Author
C.L. Walthall
Bio: C.L. Walthall is an academic researcher from Agricultural Research Service. The author has contributed to research in topics: Leaf area index & Normalized Difference Vegetation Index. The author has an hindex of 25, co-authored 42 publications receiving 5055 citations.
Papers
More filters
TL;DR: In this paper, a wide range of leaf chlorophyll levels were established in field-grown corn (Zea mays L.) with the application of 8 N levels: 0, 12.5%, 25, 50, 75, 100, 125, and 150% of the recommended rate.
1,861 citations
TL;DR: In this paper, the potential of using satellite spectral reflectance measurements to map and monitor vegetation water content (VWC) for corn and soybean canopies was evaluated, and a method developed was used to map daily VWC for the watershed over the 1-month experiment period.
746 citations
TL;DR: In this article, the current status of our understanding of how reflectance and emittance have been used to quantitatively assess agronomic parameters and some of the challenges facing future generations of scientists seeking to further advance remote sensing for agronomics applications.
Abstract: Remote sensing has provided valuable insights into agronomic management over the past 40 yr. The contributions of individuals to remote sensing methods have lead to understanding of how leaf reflectance and leaf emittance changes in response to leaf thickness, species, canopy shape, leaf age, nutrient status, and water status. Leaf chlorophyll and the preferential absorption at different wavelengths provides the basis for utilizing reflectance with either broad-band radiometers typical of current satellite platforms or hyperspectral sensors that measure reflectance at narrow wavebands. Understanding of leaf reflectance has lead to various vegetative indices for crop canopies to quantify various agronomic parameters, e.g., leaf area, crop cover, biomass, crop type, nutrient status, and yield. Emittance from crop canopies is a measure of leaf temperature and infrared thermometers have fostered crop stress indices currently used to quantify water requirements. These tools are being developed as we learn how to use the information provided in reflectance and emittance measurements with a range of sensors. Remote sensing continues to evolve as a valuable agronomic tool that provides information to scientists, consultants, and producers about the status of their crops. This area is still relatively new compared with other agronomic fields; however, the information content is providing valuable insights into improved management decisions. This article details the current status of our understanding of how reflectance and emittance have been used to quantitatively assess agronomic parameters and some of the challenges facing future generations of scientists seeking to further advance remote sensing for agronomic applications.
504 citations
TL;DR: In this article, a model aircraft was used to acquire high-resolution digital images of corn, alfalfa, and soybeans from a consumer-oriented digital camera, where colored tarpaulins were used to calibrate the images and a Normalized Green-Red Difference Index (NGRDI) was used.
Abstract: Remote sensing is a key technology for precision agriculture to assess actual crop conditions. Commercial, high-spatial-resolution imagery from aircraft and satellites are expensive so the costs may outweigh the benefits of the information. Hobbyists have been acquiring aerial photography from radio-controlled model aircraft; we evaluated these very-low-cost, very high-resolution digital photography for use in estimating nutrient status of corn and crop biomass of corn, alfalfa, and soybeans. Based on conclusions from previous work, we optimized an aerobatic model aircraft for acquiring pictures using a consumer-oriented digital camera. Colored tarpaulins were used to calibrate the images; there were large differences in digital number (DN) for the same reflectance because of differences in the exposure settings selected by the digital camera. To account for differences in exposure a Normalized Green–Red Difference Index [(NGRDI = (Green DN − Red DN)/(Green DN + Red DN)] was used; this index was linearly related to the normalized difference of the green and red reflectances, respectively. For soybeans, alfalfa and corn, dry biomass from zero to 120 g m−2 was linearly correlated to NGRDI, but for biomass greater than 150 g m−2 in corn and soybean, NGRDI did not increase further. In a fertilization experiment with corn, NGRDI did not show differences in nitrogen status, even though areas of low nitrogen status were clearly visible on late-season digital photographs. Simulations from the SAIL (Scattering of Arbitrarily Inclined Leaves) canopy radiative transfer model verified that NGRDI would be sensitive to biomass before canopy closure and that variations in leaf chlorophyll concentration would not be detectable. There are many advantages of model aircraft platforms for precision agriculture; currently, the imagery is best visually interpreted. Automated analysis of within-field variability requires more work on sensors that can be used with model aircraft platforms.
412 citations
TL;DR: In this article, artificial neural network (ANN) models were used to predict Maryland corn and soybean yields for typical climatic conditions and compared with linear regression models at state, regional, and local levels.
