• Home
  • A scalable physical model based on remote sensing in paddy yield estimation

Share To

Article Url


Manuscript ID : JSDE-2211-1248 (R1) Visit : 93 Page: 83 - 106

Article Type: Original Research

A scalable physical model based on remote sensing in paddy yield estimation

Subject Areas : soil pollution

Ehsan  Asmar 1 (Ph.D. Candidate, Department of Remote Sensing and GIS, Faculty of Natural Resources and Environment, Science and Research Branch, Islamic Azad University, Tehran, Iran.)
Mohammad H.  Vahidnia 2 (Assistant Professor, Department of Remote Sensing and GIS, Faculty of Natural Resources and Environment, Science and Research Branch, Islamic Azad University, Tehran, Iran. * (Corresponding Author))
Mojtaba  Rezaei 3 (Assistant Professor, Rice Research Institute of Iran, Agricultural Research, Education and Extension Organization (AREEO), Rasht, Iran.)
Ebrahim  Amiri 4 (Professor, Department of Water Engineering, Lahijan Branch, Islamic Azad University, Lahijan, Iran.)

Received: 2022-11-20 Accepted : 2023-02-22 Published : 2023-03-21

Keywords: Sustainable Development of Agriculture, Google Earth Engine (GEE), Remote sensing, Paddy Yield Model, Scalable,

Abstract :

Background and Objective: Rice is one of the most strategic plants in Iran. On the other hand, agriculture makes a wide variety of environmental amenities and problems. Thus researches that help the production and sustainable development in this area are significant. The main purpose of this research is the design and development of a scalable remote sensing-based paddy yield model.Material and Methodology: In this study, we used several different images available in Google Earth Engine (GEE) to estimate paddy yield at various temporal (growing seasons) and spatial scales (from 30 m resolution to regional scales). Then, a remote sensing-based light use efficiency (LUE) model integrated with inanimate environmental stressors, was implemented. This operational model was assessed against actual field-level yield data in 2016, 2017, and 2019 growing seasons across more than 691 paddy fields in Gilan province.The efficiency of the current model was evaluated through different statistical measures. The results showed a positive correlation and a signed agreement between the estimated and measured values so that in the studied growing seasons, the average correlation coefficient (R) and agreement index (d) was equal to 0.55. The average RMSE equal to 500 kg/ha, the average MAE equal to 440 kg/ha, and the average NRMSE equal to 0.12, all indicate that the accuracy of the model in estimating crop yield in these locations and years is satisfactory. Also, the submitted model showed the appropriate variability of yield values at the farm scale.Discussion and conclusion: In general, this new approach has confirmed that the use of remote sensing in the GEE is appropriate for estimating crop yield at various temporal and spatial scales, as the current model can be utilized in a wide range of applications such as agricultural management and insurance.  

References:


