كليدواژه :
زيستتوده بقايا , سنجش ازدور , شاخصهاي پوشش گياهي , سنتينل 2 , گلستان
چكيده لاتين :
Background and Objectives:
Biomass plays an important role in providing energy and the global carbon cycle and monitoring the biomass is one of the most important issues in the agricultural sector. Using remote sensing techniques is an effective tool for estimating biomass. This reduces field studies and saves time and money. Today, management practices can be improved using remote sensing technology and vegetation indices. The suitability of the weather in Golestan Province for producing most agricultural products has made the province highly diversified in terms of crop and biomass production. For this purpose, in this study, satellite images of Landsat 8 and Sentinel 2 were used to estimate the residue biomass of crops in the watershed of the southwest of Golestan province (Mohammad Abad, Qaresoo, Zaringol, and Gharnabad).
Materials and Methods:
For this, field surveys were carried out to sample the fields at different stages of plant growth, in the winter and spring of 2016. The number of samples for wheat, barley, canola, rice, and soybean was 90, 70, 65, 67, and 80, respectively. The biomass data were also measured from field survey using four quadrates (0. 25m2) and their moisture content was determined by weighting. The Landsat 8 and Sentinel 2 images were downloaded from the USGS site in the early and middle of the growing season for mapping sattelitesatellite-based vegetation indices including, NDVI, SAVI, RVI, DVI, and RDVI. The relation between the indices and obtained biomass was evaluated using regression analysis to produce biomass maps for five crop residues.
Results:
The results showed that the NDVI index is the best indicator to estimate the wheat, barley, and rapeseed residue biomass, in April, with a determination coefficient of 0.61, 0.65, and 0.65, respectively, according to the vegetative growth peak, and for the soybean crop in September, according to the peak of vegetative growth with a determination coefficient of 0.65, and the RVI index is the best indicator for estimating rice residue biomass in August in line with peak vegetative growth in the study area.
The total amount of wheat, barley, canola, rice, and soybeans residues in the study area was estimated as 751657, 175637, 14979, 42628, and 93712 tons, respectively. The results of this study can be applied to managers and decision-makers of the agricultural sector in the province by identifying the potential for agricultural production and the amount of their residues.
Conclusion:
The results of this study showed that the residue biomass of different products can be estimated by using the vegetation indices extracted from satellite images with acceptable accuracy in the study area. In this research, the overlaying for determining the cultivation area of wheat and soybean products was 97.74% and 97.6%, respectively. The precision of this method for peak vegetation periods was higher than the early stages of plant growth. Also, instead of a vegetation index, several vegetation indices were used and the NDVI and RVI index was the best indicator for estimating the residue biomass of different products.
