{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,4]],"date-time":"2025-11-04T10:23:18Z","timestamp":1762251798029,"version":"build-2065373602"},"reference-count":35,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2011,8,2]],"date-time":"2011-08-02T00:00:00Z","timestamp":1312243200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/3.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The estimation of spatially distributed crop water use or evapotranspiration (ET) can be achieved using the energy balance for land surface algorithm and multispectral imagery obtained from remote sensing sensors mounted on air- or space-borne platforms. In the energy balance model, net radiation (Rn) is well estimated using remote sensing; however, the estimation of soil heat flux (G) has had mixed results. Therefore, there is the need to improve the model to estimate soil heat flux and thus improve the efficiency of the energy balance method based on remote sensing inputs. In this study, modeling of airborne remote sensing-based soil heat flux was performed using Artificial Neural Networks (ANN). Soil heat flux was modeled using selected measured data from soybean and corn crop covers in Central Iowa, U.S.A. where measured values were obtained with soil heat flux plate sensors. Results in the modeling of G indicated that the combination Rn with air temperature (Tair) and crop height (hc) better reproduced measured values when three independent variables were considered. The combination of Rn with leaf area index (LAI) from remote sensing, and Rn with surface aerodynamic resistance (rah) yielded relative larger overall correlation coefficient values when two independent variables were included using ANN. In addition, air temperature (Tair) may be a key variable in the modeling of G as suggested by the ANN application (r of 0.83). Therefore, it is suggested that Rn, LAI, rah and hc and potentially Tair be considered in future modeling studies of G.<\/jats:p>","DOI":"10.3390\/rs3081627","type":"journal-article","created":{"date-parts":[[2011,8,30]],"date-time":"2011-08-30T05:41:28Z","timestamp":1314682888000},"page":"1627-1643","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Soil Heat Flux Modeling Using Artificial Neural Networks and Multispectral Airborne Remote Sensing Imagery"],"prefix":"10.3390","volume":"3","author":[{"given":"Dario J.","family":"Canel\u00f3n","sequence":"first","affiliation":[{"name":"Department of Bioproducts and Biosystems Engineering, University of Minnesota, 1390 Eckles Ave., Saint Paul, MN 55108, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6456-0822","authenticated-orcid":false,"given":"Jos\u00e9 L.","family":"Ch\u00e1vez","sequence":"additional","affiliation":[{"name":"Department of Civil and Environmental Engineering, Colorado State University, 1372 Campus Delivery, Fort Collins, CO 80523, USA"}]}],"member":"1968","published-online":{"date-parts":[[2011,8,2]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"223","DOI":"10.1007\/s00271-007-0088-6","article-title":"ET mapping for agricultural water management: Present status and Challenges","volume":"26","author":"Gowda","year":"2008","journal-title":"Irrig. Sci."},{"unstructured":"Ahmed, R.H. (1997). Estimating Crop Water Requirements of a Command Area Using Multispectral Video Imagery and Geographic Information Systems. [Ph.D. Thesis, Biological and Irrigation Engineering Department, Utah State University].","key":"ref_2"},{"unstructured":"Payero, J.O. (1997). Estimating Evapotranspiration of Reference Crops Using the Remote Sensing Approach. [Ph.D. Thesis, Biological and Irrigation Engineering Department, Utah State University].","key":"ref_3"},{"unstructured":"Neale, C.M.U., Hipps, L.E., Prueger, J.H., Kustas, W.P., Cooper, D.I., and Eichinger, W.E. (2000, January 2\u20137). Spatial Mapping of Evapotranspiration and Energy Balance Components Over Riparian Vegetation Using Airborne Remote Sensing. Proceedings of Remote Sensing and Hydrology 2000, Santa Fe, NM, USA.","key":"ref_4"},{"unstructured":"Ch\u00e1vez, J.L. (2005). Validating Surface Energy Balance Fluxes Derived from Airborne Remote Sensing. [Ph.D. Thesis, Biological and Irrigation Engineering Department, Utah State University].","key":"ref_5"},{"unstructured":"Chavez, J.L., Howell, T.A., Gowda, P., Neale, C.M., and Colaizzi, P.D. (2008, January 2\u20134). Evaluating Airborne Remote Sensing ET Estimates Using Eddy Covariance Systems and a Heat Flux Source Area Function. Proceedings of Irrigation Association Conference, Anaheim, CA, USA. [CDROM].","key":"ref_6"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"319","DOI":"10.1016\/0168-1923(86)90069-9","article-title":"Estimation of soil heat flux from net radiation during the growth of alfalfa","volume":"37","author":"Clothier","year":"1986","journal-title":"Agr. Forest Meteorol."