Comparison of Geostatistical Method and Remote Sensing in Estimating Rice (Oryza sativa L.) Grain Yield in Guilan Province

بوم شناسی کشاورزی

Article 12, Volume 14, Issue 3 - Serial Number 53, September 2022, Pages 579-599   

Document Type: Research Article

DOI: 10.22067/agry.2021.20308.0

Authors

Pooya Aalaee Bazkiaei¹; Behnam Kamkar^{* 2}; Ebrahim Amiri³; Hossein Kazemi⁴; Mojtaba Rezaei⁵

¹Department of Agronomy, Faculty of Plant Production, Gorgan University of Agricultural Sciences and Natural Resources, Gorgan. Iran.

²Department of Agrotechnology, Faculty of Agriculture, Ferdowsi University of Mashhad, Mashhad, Iran.

³Department of Water Engineering, Lahijan branch, Islamic Azad University, Iran.

⁴Department of Agronomy, Gorgan University of Agricultural Sciences and Natural Resources, Iran.

⁵Rice Research Institute of Iran, Agricultural Research, Education and Extension Organization (AREEO), Rasht, Iran.

Abstract

Introduction
 Food security has been the most important human concern on the planet. Food security and future changes in food prices and crop development undeniably depend on the average yield of crops. Rice is the second largest crop in terms of area under cultivation and provides food for more than half of the world's population. Due to the importance of rice production, it is important to monitor its production on a large scale. Remote sensing and geostatistical methods play an important role in spatial and temporal evaluation of climatic, soil, living factors and management methods. The study of the amount of yield obtained in province of Guilan can provide appropriate information in advancing/pursuing goals such as the study of the amount of yield gap in this region; therefore, this study was conducted to evaluate the yield of rice using two methods of geostatistics and remote sensing approach in 2016 and 2017 in Guilan province.
Materials and Methods
 In this study, different methods of preparing vegetation map to determine rice yield map were evaluated. To compare methods based on satellite-imagery and geostatistical procedure-models to estimate the rice yield of rice cultivated lands in Guilan province, a study was conducted in the 2016 and 2017. For field operations, 320 fields were surveyed to record grain yield (of total 238,000 hectares of rice-grown fields) during the physiological maturity stage. In this study, 33 statistical procedure-models were used to interpolate the amount of grain yield and then the accuracy of interpolation methods were evaluated using various statistical criteria. Satellite-imagery based methods using Landsat 8 satellite Operational Land Imager (OLI) sensor images related to the dates of June 18, August 9, August 21 in 2016, and July 23, August 8 in 2017 and the images of the Sentinel-2 satellite on June 30 and September 13, 2017, were used. Eight satellite-derived vegetation indices were calculated and the relationship between them and the yield variable was extracted using the regression relationship, and the yield map was prepared and evaluated. By fitting the peak logistics model between yield values and vegetation indices and selecting the superior index, the yield map was prepared with the help of remote sensing and the obtained yield maps were compared with different statistical criteria.
Results and Discussion
 The results of the evaluation of geostatistical interpolation methods showed the superiority of the ordinary stable kriging procedure-model over other models. In this study, the RVI vegetation index was selected as the superior index to predict actual yield throughout the Guilan Province. Comparison of geostatistical procedure-models and satellite-imagery oriented models based on the determination coefficient of regressed equations and root mean square error (RMSE) to estimate grain yield in Guilan province showed that both procedures had acceptable accuracy, however, due to the ability of remote sensing to distinguish the pointwise optical reflection of phenomena and to predict yield with high spatial resolution, this method achieved higher accuracy in yield estimating.
Conclusion
 Comparison of geostatistical and remote sensing methods in predicting farm yield indicated that the remote sensing method was more accurate. Early harvest forecasting based on the information extracted from this study showed that the use of image / images obtained in June and August and OLI sensor of Landsat-8 satellite in Guilan province can be used as a basis for forecasting the yield of this plant in the coming years. Such studies in Guilan province, by considering the share of Guilan province in the country's rice supply, will play an effective role in managerial decisions regarding rice supply and demand at the macro level. The results also showed that sampling in the study area based on a regular (systematic) spatial pattern can increase the accuracy of geostatistical methods in estimation of regional yield. The results of this project can provide suitable basic information for other studies such as yield gap, the reasons behind it, the relationship between land suitability and the obtained yield and forecasting and estimating yield in different time periods.
 