  1. Okhovat, M., and D. Vakili. "Rice (cultivation, keep, Harvest). Tehran University P." 1996. (In Persian)
    2.
  2. Lobell DB. The use of satellite data for crop yield gap analysis. Field Crops Research. 2013 Mar 1;143:56-64.
    3.
  3. Khaki, S.;Wang, L. Crop yield prediction using deep neural networks. Front. Plant Sci. 2019, 10, 621.
    4.
  4. Kross, A.; McNairn, H.; Lapen, D.; Sunohara, M.; Champagne, C. Assessment of RapidEye vegetation indices for estimation of leaf area index and biomass in corn and soybean crops. Int. J. Appl. Earth Obs. Geoinf. 2015, 34, 235–248.
    5.
  5. Scharf, P.C.; Lory, J.A. Calibrating corn color from aerial photographs to predict sidedress nitrogen need. Agron. J. 2002, 94, 397–404.
    6.
  6. Bastiaanssen,W.G.; Molden, D.J.; Makin, I.W. Remote sensing for irrigated agriculture: Examples from research and possible applications. Agric. Water Manag. 2000, 46, 137–155.
    7.
  7. Mahlein, A.K.; Oerke, E.-C.; Steiner, U.; Dehne, H.W. Recent advances in sensing plant diseases for precision crop protection. Eur. J. Plant Pathol. 2012, 133, 197–209.
    8.
  8. Lobell DB, Thau D, Seifert C, Engle E, Little B. A scalable satellite-based crop yield mapper. Remote Sensing of Environment. 2015 Jul 1;164:324-33.
    9.
  9. Lichtenberg E. Agriculture and the environment. Handbook of agricultural economics. 2002 Jan 1;2:1249-313.
    10.
  10. Khan, A.; Stöckle, C.O.; Nelson, R.L.; Peters, T.; Adam, J.C.; Lamb, B.; Chi, J.; Waldo, S. Estimating Biomass and Yield Using METRIC Evapotranspiration and Simple Growth Algorithms. Agron. J. 2019, 111, 536–544.
    11.
  11. Asseng, S.; Ewert, F.; Rosenzweig, C.; Jones, J.W.; Hatfield, J.L.; Ruane, A.C.; Boote, K.J.; Thorburn, P.J.; Rötter, R.P.; Cammarano, D.; et al. Uncertainty in simulating wheat yields under climate change. Nat. Clim. Chang. 2013, 3, 827.
    12.
  12. Monteith, J. Solar radiation and productivity in tropical ecosystems. J. Appl. Ecol. 1972, 9, 747–766.
    13.
  13. Prince SD. A model of regional primary production for use with coarse resolution satellite data. International Journal of Remote Sensing. 1991 Jun 1;12(6):1313-30.
    14.
  14. Goetz SJ, Prince SD. Modelling terrestrial carbon exchange and storage: evidence and implications of functional convergence in light-use efficiency. InAdvances in ecological research 1999 Jan 1 (Vol. 28, pp. 57-92). Academic Press.
    15.
  15. Heinsch FA, Reeves M, Votava P, Kang S, Milesi C, Zhao M, Glassy J, Jolly WM, Loehman R, Bowker CF, Kimball JS. Gpp and npp (mod17a2/a3) products nasa modis land algorithm. MOD17 User's Guide. 2003:1-57.
    16.
  16. Turner DP, Urbanski S, Bremer D, Wofsy SC, Meyers T, Gower ST, Gregory M. A cross‐biome comparison of daily light use efficiency for gross primary production. Global Change Biology. 2003 Mar;9(3):383-95.
    17.
  17. Jones, C.A. CERES-Maize: A Simulation Model of Maize Growth and Development; Texas A&M University Press: College Station, TX, USA, 1986.
    18.
  18. Williams, J.R.; Jones, C.A.; Dyke, P.T. The EPIC model and its application. In Proceedings of the International Symposium on Minimum Data Sets for Agrotechnology Transfer, Patancheru, India, 21–26 March 1983; pp. 111–121.
    19.
  19. Jones, J.W.; Hoogenboom, G.; Porter, C.H.; Boote, K.J.; Batchelor, W.D.; Hunt, L.;Wilkens, P.W.; Singh, U.; Gijsman, A.J.; Ritchie, J.T. The DSSAT cropping system model. Eur. J. Agron. 2003, 18, 235–265.
    20.
  20. Daughtry, C.; Gallo, K.; Goward, S.; Prince, S.; Kustas, W. Spectral estimates of absorbed radiation and phytomass production in corn and soybean canopies. Remote Sens. Environ. 1992, 39, 141–152.
    21.
  21. Kumar, M. Remote Sensing of Crop Growth. In Plants and the Daylight Spectrum: Proceedings of the First International Symposium of the British Photobiology Society, Leicester, UK, 5–8 January 1981; Smith, H., Ed.; Academic Press: Cambridge, MA, USA, 1981; Volume 1, pp. 133–144.
    22.
  22. Becker-Reshef I, Justice C, Sullivan M, Vermote E, Tucker C, Anyamba A, Small J, Pak E, Masuoka E, Schmaltz J, Hansen M. Monitoring global croplands with coarse resolution earth observations: The Global Agriculture Monitoring (GLAM) project. Remote Sensing. 2010 Jun 18;2(6):1589-609.
    23.
  23. MacDonald RB, Hall FG. Global crop forecasting. Science. 1980 May 16;208(4445):670-9.
    24.
