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Information Journal Paper

Title

Estimating of biomass and wheat dry-farming using Landsat OLI imagery

Author(s)

 Bagheri Milad | Darvishi Bloorani Ali | Hamzeh Saeed | JELOKHANI NIARAKI MOHAMMADREZA | Issue Writer Certificate 

Pages

  589-604

Abstract

 Introduction One of the most important planning tools for timely supply of crops, especially the strategic wheat product, is to predict the performance of this product before harvest, which can be very important in planning for itself. Combining the results of observations and ground measurements with remote sensing techniques can be widely applied in all agricultural sectors and facilitate the access to precision farming. Agricultural products have always been associated with the risk of fluctuations in the climate and changes in international markets, although this risk is never completely eliminated, but it can be understood by identifying the various parameters affecting plant growth and estimating the amount of the product Before harvesting, they minimize them. The forecast of rainfed wheat yields as a strategic product, with the Earth's population reaching 7 billion now. Materials and methods Field data includes biomass and net weight of wheat produced per farm in kilograms. These data are obtained by direct field surveys during harvesting. The GPS was used to determine the total area of the land, and considering the time zone of the crop, the Landsat-8 satellite time series was used from mid-February to late May in the studied years. After performing the necessary pre-processing on the images, the images were classified using a multi-timed classification. Initially, both NDVI and LAI indexes were obtained for all images in each ENVI environment every 5 years. Finally, the phenolic curves of both indices for each plot of land were fitted for each year from the studied years, which cultivated the wheat field, and the time of each phonological step was obtained for the studied area. Results In order to evaluate the overall accuracy of the classification, the Kappa coefficient and overall accuracy for the classes defined separately were calculated using the classification error matrix. According to the phenological diagrams, the parameters of the area under the charts of both indicators were calculated for all lands. According to the phenological diagrams, the parameters of the area under the charts of both indicators were calculated for all lands. For this reason regression relations and determination coefficients (R2) between indices and wheat and biomass were created. For this reason regression relations and determination coefficients (R2) between indices and wheat and biomass were created. In this study, both indicators were affected by the multivariable regression of the product estimation, and the highest coefficient of determination was obtained for each of the indices alone. From the 5 phonological stages, the inflorescence stage with R2=0. 65 has the highest correlation coefficient. Discussion and conclusion From the obtained coefficients, we conclude that GLAI or green leaf area index (absorption) has a higher coefficient than NDVI. GLAI, which represents the main part of the photosynthesis of the plant (leaf), which is the main factor in the production process in the plant, certainly has a greater impact on the plant's production process. This leads to the preference of this index for the NDVI for estimation, but since the main goal of the paper is to obtain a multivariate regression relationship, we can do this in addition to the effect of both the desired index, the coefficient of determination and continuity For each of the indicators, we increase individually and make estimates in the region more accurately. With the involvement of both indicators in our relationship, we obtained a significant coefficient, especially for biomass with R2=0. 865, which ensures that our prediction values are close to real values and that the program On the basis of this estimate, the probability of success will be high. From the study of phonological stages with wheat yield, we also conclude that, firstly, the entire phonological stages have less regurgitation coefficients than the phonological graphs of the two vegetation indexes, which means the whole diagram of wheat growth stages relative to the phenological periods on these graphs Have more ability to estimate the yield of wheat. Two of the five phonological stages studied, the inflorescence formation stage with R2=0. 65 the highest correlation coefficient. This step in time is about the peak of the graph or the maximum value of the phonological graph.

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    APA: Copy

    Bagheri, Milad, Darvishi Bloorani, Ali, Hamzeh, Saeed, & JELOKHANI NIARAKI, MOHAMMADREZA. (2021). Estimating of biomass and wheat dry-farming using Landsat OLI imagery. PHYSICAL GEOGRAPHY RESEARCH QUARTERLY, 52(4 ), 589-604. SID. https://sid.ir/paper/1061218/en

    Vancouver: Copy

    Bagheri Milad, Darvishi Bloorani Ali, Hamzeh Saeed, JELOKHANI NIARAKI MOHAMMADREZA. Estimating of biomass and wheat dry-farming using Landsat OLI imagery. PHYSICAL GEOGRAPHY RESEARCH QUARTERLY[Internet]. 2021;52(4 ):589-604. Available from: https://sid.ir/paper/1061218/en

    IEEE: Copy

    Milad Bagheri, Ali Darvishi Bloorani, Saeed Hamzeh, and MOHAMMADREZA JELOKHANI NIARAKI, “Estimating of biomass and wheat dry-farming using Landsat OLI imagery,” PHYSICAL GEOGRAPHY RESEARCH QUARTERLY, vol. 52, no. 4 , pp. 589–604, 2021, [Online]. Available: https://sid.ir/paper/1061218/en

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