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/0168-1923(94)90097-3","article-title":"Parameterisations for energy transfers from a sparse vine crop","volume":"71","author":"Sene","year":"1994","journal-title":"Agr. Forest Meteorol."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"75","DOI":"10.1016\/0168-1923(86)90029-8","article-title":"An analysis of infrared temperature observations over wheat and calculation of latent heat flux","volume":"37","author":"Choudhury","year":"1986","journal-title":"Agr. Forest Meteorol."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"205","DOI":"10.1016\/0168-1923(90)90033-3","article-title":"Estimation of soil heat flux\/net radiation ration from spectral data","volume":"49","author":"Kustas","year":"1990","journal-title":"Agr. Forest Meteorol."},{"unstructured":"Abdalla, S.H., Neale, C.M.U., Malek, E., and Hipps, L. (1996, January 15\u201318). Estimation of Net Radiation of Sparse Vegetation Using Multispectral Video Imagery. Proceedings of 16th Annual American Geophysical Union Hydrology Days, Fort Collins, CO, USA.","key":"ref_11"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"283","DOI":"10.1016\/0168-1923(87)90021-9","article-title":"Analysis of an empirical model for soil heat flux under a growing wheat crop for estimating evaporation by an infrared based energy balance equation","volume":"39","author":"Choudhury","year":"1987","journal-title":"Agr. Forest Meteorol."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"107","DOI":"10.1016\/S0260-8774(00)00202-8","article-title":"Artificial neural network modelling of the electrical conductivity property of recombined milk","volume":"50","author":"Therdthai","year":"2004","journal-title":"J. Food Eng."},{"key":"ref_14","first-page":"57","article-title":"Artificial neural networks and long-range precipitation prediction in California","volume":"39","author":"Silverman","year":"2000","journal-title":"Am. Meteorol. Soc."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"229","DOI":"10.1016\/j.jhydrol.2004.12.001","article-title":"Groundwater level forecasting using artificial neural networks","volume":"309","author":"Daliakopoulos","year":"2005","journal-title":"J. Hydrol."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"305","DOI":"10.1016\/j.ecolmodel.2005.03.014","article-title":"Artificial neural network application for multi-ecosystem carbon flux simulation","volume":"189","author":"Melesse","year":"2005","journal-title":"Ecol. Model."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"586","DOI":"10.1016\/j.biortech.2008.06.071","article-title":"Total nitrogen and ammonia removal prediction in horizontal subsurface flow constructed wetlands: Use of artificial neural networks and development of a design equation","volume":"100","author":"Akratos","year":"2009","journal-title":"Biores. Technol."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"83","DOI":"10.1061\/(ASCE)0733-9437(2007)133:2(83)","article-title":"Estimating evapotranspiration using artificial neural network and minimum climatological data","volume":"133","author":"Zanetti","year":"2007","journal-title":"J. Irrig. Drain. Eng."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"2225","DOI":"10.1002\/hyp.6819","article-title":"Models for estimating evapotranspiration using artificial neural networks, and their physical interpretation","volume":"22","author":"Kain","year":"2008","journal-title":"Hydrol. Process."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1007\/s00271-010-0230-8","article-title":"Artificial neural networks approach in evapotranspiration modeling: A review","volume":"29","author":"Kumar","year":"2011","journal-title":"Irrig. Sci."