Crop yield estimation in 2014 for Vojvodina using methods of remote sensing

Jovanović, Dušan; Sabo, Filip; Govedarica, Miro; Marinković, Branko

doi:10.5937/ratpov51-6712

Cited by 3 publications

(2 citation statements)

References 9 publications

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“…It provides an efficient and low-cost stratification based on crop proportion derived from visual interpretation or digital classification of remote sensing data. Remote sensing makes the estimates based on ground surveys near-real time monitoring of crops, very easy derivation of vegetation (covers hilly terrain as well) and reduces the amount of field data to be collected [11,99]. Several studies have been successfully conducted using remote sensing approaches.…”

Section: Remote Sensingmentioning

confidence: 99%

“…Several studies have been successfully conducted using remote sensing approaches. Jovanović et al [99] estimated crop yields in Vojvodina province of Serbia two months before harvest using moderate resolution imaging spectroradiometer (MODIS), normalized difference vegetation index (NDVI), as an indicator of specific crop condition, and land surface temperature (LST) as an indicator of crop moisture. Doraiswamy et al [100] found their results of maize and soybean prediction within 20% standard deviation of the official estimates using the MODIS sensor.…”

Section: Remote Sensingmentioning

confidence: 99%

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Estimation of Maize (Zea mays L.) Yield Per Harvest Area: Appropriate Methods

Tandzi

Mutengwa

2019

Agronomy

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Standardization of crop yield estimation methods at various levels of farming helps to obtain accurate agricultural statistics as well as assessing the suitability of agricultural practices under various production conditions. The current paper reviews various maize yield estimation methods, taking into account available yield parameters, and it also analyses the yield gap between maize potential and attainable yield. The easiest and more reliable methods of yield estimation are based on yield parameters collected from the field. However, farmer estimation methods are cheaper and faster compared to any other method of yield estimation from farmers’ fields. This paper also elaborates on the importance of the use of more complex methods for yield estimation, such as remote sensing and crop modelling. These complex methods are more accurate and can predict yield before field harvest with less deviation from the exact harvest yield. However, they are very expensive and not efficient for small plots of land (less than 1 ha). Factors that contribute to the gap between potential and actual yield include poor implementation of agricultural policies, strict regulation of fertilizer inputs, vulnerability of smallholder cropping systems to adverse climatic conditions, occurrence of biotic and abiotic constraints, as well as unavailability of seeds and labor.

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Section: Remote Sensingmentioning

confidence: 99%

Section: Remote Sensingmentioning

confidence: 99%

Estimation of Maize (Zea mays L.) Yield Per Harvest Area: Appropriate Methods

Tandzi

Mutengwa

2019

Agronomy

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Real-time monitoring irrigation impact on crop dynamics using Earth Observation sensors in Mashi Dam Command Area, Tonk, Rajasthan, India

Bairwa,

Sharma,

Shukla

2024

Vegetation Dynamics and Crop Stress

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Object Oriented Image Analysis in Remote Sensing of Forest and Vineyard Areas

et al. 2015

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The study of vegetation cover, forests, orchards or vineyards and crops through satellite techniques is increasingly promoted as a result of facilities they offer. Since 2010 until today, it is launched over 50 satellite platforms, delivering images of the Earth's surface with different spectral, spatial and radiometric characteristics. New satellite images such as RapidEye, WorldView2 or WorldView3, with its high spatial and radiometric resolution, prevents the use of the standard image analysis techniques (such as supervised or unsupervised), and involves the use of modern methods in image analysis, such as object base image analysis. A number of methods and techniques for processing and analysis of satellite images are developed to increase the precision of the working, given the diversity of vegetation structure analysis and expected results. This study aimed to analyze the capabilities of object-oriented image analysis (OBIA) for recognition forest and vineyard areas. OBIA is automated process of object extraction by modelling of human visual system for image interpretation. The basis for classification process is object, which is created according to the set of characteristics. In object-oriented approach classification description is based on classification rules including spectral characteristics, size, shape, as well as content and texture information. Analysis is done on multispectral imagery of high and very high spatial resolution. Represented results show the usefulness of RapidEye and WorldView2 images as well as importance of classification based on OBIA. Object-oriented image analysis (OBIA) method based on satellite imagery has facilitated the recognition forest and vineyard areas with high accuracy.

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Crop yield estimation in 2014 for Vojvodina using methods of remote sensing

Cited by 3 publications

References 9 publications

Estimation of Maize (Zea mays L.) Yield Per Harvest Area: Appropriate Methods

Estimation of Maize (Zea mays L.) Yield Per Harvest Area: Appropriate Methods

Real-time monitoring irrigation impact on crop dynamics using Earth Observation sensors in Mashi Dam Command Area, Tonk, Rajasthan, India

Object Oriented Image Analysis in Remote Sensing of Forest and Vineyard Areas

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