http://oar.icrisat.org/id/eprint/3992 2011-11-16T13:19:10Z info:eu-repo/semantics/descriptiveMetadata http://oar.icrisat.org/3992/ Application of Artificial Neural Network (ANN) to Predict Soil Organic Matter Using Remote Sensing Data in Two Ecosystems Ayoubi, S Shahri, A P Karchegani, P M Sahrawat, K L Soil Science Concern over global problems induced by rising CO2 has prompted attention on the role of forests and pastures as carbon ‘storage’ because forests and pastures store a large amount of carbon in vegetation biomass and soil. Soil organic matter (SOM) plays a critical role in soil quality and has the potential to cost-effectively mitigate the detrimental effects of rising atmospheric CO2 and other greenhouse gas emissions that cause global warming and climate change(Causarano-Medina, 2006). SOM, an important source of plant nutrients is itself influenced by land use, soil type, parent material, time, climate and vegetation (Loveland &Webb, 2003). Important climatic factors influencing SOM include rainfall and temperature. Within the same isotherm, the SOM content increases with increase in rainfall regime. For the same isohyet, the SOM content................ InTech Open Access 2011 Book Section PeerReviewed application/pdf en http://oar.icrisat.org/3992/1/InTech-Application_of_artificial_neural_network_ann_to_predict.pdf Ayoubi, S and Shahri, A P and Karchegani, P M and Sahrawat, K L (2011) Application of Artificial Neural Network (ANN) to Predict Soil Organic Matter Using Remote Sensing Data in Two Ecosystems. In: Biomass and Remote Sensing of Biomass. InTech Open Access, InTechWeb.org, pp. 181-196. ISBN 978-953-307-490-0 http://www.intechopen.com/books/show/title/biomass-and-remote-sensing-of-biomass info:eu-repo/semantics/objectFile info:eu-repo/semantics/humanStartPage