 
 
 
 
 
 

Keywords

Actual yield; Kriging; Landsat-8; Sentinel-2

References

Alizadeh Dehkordi, P., Nehbandani, A.R, Hassanpour-bourkheili, S., and Kamkar, B., 2020. Yield gap analysis using remote sensing and modeling approaches: Wheat in the Northwest of Iran. International Journal of Plant Production 1-10.  https://doi.org/10.1007/s42106-020-00095-4

Azhirabi, R., Kamkar, B., and Abdi, O., 2019. Comparison of geostatistical interpolation models (kriging) to estimate soil salinity and wheat yield (A case study: Army field of Aq Qala. Crop Production 12(1): 1-16. (In Persian with English Summary) https://doi.org/10.22069/EJCP.2019.6955.1495

Badsar, M., 2014. Yield gap estimation in wheat fields using GIS, RS and SSM model (A case study: Qaresso basin, Gorgan distinct). M.Sc. Thesis. Gorgan University of Agricultural Sciences and Natural, Iran. 95 p. (In Persian)

Balasundram, S.K., Memarian, H., and Khosla, R., 2013. Estimating oil palm yields using vegetation indices derived from Quickbird. Life Science Journal 10(4): 851-860

Bannari, A., Staenz, K., Haboudane, D., and Khurshid, K., 2006. Sensitivity analysis of chlorophyll indices to soil optical properties using ground-reflectance data. In: 2006 IEEE International Symposium on Geoscience and Remote Sensing (pp. 120-123). https://doi.org/10.1109/IGARSS.2006.36.

Baskent, E.Z., and Keles, S., 2005. Spatial forest planning: A review. Ecological Modeling 188(2-4): 145-173. https://doi.org/10.1016/j.ecolmodel.2005.01.059

Cambardella, C.A., Moorman, T.B., Novak, J.M., Parkin, T.B., Karlen, D.L., Turco, R.F., and Koropaka, A.E., 1994. Field-scale variability of soil properties in central Iowa soils. Soil Science Society of America Journal 58: 1501-1511. https://doi.org/10.2136/sssaj1994.03615995005800050033x

Dente, L., Satalino, G., Mattia, F., and Rinaldi, M., 2008. Assimilation of leaf area index derived from ASAR and MERIS data into CERES-Wheat model to map wheat yield. Remote Sensing of Environment 112(4): 1395-1407. https://doi.org/10.1016/j.rse.2007.05.023

Elvidge, C.D., and Chen, Z., 1995. Comparison of broad-band and narrow-band red and near-infrared vegetation indices. Remote Sensing of Environment 54(1): 38-48. https://doi.org/10.1016/0034-4257(95)00132-K

FAO. 2018. Food and Agricultural Organization of the United Nations (sited in: http://www,fao.org/index_en.htm/, 1/1/2020.

Fatemi, B., and Rezaei, Y., 2006. Basic of Remote Sensing. Azade publication. 257 p. (In Persian)

Ghasemi, M., 2011. Investigating Weed biodiversity in wheat fields of Gorgan County. M.Sc. Thesis. Gorgan University of Agricultural Sciences and Natural Resources, Iran. 114 p. (In Persian with English Summary)

Goudarzi, M., Farahpour, M., and Mosavi, A.R., 2006. Land cover and rangeland classification map using Land sat satellite image (TM) (Case study) Namrood watershed. Rangeland and Desert Research 13(3): 265-277. (In Persian with English Summary)

Gutierrez, M., Norton, R., Thorp, K.R., and Wang, G., 2012. Association of spectral reflectance indices with plant growth and lint yield in upland cotton. Crop Science 52(2): 849-857. https://doi.org/10.2135/cropsci2011.04.0222

Huete, A.R., 1988. A soil-adjusted vegetation index (SAVI). Remote Sensing of Environment 25: 295–309. https://doi.org/10.1016/0034-4257(88)90106-X

