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Using Hyperspectral Remote Sensing Data for Retrieving Canopy Chlorophyll and Nitrogen Content

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2 Author(s)
Clevers, J.G.P.W. ; Lab. of Geo-Inf. Sci. & Remote Sensing, Wageningen Univ., Wageningen, Netherlands ; Kooistra, L.

Plant stress is often expressed as a reduction in amount of biomass or leaf area index (LAI). In addition, stress may affect the plant pigment system, influencing the photosynthetic capacity of plants. Chlorophyll content is the main driver for this primary production. The chlorophyll content is indirectly related to the nitrogen (N) content. In this paper emphasis is on estimation of canopy chlorophyll content and N content using remote sensing techniques. Hyperspectral reflectance data representing a range of canopies were simulated using the PROSAIL radiative transfer model at a 1 nm sampling interval. Various indices were tested for estimating canopy chlorophyll content. Subsequently, tests with field data were performed for sampling locations within an extensively grazed fen meadow using ASD FieldSpec measurements and within a potato field with a Cropscan radiometer for estimating canopy N content. PROSAIL simulations showed that the red-edge chlorophyll index (CIred edge) was linearly related to the canopy chlorophyll content over the full range of potential values (R2=0.94) . In contrast, highly non-linear relationships of chlorophyll content with most traditional red-edge indices were found. At the study sites the CI2 was found to be a good and linear estimator of canopy N content (no chlorophyll was measured) for both the grassland site (R2=0.77) and for the potato field (R2=0.88) . The latter number refers to plots showing no “luxury” N consumption. However, for the full potato data set, including highly fertilized plants, an exponential relationship yielded a better fit (R2=0.85) as compared to a linear fit (R2=0.65) . Currently, this approach can, e.g., be applied with MERIS and Hyperion data and with the upcoming Sentinel-2 and -3 systems.

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Selected Topics in Applied Earth Observations and Remote Sensing, IEEE Journal of  (Volume:5 ,  Issue: 2 )