For crop management, information on the actual status of the crop is important for taking decisions on nitrogen supply, water supply or harvesting. One would also like to take into account the local spatial variation of the crop. Remote sensing has proved to be a useful technique for estimating and mapping the spatial variation of various biophysical variables. Calibration of the image data is crucial in the performance and applicability of this technique. The aim of this paper is to show the possibility to calibrate remotely sensed imagery using fast and non-destructive close-range (below 1.3m height) sensing instruments, thus providing a means for the assessment of plant characteristics over large areas at low costs. This concept was tested on a homogeneously managed grassland field, subdivided into 20 plots of 15 x 3m, at the end of July 2004. Reflected radiation was recorded with an active close-range sensing device, consisting of a visible light and near-infrared (NIR) imaging spectrograph, and a 3CCD camera, equipped with special band filters (central wavelengths are at 600, 710 and 800 nm). An airborne campaign with a four-band UltraCam digital CCD camera was used for extrapolation to larger scales. Plots were harvested, and fresh and dry biomass and leaf nitrogen content were determined. Partial least squares (PLS) models combining spectral and spatial information from the close-sensing device yielded acceptable results in predicting grassland yields and nitrogen content. Subsequently, these predictions were used to calibrate a model with the image data of the remote sensing device. These were then compared, using leave-one-out cross-validation, with the measured field variables, and the model proved to have an acceptable predictive power.
- hyperspectral vegetation indexes
- imaging spectroscopy
- grass swards
- precision agriculture
van der Heijden, G. W. A. M., Clevers, J. G. P. W., & Schut, A. G. T. (2007). Combining close-range and remote sensing for local assessment of biophysical characteristics of arable land. International Journal of Remote Sensing, 28(24), 5485-5502. https://doi.org/10.1080/01431160601105892