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Mapping Soil Organic Carbon Concentration for Multiple Fields with Image Similarity Analysis

^a Dep. of Crop and Soil Sciences, Univ. of Georgia, Athens, GA 30602
^b Global Hydrology and Climate Center, NASA, Huntsville, AL 35806

View larger version (133K): [in this window] [in a new window]   Fig. 1. The 10 crop fields selected from the NASA Advanced Thermal and Land Applications Sensor (ATLAS) data. Soil samples in circles were used for model development and those in stars were used for model validation. Fields in Group 1 (Flds 1, 2, and 3) are shown with red boundaries and Fields in Group 2 (Flds 4, 5, 6, and 7) are shown with yellow boundaries. The two groups were used for mapping soil organic C concentrations.

View larger version (17K): [in this window] [in a new window]   Fig. 2. The architecture of a typical multilayer perception network.

View larger version (12K): [in this window] [in a new window]   Fig. 3. Input, output, and the architecture of the Ward neural network system (WNNS). The input and output are part of the algorithm structure. In the WNNS, PP is the preprocessor, BP1 to BP3 are the three multilayer perception networks, and RC is the result combination.

View larger version (59K): [in this window] [in a new window]   Fig. 4. Maps of soil organic C (SOC) concentrations for the seven fields sampled. The points in Field 2 were locations selected for examining the consistence between the maps developed with the field-by-field approach and the approach using similarity analysis.

View larger version (12K): [in this window] [in a new window]   Fig. 5. The linear relationships between measured and predicted soil organic C (SOC) concentration for the two groups of fields. The dash lines are 1:1.

View larger version (11K): [in this window] [in a new window]   Fig. 6. The linear relationship between predicted soil organic C (SOC) concentrations with the field-by-field approach and the approach using similarity analysis for Field 2. The dash line is 1:1.