Kazemi, H., Tahmasebi Sarvestani, Z., Kamkar, B., Shataei, S., and Sadeghi, S., 2012. Evaluation of geostatistical methods for estimating and zoning of macronutrients in agricultural lands of Golestan province. Water Soil Science. 22(1): 201-218. (In Persian with English Summary)

Khosravi, R., Hemami, M.R. and Malekian, M., 2014. Comparison of geostatistical methods to determine the best bioclimatic data interpolation method for modeling species distribution in Central Iran. Iranian Journal of Applied Ecology 3(8): 55-68. (In Persian with English Summary) 20.1001.1.24763128.1393.3.8.5.5

Kim, Y., Jackson, T., Bindlish, R., Hong, S., Jung, G., and Lee, K., 2013. Retrieval of wheat growth parameters with radar vegetation indices. IEEE Geoscience and Remote Sensing Letters 11(4): 808-812. https://doi.org/10.1109/LGRS.2013.2279255

Kim, Y., and Van Zyl, J., 2009. A time-series approach to estimate soil moisture using polarimetric radar data. IEEE Transactions on Geoscience and Remote Sensing 47: 2519-2527. https://doi.org/10.1109/TGRS.2009.2014944

Koppe, W., Gnyp, M., Hutt, C., Yao, Y., Miao, Y., Chen, X., and Bareth, G., 2013. Rice monitoring with multitemporal and dualpolarimetric TerraSAR-X data.International Journal of Applied Earth Observation and Geoinformation, 21: 568-576. https://doi.org/10.1016/j.jag.2012.07.016

Liu, Z., Zhou, W., Shen, J., He, P., Lei, Q., and Liang, G., 2014. A simple assessment on spatial variability of rice yield and selected soil chemical properties of paddy fields in South China. Geoderma 235: 39-47. https://doi.org/10.1016/j.geoderma.2014.06.027

Lobell, D.B., 2013. The use of satellite data for crop yield gap analysis. Field Crops Research 143: 56-64. https://doi.org/10.1016/j.fcr.2012.08.008

Ma, J.W., Nguyen, C.H., Lee, K., and Heo, J., 2019. Regional-scale rice-yield estimation using stacked auto-encoder with climatic and MODIS data: A case study of South Korea. International Journal of Remote Sensing 40(1): 51-71. https://doi.org/10.1080/01431161.2018.1488291

Mo, X., Liu, S., Lin, Z., Xu, Y., Xiang, Y., and McVicar, T.R., 2005. Prediction of crop yield, water consumption and water use efficiency with a SVAT-crop growth model using remotely sensed data on the North China Plain. Ecological Modelling 183(2): 301-322. https://doi.org/10.1016/j.ecolmodel.2004.07.032

Mohammadi Ahmad Mahmoudi, E., Kamkar, B., and Abdi, O., 2015. Comparison of geostatistical- and remote sensing data-based methods in wheat yield prediction in some of growing stages (A case study: Nemooneh filed, Golestan province). Crop Production 8(2): 51-76. (In Persian with English Summary). 20.1001.1.2008739.1394.8.2.3.5

Nassiri Mahallati, M., Koocheki, A.R., and Jahani, M., 2016. Estimating within field variability of wheat yield using spatial variables: An approach to precision agriculture. Journal of Agroecology 8(3): 329-345. (In Persian with English Summary) https://doi.org/10.22067/jag.v8i3.34502

Pourhadian, H., Kamkar, B., Soltani, A., and Mokhtarpour, H., 2019. Evaluation of forage maize yield gap using an integrated crop simulation model-satellite imagery method (Case study: Four watershed basins in Golestan Province). Archives of Agronomy and Soil Science 65(2): 253-268. https://doi.org/10.1080/03650340.2018.1493579

Rahmat, S.R., Firdaus, R.R., Shaharudin, S.M., and Ling, L.Y., 2019. Leading key players and support system in Malaysian paddy production chain. Cogent Food and Agriculture 5(1): 1708682. https://doi.org/10.1080/23311932.2019.1708682

Raziei, T., 2017. Köppen-Geiger climate classification of Iran and investigation of its changes during 20^th century. Earth and Space Physics 43: 419-439. (In Persian with English Summary).  https://doi.org/10.22059/JESPHYS.2017.58916

