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Application of Artificial Neural Network for Soil Moisture Prediction Incorporating the Effects of Surface Roughness and Vegetation
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 184 - Number 30 |
| Year of Publication: 2022 |
| Authors: Veena C.S. |
10.5120/ijca2022922346
|
Veena C.S. . Application of Artificial Neural Network for Soil Moisture Prediction Incorporating the Effects of Surface Roughness and Vegetation. International Journal of Computer Applications. 184, 30 ( Oct 2022), 19-26. DOI=10.5120/ijca2022922346
Abstract
During this critical period of pandemic, agriculture is the main source of income for any country, to be specific developing countries like India. During the 21st century agriculture is not the profession of illiterate villagers but the main occupation of literates too. Nowadays farmers are using modern equipment and technology in the field of agriculture to grow more crop with less effort and in uncongenial atmosphere. Farmers have to grow different crops in different areas and at different time period. To select the type of crop in a particular time period soil moisture of the given field plays a major role which directly depicts the water absorbing capacity of the soil in a given field. So, measurement of the soil moisture of a given field becomes utmost important. After a thorough literature survey it was found that soil moisture probes are inserted in a given field at a particular distance gap which gives the measure of soil moisture. This method is useful for a small field. In order to measure the soil moisture globally, satellite images are decoded using different algorithms to calculate soil moisture. In a step ahead soil moisture is predicted from the previous data using Artificial Neural Network. Since the satellite images are captured from an altitude, surface conditions to be specific – vegetation cover and surface roughness will have a serious effect on the captured image. In this paper an attempt is made to develop an algorithm incorporating the effects of surface conditions to decode the satellite images in calculating the soil moisture.
References
- Wagner, W. Blooschal, G. Pamparon;, P. Calret, J.C. Bizzavvi operational roughness of microwave remote sensing of soil moisture for hydrologic NORD, HYDROL, 18 (1, 1-20, 2007)
- Wang, J.R. and Schmugge, J.J “An empirical model for the complex dielectric permittivity of soil as a function of water content” IEEE Transactions.Geoscience,Remotesensing,Ge-18,288-295
- Doblon, M.C UlabyF.T.Hallikanien M.T And El Sayes M.A “Microwave Di electric behavior of wet soil-part –II dielectric mixing models, IEEE Transactions,Geoscience,Remotesensing,Ge-23,35-46
- Ulaby, F.T.Moore,R.K. and fing A.K. microwave remote sensing, active and passive, vol III from theory to applications,ArtechHome,Massachvset5tes.
- NJOKU, E.G and Kong J.A “Theory for passive microwave remote sensing of near-surface soil moisture Journal of Geo physics, Res, 82, 3108-3118.
- John O Curtis “Moisture effects on the dielectric properties of soils. IEEE, transactions on Geosciences and remote sensing. Vol 39, No 1, pp – 125-128 jan 2007.
- E.Santi, S.Pettinato, S.paloscia, “Estimating Soil Moisture from C and X band SAR using Machine Learning Algorithms and Compact Polarimetry”, IEEE transactions on Geo science and Remote Sensing” 2018 vol-978
- R.Caldecott, M.poirer,and D.E. Svoboda “ A radio frequency probe to measure soil electrical properties.” “The ohio state univ.Columbus,Final Rep 715,616-4 jan 1985
- Campbell Dielectric probe manual V.103 Horseshoe bend, IN Campbell Consulting Dec 1998.
- J.E.Hipp “Soil electromagnetic parameters as functions of frequency, soil density,and soil moisture Proc IEEE Vol 62,PP 98-103,Jan 1974
- T. Schmugge, “Application of passive microwave observations of surface soil moisture”, Journal of hydrology vol 212 – 213, pp 188 – 197 1998.
- Dawson M.S. et al, “Surface Parameter retrieval using fast learning neural networks”, Remote Sensing reviews, vol 7, pp 1-18,1993
- G.R. Olheofrt and D.E. Carpon “Laboratory Measurements of the radiofrequency electrical and magnetic properties of soil from near yuma Arizona “U.S.GeologSurey,Denvar, CO Open file Rep 93-701
- A.M.Thomas “In situ measurement of moisture in soil and similar substances by n”fringe” capacitance,”J.SciInstrumVol 43,pp 21-27 1966
- S.Paloscia, G.Macelloni, E.Santhi, M.Tedesco “The capability of microwave radiometers in retrieving soil moisture profiles: an application of Artificial Neural Networks”,IEEE transactions on Geoscience and Remote sensing,2002
- V.Atluri, H.Chih-cheng and T.L.Coleman “An artificial neural network for classifying and predicting soil moisture and temperature using Levenberg-Marquardt algorithm” presented at Southeastcon 99 proceedings IEEE 1999.
- Choudhary B.J, Schmugge T.J Newton, R.W and chang A.T.C. “Effects of surface roughness on microwave emission of soils”, journal of geophysics Res., 84, pp 5699 – 5706.
- Schmugge J “Microwave of surface soil moisture and temperature in remote sensing of biosphere functioning (R.j. Hobbs and H.A. Money,eds) springer-Verlag, Newyork.
- J.A. Santanellojr, C.D. Peters – Lidard, M.E Garcia, D.M. Mocko, M.A. Tischler, M.S. Moran and D.P. Thoma, “ Using remotely sensed estimates of soil moisture to infer soil texture and hydraulic properties across a semi-arid water shed”., Remote sensing of environment vol 110, pp 79 -97 2007.
- M.S. Dawson, A.K. Fung and M.T. Manry, “A robust statistical – based estimator for soil moisture retrieval from radar measuremens”, Geoscience and Remote sensing, IEEE transactions on, vol 35, pp 57 – 67 1997.
- A.M.Thomas “In situ measurement of moisture in soil and similar substances by “n fringe” capacitance, “J SciInstrumvol 43 pp 21 – 27 1966.
- T.J.Jackson and T.J Schmugge, “vegetation effects on the microwave emission of soils”, Remote sensing of environment, vol 36 p 203 – 212, 1991.
- D. H. Chang and S. Islam, “Estimation of soil physical properties using remote sensing and Artificial Neural Network”, remote sensing of environment, vol 74 pp 534 – 544, 2000.
- Loyala D.G 2006 “Application of neural network methods to the processing of earth observation satellite data”, Neural networks, vol 19, 168 – 177.
- “Neural network tool box”, 1984-2008 in the MATLAB online documentation, the Mathworks, inc.
- M.Dabboor and T.Geldsetzer, 2014 “Towards Sea Ice Classification Using Simulated RADARSAT Constellation Mission Compact Polarimetric SAR Imagery”, Remote Sensing of Environment, Vol 140, pp.189-195.
- S. Haykin, Neural Networks: A Comprehensive Foundation. Macmillan, New York, 199
- HHornik K., Multilayer “ 1989 feed forward network are universal approximators”, Neural Networks, vol 2, no. 5, pp 359-366.
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