285 citations
Cited by
More filters
TL;DR: In this paper, the authors provide a review of fundamental concepts of drought, classification of droughts, drought indices, historical Droughts using paleoclimatic studies, and the relation between DAs and large scale climate indices.
3,352 citations
TL;DR: An overview of the GMES Sentinel-2 mission including a technical system concept overview, image quality, Level 1 data processing and operational applications is provided.
2,517 citations
Journal Article•
TL;DR: Denman et al. as discussed by the authors presented the Couplings between changes in the climate system and biogeochemistry Coordinating Lead Authors: Kenneth L. Denman (Canada), Guy Brasseur (USA, Germany), Amnat Chidthaisong (Thailand), Philippe Ciais (France), Peter M. Cox (UK), Robert E. Austin (USA), D.B. Wofsy (USA) and Xiaoye Zhang (China).
Abstract: Couplings Between Changes in the Climate System and Biogeochemistry Coordinating Lead Authors: Kenneth L. Denman (Canada), Guy Brasseur (USA, Germany) Lead Authors: Amnat Chidthaisong (Thailand), Philippe Ciais (France), Peter M. Cox (UK), Robert E. Dickinson (USA), Didier Hauglustaine (France), Christoph Heinze (Norway, Germany), Elisabeth Holland (USA), Daniel Jacob (USA, France), Ulrike Lohmann (Switzerland), Srikanthan Ramachandran (India), Pedro Leite da Silva Dias (Brazil), Steven C. Wofsy (USA), Xiaoye Zhang (China) Contributing Authors: D. Archer (USA), V. Arora (Canada), J. Austin (USA), D. Baker (USA), J.A. Berry (USA), R. Betts (UK), G. Bonan (USA), P. Bousquet (France), J. Canadell (Australia), J. Christian (Canada), D.A. Clark (USA), M. Dameris (Germany), F. Dentener (EU), D. Easterling (USA), V. Eyring (Germany), J. Feichter (Germany), P. Friedlingstein (France, Belgium), I. Fung (USA), S. Fuzzi (Italy), S. Gong (Canada), N. Gruber (USA, Switzerland), A. Guenther (USA), K. Gurney (USA), A. Henderson-Sellers (Switzerland), J. House (UK), A. Jones (UK), C. Jones (UK), B. Karcher (Germany), M. Kawamiya (Japan), K. Lassey (New Zealand), C. Le Quere (UK, France, Canada), C. Leck (Sweden), J. Lee-Taylor (USA, UK), Y. Malhi (UK), K. Masarie (USA), G. McFiggans (UK), S. Menon (USA), J.B. Miller (USA), P. Peylin (France), A. Pitman (Australia), J. Quaas (Germany), M. Raupach (Australia), P. Rayner (France), G. Rehder (Germany), U. Riebesell (Germany), C. Rodenbeck (Germany), L. Rotstayn (Australia), N. Roulet (Canada), C. Sabine (USA), M.G. Schultz (Germany), M. Schulz (France, Germany), S.E. Schwartz (USA), W. Steffen (Australia), D. Stevenson (UK), Y. Tian (USA, China), K.E. Trenberth (USA), T. Van Noije (Netherlands), O. Wild (Japan, UK), T. Zhang (USA, China), L. Zhou (USA, China) Review Editors: Kansri Boonpragob (Thailand), Martin Heimann (Germany, Switzerland), Mario Molina (USA, Mexico) This chapter should be cited as: Denman, K.L., G. Brasseur, A. Chidthaisong, P. Ciais, P.M. Cox, R.E. Dickinson, D. Hauglustaine, C. Heinze, E. Holland, D. Jacob, U. Lohmann, S Ramachandran, P.L. da Silva Dias, S.C. Wofsy and X. Zhang, 2007: Couplings Between Changes in the Climate System and Biogeochemistry. In: Climate Change 2007: The Physical Science Basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change [Solomon, S., D. Qin, M. Manning, Z. Chen, M. Marquis, K.B. Averyt, M.Tignor and H.L. Miller (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA.
2,208 citations
TL;DR: The MODIS BRDF/Albedo algorithm makes use of a semi-empirical kernel-driven bidirectional reflectance model and multidate, multispectral data to provide global 1-km gridded and tiled products of the land surface every 16 days.
2,110 citations
TL;DR: In this paper, a method for minimizing the effect of leaf chlorophyll content on the prediction of green LAI was presented, and new algorithms that adequately predict the LAI of crop canopies.
1,915 citations