Biomass estimation of wheat fields using remote sensing plant indices in Bandar-e-Turkmen county

Document Type : scientific research article

Authors

1 Ph.D. Graduate of Agronomy, Dept. of Agronomy, Faculty of Plant Production, Gorgan University of Agricultural Sciences and Natural Resources, Gorgan, Iran

2 Corresponding Author, Professor, Dept. of Horticulture, Faculty of Plant Production, Gorgan University of Agricultural Sciences and Natural Resources, Gorgan, Iran

3 Professor, Dept. of Agronomy, Faculty of Plant Production, Gorgan University of Agricultural Sciences and Natural Resources, Gorgan, Iran

4 Professor, Dept. of Agronomy, Faculty of Plant Production, Gorgan University of Agricultural Sciences and Natural Resources, Gorgan, Iran.

5 Professor, Dept. of Agrotechnology, Faculty of Agriculture, Ferdowsi University of Mashhad, Mashhad, Iran.

Abstract

Background and objectives: Estimating crop acreage and yield at the global level is one of the most critical issues that policy makers and decision makers need to assess annual crop productivity and food supply. Nowadays, satellite remote sensing (RS) and geographic information system (GIS) can make it possible to continuously estimate and monitor these parameters of crop production in large geographical areas and in this way also examine the health of fields.

Materials and methods: In order to estimate the biomass of wheat fields using remote sensing plant indices, 59 wheat fields were selected in the agricultural year of 2019-2020 with random and uniform distribution in the agricultural lands of Bandar Turkmen county. On April 25, 2019, coinciding with the peak of vegetative growth of wheat, plant samples were collected from the fields using a 0.25 square meter box and their dry weight was weighed. In this research, Sentinel-2 satellite images related to the nearest sampling time of April 31 were used to calculate NDVI, SAVI, DVI and RVI plant indices. Next, the regression relationship between the measured biomass values and plant index values was investigated and analyzed.

Results: Based on the comparison results of the studied plant index, RVI index was recognized as the best plant index. This index had the highest explanation coefficient (0.885) and correlation coefficient (0.941) and the lowest RMSE values (32.21) and coefficient of variation (5.1) compared to other indices. Therefore, this index was used to establish a regression relationship with the amount of wheat plant biomass. Then, using this relationship, the amount of plant biomass obtained from the satellite image was estimated. Based on the results, the strong regression relationship between the measured biomass and the estimated biomass indicates the high efficiency of the used satellite images and telemetry indicators in the estimation of plant biomass. The RVI map in the studied agricultural lands showed the lowest level of this index in the west and northwest of the county equal to 0.44. High soil salinity and high water table in these areas can be a reason for less vegetation in these areas and as a result the low value of RVI index in these places. This map showed higher values of this index (27.46) in the central, eastern and southeastern regions of the county. The high values of this index indicate the dense vegetation cover in these areas, which can be attributed to the high percentage of carbon and organic matter in the soil, the abundance and appropriate distribution of precipitation, optimal fields management, lower soil salinity, proper soil nutrition and other factors.

Conclusion: In general, it can be concluded that the higher RVI plant index and the subsequent higher biomass production can indicate the favorable condition of wheat health and crop growth in Bandar-e-Turkmen fields.

Keywords

Main Subjects

References

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Journal of Plant Production Research
Volume 32, Issue 1
June 2025
Pages 1-16

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