  24. Boschetti, M.; Stroppiana, D.; Confalonieri, R.; Brivio, P.A.; Crema, A.; Bocchi, S. Estimation of rice production at regional scale with a Light Use Efficiency model and MODIS time series. Ital. J. Remote Sens. Riv. Ital. Di Telerilevamento 2011, 43, 63–81.
    25.
  25. Bastiaanssen, W.G.; Ali, S. A new crop yield forecasting model based on satellite measurements applied across the Indus Basin, Pakistan. Agric. Ecosyst. Environ. 2003, 94, 321–340.
    26.
  26. Clevers JG. A simplified approach for yield prediction of sugar beet based on optical remote sensing data. Remote sensing of Environment. 1997 Aug 1;61(2):221-8.
    27.
  27. Lobell DB, Ortiz‐Monasterio JI, Asner GP, Naylor RL, Falcon WP. Combining field surveys, remote sensing, and regression trees to understand yield variations in an irrigated wheat landscape. Agronomy Journal. 2005 Jan;97(1):241-9.
    28.
  28. Moulin S, Bondeau A, Delecolle R. Combining agricultural crop models and satellite observations: from field to regional scales. International Journal of Remote Sensing. 1998 Jan 1;19(6):1021-36.
    29.
  29. Báez‐González AD, Chen PY, Tiscareño‐López M, Srinivasan R. Using satellite and field data with crop growth modeling to monitor and estimate corn yield in Mexico. Crop science. 2002 Nov;42(6):1943-9.
    30.
  30. Shanahan JF, Schepers JS, Francis DD, Varvel GE, Wilhelm WW, Tringe JM, Schlemmer MR, Major DJ. Use of remote‐sensing imagery to estimate corn grain yield. Agronomy Journal. 2001 May;93(3):583-9.
    31.
  31. Gallego J, Carfagna E, Baruth B. Accuracy, objectivity and efficiency of remote sensing for agricultural statistics. Agricultural survey methods. 2010 Apr 16:193-211.
    32.
  32. Field, C.B.; Randerson, J.T.; Malmström, C.M. Global net primary production: Combining ecology and remote sensing. Remote Sens. Environ. 1995, 51, 74–88.
    33.
  33. De Oliveira Ferreira Silva, C.; Lilla Manzione, R.; Albuquerque Filho, J.L. Large-Scale Spatial Modeling of Crop Coefficient and Biomass Production in Agroecosystems in Southeast Brazil. Horticulturae 2018, 4, 44.
    34.
  34. Casanova, D.; Epema, G.; Goudriaan, J. Monitoring rice reflectance at field level for estimating biomass and LAI. Field Crop. Res. 1998, 55, 83–92.
    35.
  35. Christensen, S.; Goudriaan, J. Deriving light interception and biomass from spectral reflectance ratio. Remote Sens. Environ. 1993, 43, 87–95.
    36.
  36. Garcia, R.; Kanemasu, E.T.; Blad, B.L.; Bauer, A.; Hatfield, J.L.; Major, D.J.; Reginato, R.J.; Hubbard, K.G. Interception and use efficiency of light in winter wheat under different nitrogen regimes. Agric. For. Meteorol. 1988, 44, 175–186.
    37.
  37. Rochette, P.; Desjardins, R.L.; Pattey, E.; Lessard, R. Crop net carbon dioxide exchange rate and radiation use efficiency in soybean. Agron. J. 1995, 87, 22–28.
    38.
  38. Richards, R.; Townley-Smith, T. Variation in leaf area development and its effect on water use, yield and harvest index of droughted wheat. Aust. J. Agric. Res. 1987, 38, 983–992.
    39.
  39. Varlet-Grancher, C.B.; Bonhomme, R.; Chartier, M.; Artis, P. Efficience de la conversion de l’énergie solaire par un couvert végétal. Acta Oecologica Oecologia Plantarum 1982, 3, 3–26.
    40.
  40. Das, D.; Mishra, K.; Kalra, N. Assessing growth and yield of wheat using remotely-sensed canopy temperature and spectral indices. Int. J. Remote Sens. 1993, 14, 3081–3092.
    41.
  41. Bueno CS, Lafarge T. Higher crop performance of rice hybrids than of elite inbreds in the tropics: 1. Hybrids accumulate more biomass during each phenological phase. Field Crops Research. 2009 Jun 26;112(2-3):229-37.
    42.
  42. Calera, A.; González-Piqueras, J.; Melia, J. Monitoring barley and corn growth from remote sensing data at field scale. Int. J. Remote Sens. 2004, 25, 97–109.
    43.
  43. Hatfield, J.; Asrar, G.; Kanemasu, E.T. Intercepted photosynthetically active radiation estimated by spectral reflectance. Remote Sens. Environ. 1984, 14, 65–75.
    44.
  44. Asrar, G.; Myneni, R.; Choudhury, B. Spatial heterogeneity in vegetation canopies and remote sensing of absorbed photosynthetically active radiation: A modeling study. Remote Sens. Environ. 1992, 41, 85–103.
    45.
  45. Carlson, T.N.; Ripley, D.A. On the relation between NDVI, fractional vegetation cover, and leaf area index. Remote Sens. Environ. 1997, 62, 241–252.
    46.
  46. Gao, Z.; Xie, X.; Gao, W.; Chang, N.-B. Spatial analysis of terrain-impacted Photosynthetic Active Radiation (PAR) using MODIS data. GIScience Remote Sens. 2011, 48, 501–521.
    47.
