Estimating the Yield and Biomass of Maize during the Growing Season Using Satellite (Data) (A Case Study: Dasht-e-Farahan)

• Main Authors: N. Ganji khorramdel, Gh. R. khaleghi
• Format: Article
• Language: fas
• Published: Isfahan University of Technology 2020-04-01
• Series: Tulīd va Farāvarī-i Maḥṣūlāt-i Zirā̒ī va Bāghī
• Subjects: remote sensing; suitable vegetation index; crop yield; biomass; maize
• Online Access: http://jcpp.iut.ac.ir/article-1-2819-en.html

Nowadays, the satellite data and remote sensing technologies are widely known as efficient tools for the inspection, identification and management of land resources and precision agriculture in most countries. Satellite information could be used in supplying basic and updated information in the estimation of vegetation cover map, irrigated land area and some biological indices of the major agricultural crops. In this study, the biomass and production of maize were estimated through the application of five common vegetation indices of NDVI, DVI, NIR, PD321, and SAVI, using the Landsat 8 satellite data. The study area was located in Dasht-e-Farahan, Markazi province, Iran, and field sampling was carried out in five farms and five dates corresponded to times of satellite passes over the area. The highest correlation coefficient of the first to fifth observed dates was obtained for NDVI (0.73), PD321 (0.58), NIR (0.67), DVI (0.62), and SAVI (0.73) indices, respectively. In the early season, when vegetation cover was low, the biomass was well estimated by the NDVI index with the correlation coefficient of 0.75. However, in the late season, when the vegetation was high, the SAVI with the high correlation coefficient of 0.73 could estimate biomass better than other indices estimations. The results, therefore, indicated the satisfactory accuracy of the applied method; in fact, the accuracy of the data in this method was higher in the middle growth period, as compared to the initial stage ones. Therefore, it is recommended to use a suitable vegetation index for each growing period, rather than using a single vegetation index in obtaining satellite data for the total of the growing season.