Ren, H., Zhou, G., and Zhang, F., 2018. Using negative soil adjustment factor in soil-adjusted vegetation index (SAVI) for aboveground living biomass estimation in arid grasslands. Remote Sensing of Environment 209: 439-445. https://doi.org/10.3390/s21062115

Rezaei Hossein Abad, A.R., 2013. An Investigation on the relationship between soil nutrients and wheat yield using Geographic Information Systems (GIS). M.Sc. Thesis, Gorgan University of Agricultural Sciences and Natural Resources, Iran. 71 p. (In Persian with English Summary)

Sanaienejad, S.H., Shah Tahmasbi, A.R., Sadr Abadi Haghighi, R., and Kelarestani, K.A., 2008. Study of spectral reflection on wheat fields in Mashhad using MODIS data. Journal of Water and Soil Science 12(45):11-19. (In Persian with English Summary) 20.1001.1.24763594.1387.12.45.2.9

SAS Institute., 2015. Base SAS 9.4 procedures guide. SAS Institute. www.sas.com

Shi, H., and Xingguo, M., 2011. Interpreting spatial heterogeneity of crop yield with a process model and remote sensing. Ecological Modeling 222(14): 2530-2541. https://doi.org/10.1016/j.ecolmodel.2010.11.011

Simoes, M.D.S., Rocha, J.V., and Lamparelli, R.A.C., 2005. Spectral variables, growth analysis and yield of sugarcane. Scientia Agricola 62(3): 199-207. https://doi.org/10.1590/S0103-90162005000300001

Siyal, A.A., Dempewolf, J., and Becker-Reshef, I., 2015. Rice yield estimation using Landsat ETM+ Data. Journal of Applied Remote Sensing 9: 1-16. https://doi.org/10.1117/1.JRS.9.095986

Tesfahunegn, G.B., Tamene, L., and Vlek, P.L.G., 2011. Catchment-scale spatial variability of soil properties and implications on site-specific soil management in northern Ethiopia. Soil Tillage Research 117: 124–139. https://doi.org/10.1016/j.still.2011.09.005

Utset, A., Lopez, T., and Diaz, M., 2000. A comparison of soil maps, kriging and a combined method for spatially prediction bulk density and field capacity of Ferralsols in the Havana-Matanaz Plain. Geoderma 96(3): 199-213. https://doi.org/10.1016/S0016-7061(99)00055-5

Wang, J., Dai, Q., Shang, J., Jin, X., Sun, Q., Zhou, G., and Dai, Q., 2019. Field-scale rice yield estimation using Sentinel-1A Synthetic Aperture Radar (SAR) data in coastal saline region of Jiangsu province, China. Remote Sensing 11(19): 2274. https://doi.org/10.3390/rs11192274

Webster, R., and Oliver, M., 2001. Geostatistics for Environmental Scientists. John Wiley & Sons, Ltd, Chichester, 271 p.

Yaghouti, H., Pazira, E., Amiri, E., and Masihabadi, M.H., 2019. The feasibility of using vegetation indices and soil texture to predict rice yield. Polish Journal of Environmental Studies 28(4). DOI: https://doi.org/10.15244/pjoes/81088

Zarco-Tejada, P.J., Ustin, S.L., and Whiting, M.L., 2005. Temporal and spatial relationships between within-field yield variability in cotton and high-spatial hyperspectral remote sensing imagery. Agronomy Journal 97(3): 641-653. https://doi.org/10.2134/agronj2003.0257

Zhu, Y., Yao, X., Tian, Y., Liu, X., and Cao, W., 2008. Analysis of common canopy vegetation indices for indicating leaf nitrogen accumulations in wheat and rice. International Journal of Applied Earth Observation and Geoinformation 10(1): 1-10. https://doi.org/10.1016/j.jag.2007.02.006

Zolekar, R.B., and Bhagat, V.S., 2015. Multi-criteria land suitability analysis for agriculture in hilly zone: Remote sensing and GIS approach. Computers and Electronics in Agriculture 118: 300-321. https://doi.org/10.1016/j.compag.2015.09.016