  47. McCree, K.J. Photosynthetically active radiation. In Physiological Plant Ecology I; Springer: Berlin, Germany, 1981; pp. 41–55.
    48.
  48. Duffie, J.A.; Beckman,W.A. Solar Engineering of Thermal Processes; John Willey & Sons: New York, NY, USA, 1980.
    49.
  49. Fletcher, A.L.; Sinclair, T.R.; Allen, L.H., Jr. Transpiration responses to vapor pressure deficit in well watered ‘slow wilting’ and commercial soybean. Environ. Exp. Bot. 2007, 61, 145–151.
    50.
  50. Fuchs, M.; Stanghellini, C. The functional dependence of canopy conductance on water vapor pressure deficit revisited. Int. J. Biometeorol. 2018, 62, 1211–1220.
    51.
  51. Oren, R.; Sperry, J.; Katul, G.; Pataki, D.; Ewers, B.; Phillips, N.; Schäfer, K. Survey and synthesis of intra-and interspecific variation in stomatal sensitivity to vapour pressure deficit. Plant Cell Environ. 1999, 22, 1515–1526.
    52.
  52. Rawson, H.; Begg, J.; Woodward, R. The effect of atmospheric humidity on photosynthesis, transpiration and water use efficiency of leaves of several plant species. Planta 1977, 134, 5–10.
    53.
  53. Yuan, W.; Zheng, Y.; Piao, S.; Ciais, P.; Lombardozzi, D.; Wang, Y.; Ryu, Y.; Chen, G.; Dong, W.; Hu, Z. Increased atmospheric vapor pressure deficit reduces global vegetation growth. Sci. Adv. 2019, 5, eaax1396.
    54.
  54. Buck AL. New equations for computing vapor pressure and enhancement factor. Journal of Applied Meteorology and Climatology. 1981 Dec;20(12):1527-32..
    55.
  55. Stewart, J. Modelling surface conductance of pine forest. Agric. For. Meteorol. 1988, 43, 19–35.
    56.
  56. Stewart, J. On the use of the Penrnan-Monteith equation for determining area évapotranspiration. Estimation Areal Evapotranspiration 1987, 3–12.
    57.
  57. Jarvis, P. The interpretation of the variations in leaf water potential and stomatal conductance found in canopies in the field. Philos. Trans. R. Soc. Lond. B Biol. Sci. 1976, 273, 593–610.
    58.
  58. Maidment, D.R. Handbook of Hydrology; McGraw-Hill: New York, NY, USA, 1993; Volume 9780070.
    59.
  59. Field CB, Randerson JT, Malmström CM. Global net primary production: combining ecology and remote sensing. Remote sensing of Environment. 1995 Jan 1;51(1):74-88.
    60.
  60. Bastiaanssen WG, Menenti M, Feddes RA, Holtslag AA. A remote sensing surface energy balance algorithm for land (SEBAL). 1. Formulation. Journal of hydrology. 1998 Dec 1;212:198-212.
    61.
  61. Allen RG, Pereira LS, Raes D, Smith M. Crop evapotranspiration-Guidelines for computing crop water requirements-FAO Irrigation and drainage paper 56. Fao, Rome. 1998;300(9):D05109.
    62.
  62. Allen R, Irmak A, Trezza R, Hendrickx JM, Bastiaanssen W, Kjaersgaard J. Satellite‐based ET estimation in agriculture using SEBAL and METRIC. Hydrological Processes. 2011 Dec 30;25(26):4011-27.
    63.
  63. Waters, R., Allen, R., Bastiaanssen, W., Tasumi, M. & Trezza, R. Surface energy balance algorithms for land, Idaho implementation, advanced training and users manual. NASA USA. 2002.
    64.
  64. Davatgar, N., et al. Detection and assessment of harmful environmental and managerial factors in paddy fields. 2011. (In Persian)
    65.
  65. Venancio LP, Mantovani EC, do Amaral CH, Neale CM, Gonçalves IZ, Filgueiras R, Campos I. Forecasting corn yield at the farm level in Brazil based on the FAO-66 approach and soil-adjusted vegetation index (SAVI). Agricultural Water Management. 2019 Nov 20;225:105779.
    66.
  66. Dong T, Liu J, Qian B, Jing Q, Croft H, Chen J, Wang J, Huffman T, Shang J, Chen P. Deriving maximum light use efficiency from crop growth model and satellite data to improve crop biomass estimation. IEEE Journal of Selected topics in Applied Earth observations and Remote sensing. 2016 Oct 18;10(1):104-17.
    67.
  67. Hedayati A, Vahidnia MH, Behzadi S. Paddy lands detection using Landsat-8 satellite images and object-based classification in Rasht city, Iran. The Egyptian Journal of Remote Sensing and Space Science. 2022 Feb 1;25(1):73-84.
    68.
  68. Asmar E, Sadeghi Niaraki A, Abdeh Kolahchi A, Rezaei M. Scrutiny of TRMM Satellite Precipitation Data Efficiency for Evaluation of Rainfall Damages on Gilan’s Province Rice Farming. Journal of Geomatics Science and Technology. 2019 Sep 10;9(1):57-64. (In Persian)
    69.
  69. Nevavuori P, Narra N, Lipping T. Crop yield prediction with deep convolutional neural networks. Computers and electronics in agriculture. 2019 Aug 1;163:104859.
    70.




