Features
HYSOMA structure:
The HYSOMA work flow, from ortho-rectified reflectance to soil attributes maps is schematically presented here:
1- HYSOMA reduces the spectral range to the number of bands, which are suitable for any of the further analyses. Bands which do not provide meaningful values are excluded from the available band list.
2- The software selects spectrally soil dominated pixels and eliminates from the image all pixels which are not dominated by a soil signature. This is realized in HYSOMA by masking and excluding water pixels and vegetation pixels, in both vital and dry condition. Water dominated pixels are excluded per default through the Normalised Difference Red Blue Index (NDRBI) as suggested by Carter (1991) and Zakaluk and Ranjan (2008), which simply uses the ratio of the difference and the sum between the red (660 nm by default) and blue band (460 nm by default). Another method to select land pixels is to use as input the water mask generated as EUFAR pre-processing quality layer (QC_water).
To mask the remaining non-soil pixels, HYSOMA identifies vegetation dominated regions and excludes them from further processing.
3- Finally, the HYSOMA Soil Mapping module performs soil functions, and produces soil maps based on the spectrally soil dominant pixels, which remain from the soil masking procedure. The one-dimensional grey value maps can easily be imported and visualized in any image processing software. In total, for the 5 soil selected parameter, 11 algorithms are proposed (see Table) and 11 soil map files are created, plus the map file associated with the soil quality layer. Additionally, map files associated with the soil selection procedure (water map, NDVI map, CAI map, soil dominant pixels map) are saved. Also, a HYSOMA run report file can be uploaded by selecting "load last log file" in the main interface.
Soil mapping tools:
In the following table we present the soil functions currently available in HYSOMA in terms of algorithms proposed, required spectral coverage for each parameter, and estimated soil parameters:
Table: Overview of HYSOMA automatic soil functions for identification and semi-quantification. RI: Redness Index, SWIR FI: Short-Wave Infrared Fine particles Index, NSMI: Normalised Soil Moisture Index, SMGM: Soil Moisture Gaussian Modelling (EUFAR deliverable DJ244.v1).
Soil analyses tools:
Generate Spectral Library File: This option allows experimented or non-experimented users to extract individual spectra from the input image based on their geographic coordinates, and to put them in a spectral library file. Also, HYSOMA reads as input data file not only hyperspectral images but spectral library file in ENVI format, on which all soil functions can be then performed.
Generate Calibration File: This option allows experimented users to perform fully quantitative mapping using input field data for calibration. This input field data option allows calibrating automatically generated soil semi-quantified maps (HYSOMA automatic soil functions) with field measurements. Two methods are proposed. Either the users enter directly a field measurement file with name of field location, coordinates X,Y and absolute value of soil parameter, and HYSOMA performs the calibration and delivers as output a quantitative soil map file, or the users give as input already calculated gains and offsets for calibration.
Generate Validation File: This option allows the users to extract from HYSOMA output soil maps the soil parameter values of individual points based on their geographic coordinates.
References:
Bartholomeus, H.M., Schaepman, M.E., Kooistra, L., Stevens, A., Hoogmoed, W.B., and Spaargaren, O.S.P. (2008), Spectral reflectance based indices for soil organic carbon quantification. Geoderma, 145, 28-36
Clark, R.N., Gallagher, A.J. and Swayze, G.A. (1990). Material Absorption Band Depth Mapping of Imaging Spectrometer Data Using a Complete Band Shape Least-Squares Fit with Library Reference Spectra. In, Proceedings of the Second Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) Workshop (pp. 176-186).
Haubrock, S.-N., Chabrillat, S., Lemmnitz, C. and Kaufmann, H. (2008a), Surface soil moisture quantification models from reflectance data under field conditions, Int. J. Remote Sensing, 29 (1): 3-29.
Haubrock, S.-N., Chabrillat, S., Kuhnert, M., Hostert, P. and Kaufmann, H. (2008b), Surface soil moisture quantification and validation based on hyperspectral data and field measurements, Journal of Applied Remote Sensing, Vol. 2, 023552
Levin, N., Kidron, G.J. and Ben-Dor, E. (2007), Surface properties of stabilizing coastal dunes: combining spectral and field analyses, Sedimentology, 54, 771-788.
Madeira, J., Bedidi, A., Cervelle, B., Pouget, M. and Flay, N. 1997. Visible spectrometric indices of hematite (Hm) and goethite (Gt) content in lateritic soils: the application of a Thematic Mapper (TM) image for soil-mapping in Brasilia, Brazil. Int. J. Remote Sens. 18(13): 2835-2852.
Mathieu, R., Pouget, M., Cervelle, B. and Escadafal, R. 1998. Relationships between satellitebased radiometric indices simulated using laboratory reflectancedata and typic soil color of an arid environment. Remote Sens. Environ. 66: 17-28.
Whiting, M.L., Li, L., and Ustin, S.L. (2004), Predicting water content using Gaussian model on soil spectra, Remote Sensing of the Environment, 89, 535-552.