzewski, M. AU - Waller, P. AU - Choi, C., Riley, E. AU - Thompson, T. AU - Lascano, R.J. AU - Li, H. AU - Moran, M.S. TI - Coincident detection of crop water stress, nitrogen status and canopy density using ground based multispectral data T2 - Proc. 5th Int. Conf. Precis Agric PY - 2000 ER - TY - CPAPER ID - clarke2001 AU - Clarke, T. R. AU - Moran, M. S. AU - Barnes, E. M. AU - Pinter, P. J., Jr. AU - Qi, J. TI - Planar domain indices: a method for measuring a quality of a single component in two-component pixels DO - 10.1109/igarss.2001.976818 T2 - Geoscience and Remote Sensing Symposium, 2001. IGARSS '01. IEEE 2001 International PY - 2001 VL - 3 SP - 1279-1281 AB - A method is presented that reduces the difficulty of measuring a particular quality of one component in a multi-component element. The planar domain index design requires two measurements: that of a signal sensitive to the desired quality of the target component and another signal sensitive to the component's weight or relative proportion to the whole. The quality signal and component weight signal form the two dimensions of a plane, and the maximum and minimum possible values for each signal define the boundaries of a domain within this plane. The position of a coordinate pair within the domain can then be correlated to the quality being measured, independent of the component's proportion to the whole. Examples given involve mixed vegetation and soil targets, with vegetation indices used to measure the component weight. Quality signals of the example applications include canopy minus air temperature as a measure of evapotranspiration, a normalized difference of near infrared and far red wavelengths as a measure of chlorophyll content, and differential synthetic aperture radar (SAR) for measuring near-surface soil moisture KW - agriculture, geophysical signal processing, geophysical techniques, image processing, terrain mapping, vegetation mapping, canopy, geophysical measurement technique, land surface, planar domain index, planar domain indices, quality, remote sensing, single ER - TY - JOUR ID - el-shikha2008 AU - El-Shikha, D. M. AU - Barnes, E. M. AU - Clarke, T. R. AU - Hunsaker, D. J. AU - Haberland, J. A. AU - Pinter, P. J. AU - Waller, P. M. AU - Thompson, T. L. TI - Remote sensing of cotton nitrogen status using the Canopy Chlorophyll Content Index (CCCI) UR - ://WOS:000255421800007 T2 - Transactions of the Asabe PY - 2008 DA - Jan-Feb SN - 0001-2351 VL - 51 IS - 1 SP - 73-82 N1 - ISI Document Delivery No.: 294QY Times Cited: 6 Cited Reference Count: 37 El-Shikha, D. M. Barnes, E. M. Clarke, T. R. Hunsaker, D. J. Haberland, J. A. Pinter, P. J., Jr. Waller, P. M. Thompson, T. L. Amer soc agricultural & biological engineers St joseph AB - Various remote sensing indices have been used to infer crop nitrogen (N) status for field-scale nutrient management. However, such indices may indicate erroneous N status if there is a decrease in crop canopy density influenced by other factors, such as water stress. The Canopy Chlorophyll Content Index (CCCI) is a two-dimensional remote sensing index that has been proposed for inferring cotton N status. The CCCI uses reflectances in the near-infrared (NIR) and red spectral regions to account for seasonal changes in canopy density, while reflectances in the NIR and far-red regions are used to detect relative changes in canopy chlorophyll, a surrogate for N content. The primary objective of this study was to evaluate the CCCI and several other remote sensing indices for detecting the N status for cotton during the growing season. A secondary objective was to evaluate the ability of the indices to appropriately detect N in the presence of variable water status. Remote sensing data were collected during the 1998 (day of year [DOY] 114 to 310) and 1999 (DOY 106 to 316) cotton seasons in Arizona, in which treatments of optimal and low levels of N and water were imposed. In the 1998 season, water treatments were not imposed until late in the season (DOY 261), well after full cover Following an early season N application in 1998 for the optimal (DOY 154) but not the low N treatment, the CCCI detected significant differences in crop N status between the N treatments starting on DOY 173, when canopy cover was about 30%. A common vegetation index, the ratio of NIR to red (RVI), also detected significant separation between N treatments, but RVI detection occurred 16 days after the CCCI response. After an equal amount of N was applied to both optimal and low N treatments on DOY 190 in 1998, the CCCI indicated comparable N status for the N treatments on DOY 198, a trend not detected by RVI. In the 1999 season, both N and water treatments were imposed early and frequently during the season. The N status was poorly described by both the CCCI and RVI under partial canopy conditions when water status differed among treatments. However, once full canopy was obtained in 1999, the CCCI provided reliable N status information regardless of water status. At full