ASSESSMENT OF BAYESIAN STRUCTURE OF HIDDEN MARKOV MODEL FOR REAL TIME PREDICTION OF MAIZE PHENOLOGY

Q: How can I download an article?

To download an article from SID, first log in to the site, search for the article title, and click on the 'Download Article' option.

Q: How can I download an ISI article?

To download an ISI article on SID, enter the keyword or article title in the search bar, view the relevant results, click on the desired article, and select the 'Download Article' option.

Q: How can I access the SID database?

To access the SID database, visit SID.ir, create an account, and log in to access scientific resources.

Q: Is downloading articles from SID free?

Some articles on SID are available for free, while others require payment. Details are specified on the article's page.

GHAMGHAMI M.; GHAHREMAN N.; IRANNEJAD P.; PEZESHK H.

مرکز اطلاعات علمی Scientific Information Database (SID) - Trusted Source for Research and Academic Resources

Journal Paper

Paper Information

Journal: Journal of Agricultural Meteorology Year:2017 | Volume:5 | Issue:1 Page(s): 1-14

Download Full-Text

Persian Verion

View:

828

Download:

Cites:

Information Journal Paper

Title

ASSESSMENT OF BAYESIAN STRUCTURE OF HIDDEN MARKOV MODEL FOR REAL TIME PREDICTION OF MAIZE PHENOLOGY

Author(s)

GHAMGHAMI M. | GHAHREMAN N. | IRANNEJAD P. | PEZESHK H. | Issue Writer Certificate

Keywords

PROGRESS PERCENTAGEQ1

PHENOLOGYQ2

AGDDQ1

NDVIQ1

Abstract

The Crop PROGRESS PERCENTAGE (CPP) in a given PHENOLOGY stage reflects growth status in life cycle. Generally, routine field measurements of this parameter are lacking, hence various alternative approaches have been proposed for its estimation. The statistical methods such as Bayesian approaches and hidden Markov models (HMMs) have appropriate structural skills for solving problems with variety of continuous or discrete data and can be combined with remotely sensed dataalso. The aim of this study is evaluation of hidden Markov models’ skill in real time prediction of maize PROGRESS PERCENTAGE in research field of university of Tehran located in Karaj. The HMMs follow the Bayesian structure in which, there are usually two layers; hidden and observable. Different phenological stages including Emergence to Milky were considered as the hidden layer and both Normalized Difference Vegetation Index (NDVI) and Accumulated Growth Degree-Day (AGDD) features, extracted from air temperature and LANDSAT7 ETM+images, as a vector variable of observable layer. Calibration and evaluation of the model was performed using a 9 years (2002-2010) data set of the field PHENOLOGY observations and meteorological data. According to the results, in general, for all phenological stages, the HMM was able to estimate the CPPs with average RMSE of 14%, which confirms the applicability of this approach as a suitable tool. Further studies in other climatic regions of the country are recommended for more scrutiny of phenological prediction models using remotes sensing and statistical approaches.

Cites

No record.

References

No record.

Cite

APA: Copy

GHAMGHAMI, M., GHAHREMAN, N., IRANNEJAD, P., & PEZESHK, H.. (2017). ASSESSMENT OF BAYESIAN STRUCTURE OF HIDDEN MARKOV MODEL FOR REAL TIME PREDICTION OF MAIZE PHENOLOGY. JOURNAL OF AGRICULTURAL METEOROLOGY, 5(1 ), 1-14. SID. https://sid.ir/paper/249974/en

Vancouver: Copy

GHAMGHAMI M., GHAHREMAN N., IRANNEJAD P., PEZESHK H.. ASSESSMENT OF BAYESIAN STRUCTURE OF HIDDEN MARKOV MODEL FOR REAL TIME PREDICTION OF MAIZE PHENOLOGY. JOURNAL OF AGRICULTURAL METEOROLOGY[Internet]. 2017;5(1 ):1-14. Available from: https://sid.ir/paper/249974/en

IEEE: Copy

M. GHAMGHAMI, N. GHAHREMAN, P. IRANNEJAD, and H. PEZESHK, “ASSESSMENT OF BAYESIAN STRUCTURE OF HIDDEN MARKOV MODEL FOR REAL TIME PREDICTION OF MAIZE PHENOLOGY,” JOURNAL OF AGRICULTURAL METEOROLOGY, vol. 5, no. 1 , pp. 1–14, 2017, [Online]. Available: https://sid.ir/paper/249974/en

Related Journal Papers