Winter oilseed rape and winter wheat growth prediction using remote sensing methods

Domínguez, José Antonio; Kumhálová, Jitka; Novák, P.

doi:10.17221/412/2015-pse

Cited by 35 publications

(12 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Figure 3 show this relationship for the models, WR15_04, WR31_05, and WR30_06, respectively. To determine the quality of the prediction, computations applied for ex post methods were performed, using the formulae (1)(2)(3)(4). The results are given in Table 4.…”

Section: Resultsmentioning

confidence: 99%

“…The prediction of the quantity and quality of crop yields is very important in terms of planning, use as a means of production, current decision-making, transport, stockholding, and risk management [1][2][3]. The prediction of yields during the growing season is the basis for estimating production levels and expected yields at the end of the growing season, and therefore the amount of income [4].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Application of Artificial Neural Networks for Multi-Criteria Yield Prediction of Winter Rapeseed

Niedbała

2019

Sustainability

View full text Add to dashboard Cite

The aim of the work was to produce three independent, multi-criteria models for the prediction of winter rapeseed yield. Each of the models was constructed in such a way that the yield prediction can be carried out on three dates: April 15th, May 31st, and June 30th. For model building, artificial neural networks with multi-layer perceptron (MLP) topology were used, on the basis of meteorological data (temperature and precipitation) and information about mineral fertilisation. The data were collected from the years, 2008–2015, from 328 production fields located in Greater Poland, Poland. An assessment of the quality of forecasts produced based on neural models was verified by determination of forecast errors using RAE (relative approximation error), RMS (root mean square error), MAE (mean absolute error) error indicators, and MAPE (mean absolute percentage error). An important feature of the produced prediction models is the ability to realize the forecast in the current agrotechnical year on the basis of the current weather and fertiliser information. The lowest MAPE error values were obtained for the neural model WR15_04 (April 15th) based on the MLP network with structure 15:15-18-11-1:1, which reached 7.51%. Other models reached MAPE errors of 7.85% for model WR31_05 (May 31st) and 8.12% for model WR30_06 (June 30th). The performed sensitivity analysis gave information about the factors that have the greatest impact on winter rapeseed yields. The highest rank of 1 was obtained by two networks for the same independent variable in the form of the sum of precipitation within a period from September 1st to December 31st of the previous year. However, in model WR15_04, the highest rank obtained a feature in the form of a sum of molybdenum fertilization in the current year (MO_CY). The models of winter rapeseed yield produced in the work will be the basis for the construction of new forecasting tools, which may be an important element of precision agriculture and the main element of decision support systems.

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Application of Artificial Neural Networks for Multi-Criteria Yield Prediction of Winter Rapeseed

Niedbała

2019

Sustainability

View full text Add to dashboard Cite

show abstract

“…Landsat images were downloaded from US Geological Survey (USGS) storage at C1 Level-1 (Top-Of-Atmosphere reflectances in cartographic geometry) (USGS, 2018). Fast Line-of-Sight Atmospheric Analysis of the Hypercubes module in ENVI SW was used for the atmospheric correction of images; as in (Domínguez et al, 2015(Domínguez et al, , 2017. Table 2 provides the last cloud-free images available in the crop vegetation period for 2005, 2009, 2011, 2013 and 2017 crops.…”

Section: Yield Frequency Map (%)mentioning

confidence: 99%

“…The actual information concerning crop growth and cultivar adaptation to weather and site conditions during the vegetation season is provided by freely available data from several satellites: Landsat 5 (L5) with a Thematic Mapper (TM) sensor, Landsat 7 (L7) with an Enhanced Thematic Mapper Plus (ETM+) instrument (Domínguez et al, 2015(Domínguez et al, , 2017, Landsat 8 (L8) with an Operational Land Imager (OLI) and Sentinel 2 (S2) with a Multi-Spectral Instrument (MSI) (Clevers et al, 2017;Flynn et al, 2020). However, in order to obtain useful information about crop spectral reflectance, advanced knowledge is required for appropriate data processing.…”

Section: Introductionmentioning

confidence: 99%

Landsat and Sentinel-2 images as a tool for the effective estimation of winter and spring cultivar growth and yield prediction in the Czech Republic

Jelínek¹,

Kumhálová²,

Chyba³

et al. 2020

Int. Agrophys.

View full text Add to dashboard Cite

The influence of climate and topography on crop condition and yield estimates is most effectively monitored by non-invasive satellite imagery. This paper evaluates the efficiency of free-access Sentinel 2 and Landsat 5, 7 and 8 satellite images scanned by different sensors on wheat growth and yield prediction. Five winter and spring wheat cultivars were grown between 2005 and 2017 in a relatively small 11.5 ha field with a 6% slope. The normalized difference vegetation index was derived from the satellite images acquired for later growth phases of the wheat crops (Biologische Bundesanstalt, Bundessorenamt and Chemical industry 55-70) and then compared with the topography wetness index, crop yields and yield frequency maps. The results showed a better correlation of data obtained over one day (R 2 = 0.876) than data with a one-day delay (R 2 = 0.689) using the Sentinel 2 B8 band instead of the B8A band for the near-infrared part of electromagnetic spectrum in the normalized difference vegetation index calculation. K e y w o r d s: satellite sensors, agriculture, satellite imagery, wheat varieties

show abstract

“…Remote sensing technology has been continuously developed over the recent decades, and it is now widely used in crop extraction [ 5 , 6 , 7 ]. The use of satellite remote sensing technology in crop extraction can significantly reduce workload, improve efficiency, and ensure data objectivity.…”

Section: Introductionmentioning

confidence: 99%

Optimal Bands Combination Selection for Extracting Garlic Planting Area with Multi-Temporal Sentinel-2 Imagery

Han

Guan

et al. 2021

Sensors

View full text Add to dashboard Cite

Garlic is one of the main economic crops in China. Accurate and timely extraction of the garlic planting area is critical for adjusting the agricultural planting structure and implementing rural policy actions. Crop extraction methods based on remote sensing usually use spectral–temporal features. Still, for garlic extraction, most methods simply combine all multi-temporal images. There has been a lack of research on each band’s function in each multi-temporal image and optimal bands combination. To systematically explore the potential of the multi-temporal method for garlic extraction, we obtained a series of Sentinel-2 images in the whole garlic growth cycle. The importance of each band in all these images was ranked by the random forest (RF) method. According to the importance score of each band, eight different multi-temporal combination schemes were designed. The RF classifier was employed to extract garlic planting area, and the accuracy of the eight schemes was compared. The results show that (1) the Scheme VI (the top 39 bands in importance score) achieved the best accuracy of 98.65%, which is 6% higher than the optimal mono-temporal (February, wintering period) result, and (2) the red-edge band and the shortwave-infrared band played an essential role in accurate garlic extraction. This study gives inspiration in selecting the remotely sensed data source, the band, and phenology for accurately extracting garlic planting area, which could be transferred to other sites with larger areas and similar agriculture structures.

show abstract

Winter oilseed rape and winter wheat growth prediction using remote sensing methods

Cited by 35 publications

References 28 publications

Application of Artificial Neural Networks for Multi-Criteria Yield Prediction of Winter Rapeseed

Application of Artificial Neural Networks for Multi-Criteria Yield Prediction of Winter Rapeseed

Landsat and Sentinel-2 images as a tool for the effective estimation of winter and spring cultivar growth and yield prediction in the Czech Republic

Optimal Bands Combination Selection for Extracting Garlic Planting Area with Multi-Temporal Sentinel-2 Imagery

Contact Info

Product

Resources

About