cotton cover, the CCCI was significantly correlated with measured parameters of N status, including petiole NO3-N (r = 0.74), SPAD chlorophyll (r = 0.65), and total leaf N contents (r = 0.86). For well-watered cotton, the CCCI shows promise as a useful indicator of cotton N status after the canopy reaches about 30% cover However, further study is needed to develop the CCCI as a robust N detection tool independent of water stress. KW - canopy reflectance KW - fertility detection KW - radiometers KW - spectral analysis KW - water stress KW - reflectance indexes KW - spectral radiance KW - winter-wheat KW - corn leaves KW - grain-yield KW - water KW - stress KW - plants KW - light KW - field ER - TY - JOUR ID - herrmann2010 AU - Herrmann, I. AU - Karnieli, A. AU - Bonfil, D. J. AU - Cohen, Y. AU - Alchanatis, V. TI - SWIR-based spectral indices for assessing nitrogen content in potato fields UR - http://dx.doi.org/10.1080/01431160903283892 DO - 10.1080/01431160903283892 PR - Taylor & Francis T2 - International Journal of Remote Sensing PY - 2010 DA - 2010/10/01 SN - 0143-1161 VL - 31 IS - 19 SP - 5127-5143 AB - Nitrogen (N) is an essential element in plant growth and productivity, and N fertilizer is therefore of prime importance in cultivated crops. The amount and timing of N application has economic and environmental implications and is consequently considered to be an important issue in precision agriculture. Spectral indices derived from handheld, airborne and spaceborne spectrometers are used for assessing N content. The majority of these indices are based on indirect indicators, mostly chlorophyll content, which is proven to be physiologically linked to N content. The current research aimed to explore the performance of new N spectral indices dependent upon the shortwave infrared (SWIR) region (1200?2500 nm), and particularly the 1510 nm band because it is related directly to N content. Traditional nitrogen indices (NIs) and four proposed new SWIR-based indices were tested with canopy-level spectral data obtained during two growing seasons in potato experimental plots in the northwest Negev, Israel. Above-ground biomass samples were collected at the same location of the spectral sampling to provide in-situ N content data. The performance of all indices was evaluated by three methods: (1) correlations between the existing and proposed indices and N as well as correlations among the indices themselves; (2) the root mean square error prediction (RMSEP) of the N content; and (3) the indices relative sensitivity (S r) to the N content. The results reveal a firm advantage for the proposed SWIR-based indices in their ability to predict, and in their sensitivity to, N content. The best index is one that combines information from the 1510 and 660 nm bands but no significant differences were found among the new SWIR-based indices. ER - TY - JOUR ID - pinter2003 AU - Pinter, P. J. AU - Hatfield, J. L. AU - Schepers, J. S. AU - Barnes, E. M. AU - Moran, M. S. AU - Daughtry, C. S. T. AU - Upchurch, D. R. TI - Remote sensing for crop management UR - ://WOS:000221193400006 T2 - Photogrammetric Engineering and Remote Sensing PY - 2003 DA - Jun SN - 0099-1112 VL - 69 IS - 6 SP - 647-664 N1 - ISI Document Delivery No.: 817RA Times Cited: 88 Cited Reference Count: 256 Pinter, PJ Hatfield, JL Schepers, JS Barnes, EM Moran, MS Daughtry, CST Upchurch, DR Amer soc photogrammetry Bethesda AB - Scientists with the Agricultural Research Service (ARS) and various government agencies and private institutions have provided a great deal of fundamental information relating spectral reflectance and thermal emittance properties of soils and crops to their agronomic and biophysical characteristics. This knowledge has facilitated the development and use of various remote sensing methods for non-destructive monitoring of plant growth and development and for the detection of many environmental stresses which limit plant productivity. Coupled with rapid advances in computing and position-locating technologies, remote sensing from ground-, air-, and space-based platforms is now capable of providing detailed spatial and temporal information on plant response to their local environment that is needed for site specific agricultural management approaches. This manuscript, which emphasizes contributions by ARS researchers, reviews the biophysical basis of remote sensing; examines approaches that have been developed, refined, and tested for management of water, nutrients, and pests in agricultural crops; and assesses the role of remote sensing in yield prediction. It concludes with a discussion of challenges facing remote sensing in the future. KW - water-stress index KW - spectral-biophysical data KW - soil heat-flux KW - infrared KW - aerial photography KW - laser-induced fluorescence KW - constant leaf KW - temperature KW - adjusted vegetation index KW - surface-energy balance KW - clover-seed production KW - gossypium-hirsutum l 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 -