 




 

_||_

  1. Okhovat, M., and D. Vakili. "Rice (cultivation, keep, Harvest). Tehran University P." 1996. (In Persian)
    2.
  2. Lobell DB. The use of satellite data for crop yield gap analysis. Field Crops Research. 2013 Mar 1;143:56-64.
    3.
  3. Khaki, S.;Wang, L. Crop yield prediction using deep neural networks. Front. Plant Sci. 2019, 10, 621.
    4.
  4. Kross, A.; McNairn, H.; Lapen, D.; Sunohara, M.; Champagne, C. Assessment of RapidEye vegetation indices for estimation of leaf area index and biomass in corn and soybean crops. Int. J. Appl. Earth Obs. Geoinf. 2015, 34, 235–248.
    5.
  5. Scharf, P.C.; Lory, J.A. Calibrating corn color from aerial photographs to predict sidedress nitrogen need. Agron. J. 2002, 94, 397–404.
    6.
  6. Bastiaanssen,W.G.; Molden, D.J.; Makin, I.W. Remote sensing for irrigated agriculture: Examples from research and possible applications. Agric. Water Manag. 2000, 46, 137–155.
    7.
  7. Mahlein, A.K.; Oerke, E.-C.; Steiner, U.; Dehne, H.W. Recent advances in sensing plant diseases for precision crop protection. Eur. J. Plant Pathol. 2012, 133, 197–209.
    8.
  8. Lobell DB, Thau D, Seifert C, Engle E, Little B. A scalable satellite-based crop yield mapper. Remote Sensing of Environment. 2015 Jul 1;164:324-33.
    9.
  9. Lichtenberg E. Agriculture and the environment. Handbook of agricultural economics. 2002 Jan 1;2:1249-313.
    10.
  10. Khan, A.; Stöckle, C.O.; Nelson, R.L.; Peters, T.; Adam, J.C.; Lamb, B.; Chi, J.; Waldo, S. Estimating Biomass and Yield Using METRIC Evapotranspiration and Simple Growth Algorithms. Agron. J. 2019, 111, 536–544.
    11.
  11. Asseng, S.; Ewert, F.; Rosenzweig, C.; Jones, J.W.; Hatfield, J.L.; Ruane, A.C.; Boote, K.J.; Thorburn, P.J.; Rötter, R.P.; Cammarano, D.; et al. Uncertainty in simulating wheat yields under climate change. Nat. Clim. Chang. 2013, 3, 827.
    12.
  12. Monteith, J. Solar radiation and productivity in tropical ecosystems. J. Appl. Ecol. 1972, 9, 747–766.
    13.
  13. Prince SD. A model of regional primary production for use with coarse resolution satellite data. International Journal of Remote Sensing. 1991 Jun 1;12(6):1313-30.
    14.
  14. Goetz SJ, Prince SD. Modelling terrestrial carbon exchange and storage: evidence and implications of functional convergence in light-use efficiency. InAdvances in ecological research 1999 Jan 1 (Vol. 28, pp. 57-92). Academic Press.
    15.
  15. Heinsch FA, Reeves M, Votava P, Kang S, Milesi C, Zhao M, Glassy J, Jolly WM, Loehman R, Bowker CF, Kimball JS. Gpp and npp (mod17a2/a3) products nasa modis land algorithm. MOD17 User's Guide. 2003:1-57.
    16.
  16. Turner DP, Urbanski S, Bremer D, Wofsy SC, Meyers T, Gower ST, Gregory M. A cross‐biome comparison of daily light use efficiency for gross primary production. Global Change Biology. 2003 Mar;9(3):383-95.
    17.
  17. Jones, C.A. CERES-Maize: A Simulation Model of Maize Growth and Development; Texas A&M University Press: College Station, TX, USA, 1986.
    18.
  18. Williams, J.R.; Jones, C.A.; Dyke, P.T. The EPIC model and its application. In Proceedings of the International Symposium on Minimum Data Sets for Agrotechnology Transfer, Patancheru, India, 21–26 March 1983; pp. 111–121.
    19.
  19. Jones, J.W.; Hoogenboom, G.; Porter, C.H.; Boote, K.J.; Batchelor, W.D.; Hunt, L.;Wilkens, P.W.; Singh, U.; Gijsman, A.J.; Ritchie, J.T. The DSSAT cropping system model. Eur. J. Agron. 2003, 18, 235–265.