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"253","DOI":"10.1007\/s00271-007-0090-z","article-title":"Comparative study of Hargreaves\u2019s and artificial neural network\u2019s methodologies in estimating reference evapotranspiration in a semiarid environment","volume":"26","author":"Khoob","year":"2008","journal-title":"Irrig. Sci."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"791","DOI":"10.1175\/JHM456.1","article-title":"The soil moisture atmosphere coupling experiment (SMACEX) background, hydro-meteorological conditions and preliminary findings","volume":"6","author":"Kustas","year":"2005","journal-title":"J. Hydrometeor."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"187","DOI":"10.1016\/0034-4257(94)90014-0","article-title":"An airborne multispectral video\/radiometer remote sensing system:development and calibration","volume":"49","author":"Neale","year":"1994","journal-title":"Remote Sens. Environ."},{"unstructured":"Cai, B., and Neale, C.M.U. (1999, January 5\u20137). A Method for Constructing Three Dimensional Models from Airborne Imagery. Proceedings of the 17th Biennial Workshop on Color Photography and Videography in Resource Assessment, Reno, NV, USA.","key":"ref_24"},{"unstructured":"Sundararaman, S., and Neale, C.M.U. (May, January 29). Geometric Calibration of the USU Videography System. Proceedings of 16th Biennial Workshop on Videography and Color Photography for Resource Assessment, Weslaco, TX, USA.","key":"ref_25"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"231","DOI":"10.1016\/0034-4257(92)90005-5","article-title":"Bidirectional calibration results of 11 Spectralon and 16 BaSO4 reference reflectance panels","volume":"40","author":"Jackson","year":"1992","journal-title":"Remote Sens. Environ."},{"unstructured":"Berk, A., Bernstein, L.S., and Robertson, D.C. (1989). MODTRAN: A Moderate Resolution Model for LOWTRAN 7, US Aire Force Geophysics Laboratory. Report GL-TR-89-0122.","key":"ref_27"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"337","DOI":"10.1016\/0034-4257(89)90093-X","article-title":"The infrared emissivity of soil and artemisia tridendata and subsequent temperature corrections in a shrub-steppe ecosystem","volume":"27","author":"Hipps","year":"1989","journal-title":"Remote Sens. Environ."},{"key":"ref_29","first-page":"1263","article-title":"Incorporating surface emissivity into a thermal atmospheric correction","volume":"68","author":"Brunsell","year":"2002","journal-title":"Photogramme. Eng. Remote Sensing"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"923","DOI":"10.1175\/JHM467.1","article-title":"Comparing aircraft-based remotely sensed energy balance fluxes with eddy covariance tower data using heat flux source area functions","volume":"6","author":"Neale","year":"2005","journal-title":"J. Hydrometeor."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"901","DOI":"10.1080\/00029890.2000.12005290","article-title":"A unified interpretation of the binomial coefficients, the stirling numbers, and the gaussian coefficients","volume":"107","author":"Kinvalina","year":"2000","journal-title":"Am. Math. Mon."},{"unstructured":"Sofo, A. (2006). General properties involving reciprocals of binomial coefficients. J. Integer Seq., 9, article 06.4.5.","key":"ref_32"},{"unstructured":"Available online: http:\/\/www.alyuda.com\/.","key":"ref_33"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"31","DOI":"10.1109\/2.485891","article-title":"Artificial networks: A tutorial","volume":"9","author":"Jain","year":"1996","journal-title":"Computer"},{"unstructured":"Hagan, M., Demuth, H., and Beale, M. (1996). Neural Network Design, Brooks\/Cole Publishing Company. [1st ed.].","key":"ref_35"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/3\/8\/1627\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T21:56:56Z","timestamp":1760219816000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/3\/8\/1627"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2011,8,2]]},"references-count":35,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2011,8]]}},"alternative-id":["rs3081627"],"URL":"https:\/\/doi.org\/10.3390\/rs3081627","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2011,8,2]]}}}