Integrated narrow-band vegetation indices for prediction of crop chlorophyll content for application to precision agriculture
Introduction
Remote sensing data and techniques have already proven to be relevant to many requirements of crop inventory and monitoring. Different studies and experiments demonstrated their usefulness and feasibility to address various agricultural issues, such as crop classification and mapping Erol & Akdeniz, 1996, Grignetti et al., 1997, Pax-Lenney & Woodcock, 1997, crop forecasting and yield predictions Clevers, 1997, Moran et al., 1995, Rasmussen, 1992, Tucker et al., 1980, crop status and condition Blackmer et al., 1994, Boissard et al., 1993, Clevers et al., 1994, Potdar, 1993, and crop disease and micronutrient deficiency Adams et al., 2000a, Adams et al., 2000b, Adams et al., 1999, Malthus & Madeira, 1993. Nowadays, there is an increased interest in precision farming and the development of smart systems for agricultural resources management; these relatively new approaches aim to increase the productivity, optimize the profitability, and protect the environment. In this context, image-based remote sensing technology is seen as a key tool to provide valuable information that is still lacking or inappropriate to the achievement of sustainable and efficient agricultural practices Daughtry et al., 2000, Moran et al., 1997. More specifically, farmers and agricultural managers are interested in measuring and assessing soil and crop status at specific critical times: first, in earlier growth stages in order to supply adequate fertilizers quantities for a normal growth of the crop, and second, during an advanced development stage for health monitoring and the prediction of yield. For this purpose, remote sensors can play a valuable role in providing time-specific and time-critical information for precision farming, due to their capabilities in measuring biophysical indicators/parameters and detecting their spatial variability. The latter is critical to the variable rate technology, which consists of applying specific inputs, such as fertilizers, for specific soil and crop conditions (Moran et al., 1997). Among the fertilizing elements, nitrogen is generally the most important and also the major limiting factor for crop growth and agriculture productivity.
Nitrogen concentration in green vegetation is related to chlorophyll content, and therefore indirectly to one of the basic plant physiological processes: photosynthesis. When nitrogen supply surpasses vegetation's nutritional needs, the excess is eliminated by runoff and water infiltration leading to pollution of aquatic ecosystems (i.e., eutrophication) (Wood, Reeves, & Himelrick, 1993, cited in Daughtry et al., 2000). This nitrogen loss to the environment represents an economic loss for farmers. However, inappropriate reduction of nitrogen supply could result in reduced yields, and subsequently, substantial economic losses. With this dilemma, the optimal and rational solution is an adequate assessment of nitrogen status and its variability in agricultural landscapes. Since yield is determined by crop condition at the earlier stages of growth, it is mandatory to provide farmers with nitrogen status at those stages in order to supply appropriate rates based upon an accurate assessment of plant growth requirements and deficiencies.
For this purpose, remote sensing techniques have been used to assess crop conditions relative to nitrogen status and effects. Foliage spectral properties, reflectance and transmittance, were found to be affected by nitrogen deficiency (Blackmer, Schepers, Varvel, & Walter-Shea, 1996): nitrogen shortage reduces leaf chlorophyll content, and therefore, increases its transmittance at visible wavelengths. Thus, reflected radiation from crop leaves and canopies has been used both to estimate chlorophyll concentration of crop canopies (Daughtry et al., 2000) and by implication to assess nitrogen variability and stress Blackmer et al., 1994, Blackmer et al., 1996. However, at the canopy scale, nitrogen treatments do not affect leaf chlorophyll content alone; they also induce differences in other biophysical parameters such as: Leaf Area Index (LAI), biomass, and foliage (Walburg, Bauer, Daughtry, & Housley, 1982). Moreover, optical indices developed for chlorophyll content estimation, using crop canopy reflected radiation, are responsive to other vegetation and environmental parameters like LAI and underlying soil reflectance Daughtry et al., 2000, Kim et al., 1994.
It is this multifactor interaction complexity, responsible for canopy spectral reflectance variability at different phenological stages, that inspired this work on developing a methodology for an accurate estimation of crop chlorophyll content. The objectives were: (i) to simulate corn canopy reflectance, using PROSPECT and SAILH radiative transfer models, for various crop optical and biophysical variables; (ii) to elaborate a methodology for estimating crop chlorophyll concentration, using CASI hyperspectral airborne reflectance data; and (iii) to validate the estimates through a comparison with chlorophyll measurements in the laboratory from plot field sampling.
Section snippets
Study area
The study area is one of the four experimental sites of the GEOmatics for Informed Decisions (GEOIDE) project for precision agriculture. It is located near Montreal, at the Horticultural Research and Development Centre of Agriculture and Agri-Food Canada, St-Jean-sur-Richelieu, Quebec, Canada, also known as the L'Acadie experimental research substation, where corn was grown on four adjacent experimental fields. In general, the soils in the fields were clay loam, with 31% sand, 33% silt, and 36%
Sensitivity to chlorophyll content
In a preliminary analysis for individual leaf spectra, chlorophyll indices MCARI (Eq. (1)) and TCARI (Eq. (2)) were plotted as a function of chlorophyll content for leaf reflectances derived by simulations with the PROSPECT model (Fig. 1). As chlorophyll content increases, MCARI initially increases, but then decreases as chlorophyll content exceeds 20 μg/cm2. This functional behavior denotes a sensitivity limitation of MCARI at low pigment concentrations, owing probably to its responsivity to
Conclusion
In this study, leaf and canopy models (PROSPECT and SAILH) were employed to simulate chlorophyll and LAI effects on crop canopy reflectance. Then, a methodology for predicting chlorophyll status from hyperspectral data, based on combining vegetation and chlorophyll indices through scaling up, has been developed and successfully tested with airborne CASI hyperspectral images over a corn crop experimental site.
The methodology was used to investigate and take into account the effects of
Acknowledgements
The authors gratefully acknowledge the financial support provided for this research by GEOIDE (GEOmatics Informed Decisions) and the Canadian Space Agency. We thank Lawrence Gray and Phil Brasher and Heidi Beck of Aviation International for making CASI airborne field campaigns work efficiently. Efforts by Elizabeth Pattey of Agriculture and Agri-Food Canada (Ottawa) for the coordination of the CASI missions with field data collection are much appreciated. We gratefully acknowledge Craig S. T.
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