    20.
  20. Daughtry, C.; Gallo, K.; Goward, S.; Prince, S.; Kustas, W. Spectral estimates of absorbed radiation and phytomass production in corn and soybean canopies. Remote Sens. Environ. 1992, 39, 141–152.
    21.
  21. Kumar, M. Remote Sensing of Crop Growth. In Plants and the Daylight Spectrum: Proceedings of the First International Symposium of the British Photobiology Society, Leicester, UK, 5–8 January 1981; Smith, H., Ed.; Academic Press: Cambridge, MA, USA, 1981; Volume 1, pp. 133–144.
    22.
  22. Becker-Reshef I, Justice C, Sullivan M, Vermote E, Tucker C, Anyamba A, Small J, Pak E, Masuoka E, Schmaltz J, Hansen M. Monitoring global croplands with coarse resolution earth observations: The Global Agriculture Monitoring (GLAM) project. Remote Sensing. 2010 Jun 18;2(6):1589-609.
    23.
  23. MacDonald RB, Hall FG. Global crop forecasting. Science. 1980 May 16;208(4445):670-9.
    24.
  24. Boschetti, M.; Stroppiana, D.; Confalonieri, R.; Brivio, P.A.; Crema, A.; Bocchi, S. Estimation of rice production at regional scale with a Light Use Efficiency model and MODIS time series. Ital. J. Remote Sens. Riv. Ital. Di Telerilevamento 2011, 43, 63–81.
    25.
  25. Bastiaanssen, W.G.; Ali, S. A new crop yield forecasting model based on satellite measurements applied across the Indus Basin, Pakistan. Agric. Ecosyst. Environ. 2003, 94, 321–340.
    26.
  26. Clevers JG. A simplified approach for yield prediction of sugar beet based on optical remote sensing data. Remote sensing of Environment. 1997 Aug 1;61(2):221-8.
    27.
  27. Lobell DB, Ortiz‐Monasterio JI, Asner GP, Naylor RL, Falcon WP. Combining field surveys, remote sensing, and regression trees to understand yield variations in an irrigated wheat landscape. Agronomy Journal. 2005 Jan;97(1):241-9.
    28.
  28. Moulin S, Bondeau A, Delecolle R. Combining agricultural crop models and satellite observations: from field to regional scales. International Journal of Remote Sensing. 1998 Jan 1;19(6):1021-36.
    29.
  29. Báez‐González AD, Chen PY, Tiscareño‐López M, Srinivasan R. Using satellite and field data with crop growth modeling to monitor and estimate corn yield in Mexico. Crop science. 2002 Nov;42(6):1943-9.
    30.
  30. Shanahan JF, Schepers JS, Francis DD, Varvel GE, Wilhelm WW, Tringe JM, Schlemmer MR, Major DJ. Use of remote‐sensing imagery to estimate corn grain yield. Agronomy Journal. 2001 May;93(3):583-9.
    31.
  31. Gallego J, Carfagna E, Baruth B. Accuracy, objectivity and efficiency of remote sensing for agricultural statistics. Agricultural survey methods. 2010 Apr 16:193-211.
    32.
  32. Field, C.B.; Randerson, J.T.; Malmström, C.M. Global net primary production: Combining ecology and remote sensing. Remote Sens. Environ. 1995, 51, 74–88.
    33.
  33. De Oliveira Ferreira Silva, C.; Lilla Manzione, R.; Albuquerque Filho, J.L. Large-Scale Spatial Modeling of Crop Coefficient and Biomass Production in Agroecosystems in Southeast Brazil. Horticulturae 2018, 4, 44.
    34.
  34. Casanova, D.; Epema, G.; Goudriaan, J. Monitoring rice reflectance at field level for estimating biomass and LAI. Field Crop. Res. 1998, 55, 83–92.
    35.
  35. Christensen, S.; Goudriaan, J. Deriving light interception and biomass from spectral reflectance ratio. Remote Sens. Environ. 1993, 43, 87–95.
    36.
