Assessing crop health in time and space by measuring spectral reflectance of solar radiation from plant canopies

摘要

Total photosynthetic (green) leaf area per unit ground area (green leaf area index, GLAI) has been used to describe and explain the relationship between crop development and yield. Epidemiological principles can be employed to generate a range of disease intensities over time and space; the impact of these epidemics on crop health can be measured nondestructively using a number of remote sensing technologies, including multispectral radiometry. Because yield often is related to the mount of radiation intercepted by the crop canopy (i.e., GLAI), absolute measurements of GLAI should have a better relationship with yield than proportional (visual) estimates of disease intensity. The absolute amount of GLAI that is available to intercept radiation during the cropping season is a function of the total leaf area minus the GLAI that is removed by diseases and pests. Therefore, changes of plant health due to diseases and pests should result in both temporal and spatial pattern changes in GLAI which could be monitored using remote sensing technologies. Using global positioning systems (GPS), geographic information systems (GIS) and geostatistics, remote sensing data can be analyzed and interpreted to i) identify the likely cause(s) of plant stress, ii) quantify the amount of plant stress, and iii) to prescribe crop management practices that would increase the farmer's return on investment. One of the most important advantages in using remote sensing that often goes unappreciated is the fact that remote sensing can provide more reliable (precise) information concerning GLAI/plant health than the best visual disease assessment methods currently available. Examples of yield prediction models and disease gradient models using visual versus percentage reflectance data will be compared for several foliar pathosystems (alfalfa, peanut, soybean, and turfgrass). Case studies to evaluate the risks and benefits of new agricultural biotechnologies using remote sensing and GIS technologies will also be presented.