Directional Area Scattering Factor



In the field “Input Raster”, specify the input data set.

This should be a hyperspectral image covering at least the spectral range of 690 to 900 nm. The approach may work with multispectral data with red edge bands like Sentinel-2.


In the field “DASF Output Raster”, the DASF file name can be set. Otherwise, the output will be saved as a temporary file.

In the field “DASF retrieval quality Output Raster”, a retrieval quality image file name can be set. Otherwise, the retrieval quality image will be saved as a temporary file. The image consists of two bands: an R2 (coefficient of determination) image and an image of the p-value of the regression.


The DASF is an estimate of the ratio of the leaf area that forms the canopy boundary, as seen along a given direction, to the total leaf area (Knyazikhin et al., 2013).

Ollinger et al. (2008) developed a regression model for foliar N concentration across several North American mixed forests that exploits a high positive correlation between foliar N and near infrared (NIR) reflectance. The model explained 79 per cent of the variance for a data range of 0.75 to 2.35 % N. The regression model was applied to all forested areas in North America using MODIS satellite data. However, Knyazikhin et al. (2013) pointed out that the positive correlation between reflectance and the content of an absorbing material is counter-intuitive, contending that the observed relationship must be spurious. They showed that the NIR reflectance of closed forest canopies can be explained to a very high degree (R2 = 0.81) by the broadleaf fraction of a canopy and that the N content is also highly dependent on the broadleaf fraction (R2 = 0.89). This corresponds to the common knowledge that (a) leaves contain significantly higher amounts of N than needles and (b) that conifers appear darker than broadleaf trees. The lower reflectivity of coniferous trees is mostly due to structural effects: needle shoots have a much higher photon recollision probability due to their higher complexity compared to broadleaves. Knyazikhin et al. (2013) propose a normalizing factor, the DASF, that takes this complexity into account and can be easily computed from remote sensing data. If the reflectance of closed forest stands is divided by the DASF, the structural differences are removed from the signal. The resulting signal is termed canopy scattering coefficient (CSC). By adopting this normalization strategy, Wang et al. (2017) were able to estimate foliar N concentration of mixed forest stands in the Bavarian forest national park from airborne hyperspectral imagery using continuous wavelet analysis with a coefficient of determination of 0.65 for a range of 1.45 to 3.29 %. (Hill et al., 2019)


Knyazikhin, Y., Schull, M.A., Stenberg, P., Mõttus, M., Rautiainen, M., Yang, Y., Marshak, A., Latorre Carmona, P., Kaufmann, R.K., Lewis, P., Disney, M.I., Vanderbilt, V., Davis, A.B., Baret, F., Jacquemoud, S., Lyapustin, A. & Myneni, R.B. (2013). Hyperspectral remote sensing of foliar nitrogen content. Proceedings of the National Academy of Sciences, 110, E185-E192. DOI:10.1073/pnas.1210196109,

Ollinger, S.V., Richardson, A.D., Martin, M.E., Hollinger, D.Y., Frolking, S.E., Reich, P.B., Plourde, L.C., Katul, G.G., Munger, J.W., Oren, R., Smith, M.-L., Paw U, K.T., Bolstad, P.V., Cook, B.D., Day, M.C., Martin, T.A., Monson, R.K. & Schmid, H.P. (2008). Canopy nitrogen, carbon assimilation, and albedo in temperate and boreal forests: Functional relations and potential climate feedbacks. Proceedings of the National Academy of Sciences, 105, 19336-19341. DOI:10.1073/pnas.0810021105,

Wang, Z., Skidmore, A.K., Wang, T., Darvishzadeh, R., Heiden, U., Heurich, M., Latifi, H. & Hearne, J. (2017). Canopy foliar nitrogen retrieved from airborne hyperspectral imagery by correcting for canopy structure effects. International Journal of Applied Earth Observation and Geoinformation, 54, 84-94. DOI:10.1016/j.jag.2016.09.008,

Hill, J., Buddenbaum, H., & Townsend, P.A. (2019). Imaging Spectroscopy of Forest Ecosystems: Perspectives for the Use of Space-borne Hyperspectral Earth Observation Systems. Surveys in Geophysics, 40, 553-588. DOI:10.1007/s10712-019-09514-2,