Evaluation of RapidEye vegetation indices for prediction of biophysical/biochemical variables of winter wheat

Kamenova, Ilina; Dimitrov, Petar; Yordanova, Rusina

doi:10.3897/arb.v30.e06

Cited by 2 publications

(2 citation statements)

References 17 publications

(7 reference statements)

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“…In 2018 was presented a conceptual framework for monitoring based on regular and systematic observation and assessment of agricultural activities, with automated procedures for extracting information from Sentinel satellites playing a key role in this approach. 11 Numerous studies highlight the benefits of using multi-channel satellite data, including for: mapping areas occupied by agricultural crops; 12,13 monitoring biophysical parameters of crops; [14][15][16][17] predicting yields; 18,19 observing ecosystem services and preserving biodiversity; [20][21][22][23] monitoring phenology. Among the many available sources of data, the EU provides access to free satellite data from the Copernicus program and gradually connects its policies with the use of this resource.…”

Section: Introductionmentioning

confidence: 99%

Crop type mapping and LAI, fAPAR, and fCover prediction in winter wheat fields with Sentinel-2 data

Kamenova

2023

Ninth International Conference on Remote Sensing and Geoinformation of the Environment (RSCy2023)

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The aim of this study is to map winter wheat fields in designated study area in Bulgaria and to analyze the spatial variation of various biophysical parameters of winter wheat crops in these fields using multispectral satellite imagery. The study uses Sentinel-2 data for image classification and to predict the Leaf Area Index (LAI), fraction of absorbed photosynthetically active radiation (fAPAR), and fraction of vegetation cover (fCover) of the crops. The study area is situated in the Danubian plain in Bulgaria, a heavily cultivated agricultural region in Europe. To distinguish winter wheat fields from other agricultural fields, classification techniques were applied using two methods, Support Vector Machines (SVM) and Random Forest (RF), to identify the winter wheat fields in different phonological phases during the growing season. Both classification methods performed with similar accuracy and showed high accuracies in classifying winter wheat using Sentinel-2 images at various phonological phases (F1 > 93%; tillering; F1 > 95%; stem elongation; F1 > 94%; anthesis). To predict the LAI, fAPAR, and fCover dynamics in the winter wheat fields, regression models were used, calibrated with vegetation indices and in situ data. Maps displaying the within-field variation of LAI, fAPAR, and fCover were created for two growth stages: tillering and stem elongation.

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Section: Introductionmentioning

confidence: 99%

Crop type mapping and LAI, fAPAR, and fCover prediction in winter wheat fields with Sentinel-2 data

Kamenova

2023

Ninth International Conference on Remote Sensing and Geoinformation of the Environment (RSCy2023)

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“…The current pilot study in Pernik Province is the first in Bulgaria, to the best knowledge of the author, that will be incorporating the Perpendicular Vegetation Index (PVI) in the investigation of forests. Up to now Kamenova et al [8] and Kamenova and Dimitrov [7] have applied PVI in Bulgaria with a focus was on agricultural areas and more specificallythe winter wheat culture. The present research aims to check the index's suitability to detect differences within the vegetation during the growing season.…”

Section: Introductionmentioning

confidence: 99%

An Investigation of the Forests of Pernik Province (Western Bulgaria) by the Use of the Perpendicular Vegetation Index (PVI)

Grigorov¹

2022

Civil and Environmental Engineering Reports

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The current research represents a pilot study for application of the Perpendicular Vegetation Index (PVI) for an area with forests in Bulgaria. It is the first of its kind when it comes to forest studying in the country to the best knowledge of the author. When it comes to soil background Landsat images and other spectral data may be used for monitoring forest territories as well. The study area is Pernik Province which is located in the western parts of Bulgaria. The main aim is to investigate the PVI for the forests of Pernik Province. The index has been calculated by the application of Landsat 8 bands. The PVI has been processed for several months of different years. The main focus is both on the beginning and the end of the growing season when there are significant changes in leaf biomass. The results are promising and show typical vegetation features in the beginning of the growing season (April), a well-developed vegetation (July) and a steadily decreasing biomass in November.

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Evaluation of RapidEye vegetation indices for prediction of biophysical/biochemical variables of winter wheat

Cited by 2 publications

References 17 publications

Crop type mapping and LAI, fAPAR, and fCover prediction in winter wheat fields with Sentinel-2 data

Crop type mapping and LAI, fAPAR, and fCover prediction in winter wheat fields with Sentinel-2 data

An Investigation of the Forests of Pernik Province (Western Bulgaria) by the Use of the Perpendicular Vegetation Index (PVI)

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