  36. Garcia, R.; Kanemasu, E.T.; Blad, B.L.; Bauer, A.; Hatfield, J.L.; Major, D.J.; Reginato, R.J.; Hubbard, K.G. Interception and use efficiency of light in winter wheat under different nitrogen regimes. Agric. For. Meteorol. 1988, 44, 175–186.
    37.
  37. Rochette, P.; Desjardins, R.L.; Pattey, E.; Lessard, R. Crop net carbon dioxide exchange rate and radiation use efficiency in soybean. Agron. J. 1995, 87, 22–28.
    38.
  38. Richards, R.; Townley-Smith, T. Variation in leaf area development and its effect on water use, yield and harvest index of droughted wheat. Aust. J. Agric. Res. 1987, 38, 983–992.
    39.
  39. Varlet-Grancher, C.B.; Bonhomme, R.; Chartier, M.; Artis, P. Efficience de la conversion de l’énergie solaire par un couvert végétal. Acta Oecologica Oecologia Plantarum 1982, 3, 3–26.
    40.
  40. Das, D.; Mishra, K.; Kalra, N. Assessing growth and yield of wheat using remotely-sensed canopy temperature and spectral indices. Int. J. Remote Sens. 1993, 14, 3081–3092.
    41.
  41. Bueno CS, Lafarge T. Higher crop performance of rice hybrids than of elite inbreds in the tropics: 1. Hybrids accumulate more biomass during each phenological phase. Field Crops Research. 2009 Jun 26;112(2-3):229-37.
    42.
  42. Calera, A.; González-Piqueras, J.; Melia, J. Monitoring barley and corn growth from remote sensing data at field scale. Int. J. Remote Sens. 2004, 25, 97–109.
    43.
  43. Hatfield, J.; Asrar, G.; Kanemasu, E.T. Intercepted photosynthetically active radiation estimated by spectral reflectance. Remote Sens. Environ. 1984, 14, 65–75.
    44.
  44. Asrar, G.; Myneni, R.; Choudhury, B. Spatial heterogeneity in vegetation canopies and remote sensing of absorbed photosynthetically active radiation: A modeling study. Remote Sens. Environ. 1992, 41, 85–103.
    45.
  45. Carlson, T.N.; Ripley, D.A. On the relation between NDVI, fractional vegetation cover, and leaf area index. Remote Sens. Environ. 1997, 62, 241–252.
    46.
  46. Gao, Z.; Xie, X.; Gao, W.; Chang, N.-B. Spatial analysis of terrain-impacted Photosynthetic Active Radiation (PAR) using MODIS data. GIScience Remote Sens. 2011, 48, 501–521.
    47.
  47. McCree, K.J. Photosynthetically active radiation. In Physiological Plant Ecology I; Springer: Berlin, Germany, 1981; pp. 41–55.
    48.
  48. Duffie, J.A.; Beckman,W.A. Solar Engineering of Thermal Processes; John Willey & Sons: New York, NY, USA, 1980.
    49.
  49. Fletcher, A.L.; Sinclair, T.R.; Allen, L.H., Jr. Transpiration responses to vapor pressure deficit in well watered ‘slow wilting’ and commercial soybean. Environ. Exp. Bot. 2007, 61, 145–151.
    50.
  50. Fuchs, M.; Stanghellini, C. The functional dependence of canopy conductance on water vapor pressure deficit revisited. Int. J. Biometeorol. 2018, 62, 1211–1220.
    51.
  51. Oren, R.; Sperry, J.; Katul, G.; Pataki, D.; Ewers, B.; Phillips, N.; Schäfer, K. Survey and synthesis of intra-and interspecific variation in stomatal sensitivity to vapour pressure deficit. Plant Cell Environ. 1999, 22, 1515–1526.
    52.
  52. Rawson, H.; Begg, J.; Woodward, R. The effect of atmospheric humidity on photosynthesis, transpiration and water use efficiency of leaves of several plant species. Planta 1977, 134, 5–10.
    53.
  53. Yuan, W.; Zheng, Y.; Piao, S.; Ciais, P.; Lombardozzi, D.; Wang, Y.; Ryu, Y.; Chen, G.; Dong, W.; Hu, Z. Increased atmospheric vapor pressure deficit reduces global vegetation growth. Sci. Adv. 2019, 5, eaax1396.
    54.
  54. Buck AL. New equations for computing vapor pressure and enhancement factor. Journal of Applied Meteorology and Climatology. 1981 Dec;20(12):1527-32..
    55.
  55. Stewart, J. Modelling surface conductance of pine forest. Agric. For. Meteorol. 1988, 43, 19–35.
    56.
  56. Stewart, J. On the use of the Penrnan-Monteith equation for determining area évapotranspiration. Estimation Areal Evapotranspiration 1987, 3–12.
    57.
  57. Jarvis, P. The interpretation of the variations in leaf water potential and stomatal conductance found in canopies in the field. Philos. Trans. R. Soc. Lond. B Biol. Sci. 1976, 273, 593–610.
    58.
  58. Maidment, D.R. Handbook of Hydrology; McGraw-Hill: New York, NY, USA, 1993; Volume 9780070.
    59.
  59. Field CB, Randerson JT, Malmström CM. Global net primary production: combining ecology and remote sensing. Remote sensing of Environment. 1995 Jan 1;51(1):74-88.
    60.
  60. Bastiaanssen WG, Menenti M, Feddes RA, Holtslag AA. A remote sensing surface energy balance algorithm for land (SEBAL). 1. Formulation. Journal of hydrology. 1998 Dec 1;212:198-212.
    61.
  61. Allen RG, Pereira LS, Raes D, Smith M. Crop evapotranspiration-Guidelines for computing crop water requirements-FAO Irrigation and drainage paper 56. Fao, Rome. 1998;300(9):D05109.
    62.
  62. Allen R, Irmak A, Trezza R, Hendrickx JM, Bastiaanssen W, Kjaersgaard J. Satellite‐based ET estimation in agriculture using SEBAL and METRIC. Hydrological Processes. 2011 Dec 30;25(26):4011-27.
    63.
  63. Waters, R., Allen, R., Bastiaanssen, W., Tasumi, M. & Trezza, R. Surface energy balance algorithms for land, Idaho implementation, advanced training and users manual. NASA USA. 2002.
    64.
  64. Davatgar, N., et al. Detection and assessment of harmful environmental and managerial factors in paddy fields. 2011. (In Persian)
    65.
  65. Venancio LP, Mantovani EC, do Amaral CH, Neale CM, Gonçalves IZ, Filgueiras R, Campos I. Forecasting corn yield at the farm level in Brazil based on the FAO-66 approach and soil-adjusted vegetation index (SAVI). Agricultural Water Management. 2019 Nov 20;225:105779.
    66.
  66. Dong T, Liu J, Qian B, Jing Q, Croft H, Chen J, Wang J, Huffman T, Shang J, Chen P. Deriving maximum light use efficiency from crop growth model and satellite data to improve crop biomass estimation. IEEE Journal of Selected topics in Applied Earth observations and Remote sensing. 2016 Oct 18;10(1):104-17.
    67.
  67. Hedayati A, Vahidnia MH, Behzadi S. Paddy lands detection using Landsat-8 satellite images and object-based classification in Rasht city, Iran. The Egyptian Journal of Remote Sensing and Space Science. 2022 Feb 1;25(1):73-84.
    68.
  68. Asmar E, Sadeghi Niaraki A, Abdeh Kolahchi A, Rezaei M. Scrutiny of TRMM Satellite Precipitation Data Efficiency for Evaluation of Rainfall Damages on Gilan’s Province Rice Farming. Journal of Geomatics Science and Technology. 2019 Sep 10;9(1):57-64. (In Persian)
    69.
  69. Nevavuori P, Narra N, Lipping T. Crop yield prediction with deep convolutional neural networks. Computers and electronics in agriculture. 2019 Aug 1;163:104859.
    70.




 




 

Sanad

Sanad is a platform for managing Azad University publications

Links

Related Centers

Technical Support

Official pages

The rights to this website are owned by the Raimag Press Management System.
Copyright © 2021-2024

Home| Login| About Us| Contact Us|
[فارسی] [العربية] [fa] [ar]