scholarly journals Analysis and comparison of vegetation indices of winter wheat crop areas, calculated on the basis of Sentinel-2 and fieldspec spectroradiometer data

2017 ◽  
pp. 37-46 ◽  
Author(s):  
Vadim Lyalko ◽  
Oleksii Sakhatsky ◽  
Galina Zholobak ◽  
Oksana Sybirtseva ◽  
Stanislav Dugin ◽  
...  
Keyword(s):  
Winter Wheat ◽  
Vegetation Indices ◽  
Correlation Coefficients ◽  
Wheat Crop ◽  
Growth Stages ◽  
Double Ratio ◽  
Winter Wheat Crop ◽  
Chlorophyll Index ◽  
Spectral Channels ◽  
Sentinel 2

Ten vegetation indices (VIs) were analyzed, which were calculated simultaneously based on Sentine-l2 data and on results of ground spectrometric survey by ASD FieldSpec® 3FR for the identically geographical sites of the production crops of winter wheat of two cultivars Bohdana and Skagen. The values of the most studied VIs on Sentinel-2 satellite data are similar by quantity to the same indices, calculated on the narrow spectral channels of ASD FieldSpec® 3FR, except for DRICI (Double ratio index for chlorophyll index) and СІ green (ratio green chlorophyll index), the satellite values of which are much lower than those received by spectroradiometer. It was shown that the differences of VIs received by Sentinel-2 and ASD FieldSpec® 3FR depend on the growth stages of winter wheat: during vegetation season the correlation coefficients between them increase for crop areas of both studied cultivars.

scholarly journals Crop Water Content of Winter Wheat Revealed with Sentinel-1 and Sentinel-2 Imagery

Sensors ◽  
2019 ◽  
Vol 19 (18) ◽  
pp. 4013 ◽  
Author(s):  
Dong Han ◽  
Shuaibing Liu ◽  
Ying Du ◽  
Xinrui Xie ◽  
Lingling Fan ◽  
...  
Keyword(s):  
Winter Wheat ◽  
Water Content ◽  
Vegetation Indices ◽  
Spectral Feature ◽  
Wheat Crop ◽  
Crop Water ◽  
Inversion Model ◽  
Model Based ◽  
Winter Wheat Crop ◽  
Sentinel 2

This study aims to efficiently estimate the crop water content of winter wheat using high spatial and temporal resolution satellite-based imagery. Synthetic-aperture radar (SAR) data collected by the Sentinel-1 satellite and optical imagery from the Sentinel-2 satellite was used to create inversion models for winter wheat crop water content, respectively. In the Sentinel-1 approach, several enhanced radar indices were constructed by Sentinel-1 backscatter coefficient of imagery, and selected the one that was most sensitive to soil water content as the input parameter of a water cloud model. Finally, a water content inversion model for winter wheat crop was established. In the Sentinel-2 approach, the gray relational analysis was used for several optical vegetation indices constructed by Sentinel-2 spectral feature of imagery, and three vegetation indices were selected for multiple linear regression modeling to retrieve the wheat crop water content. 58 ground samples were utilized in modeling and verification. The water content inversion model based on Sentinel-2 optical images exhibited higher verification accuracy (R = 0.632, RMSE = 0.021 and nRMSE = 19.65%) than the inversion model based on Sentinel-1 SAR (R = 0.433, RMSE = 0.026 and nRMSE = 21.24%). This study provides a reference for estimating the water content of wheat crops using data from the Sentinel series of satellites.

scholarly journals Comparison of Proximal and Remote Sensing for the Diagnosis of Crop Status in Site-Specific Crop Management

Sensors ◽  
2021 ◽  
Vol 22 (1) ◽  
pp. 19
Author(s):  
Jiří Mezera ◽  
Vojtěch Lukas ◽  
Igor Horniaček ◽  
Vladimír Smutný ◽  
Jakub Elbl
Keyword(s):  
Remote Sensing ◽  
Winter Wheat ◽  
Vegetation Indices ◽  
Wheat Yield ◽  
Nitrogen Fertilizers ◽  
Wheat Crop ◽  
Significant Difference ◽  
Winter Wheat Crop ◽  
The Individual ◽  
Sentinel 2

The presented paper deals with the issue of selecting a suitable system for monitoring the winter wheat crop in order to determine its condition as a basis for variable applications of nitrogen fertilizers. In a four-year (2017–2020) field experiment, 1400 ha of winter wheat crop were monitored using the ISARIA on-the-go system and remote sensing using Sentinel-2 multispectral satellite images. The results of spectral measurements of ISARIA vegetation indices (IRMI, IBI) were statistically compared with the values of selected vegetation indices obtained from Sentinel-2 (EVI, GNDVI, NDMI, NDRE, NDVI and NRERI) in order to determine potential hips. Positive correlations were found between the vegetation indices determined by the ISARIA system and indices obtained by multispectral images from Sentinel-2 satellites. The correlations were medium to strong (r = 0.51–0.89). Therefore, it can be stated that both technologies were able to capture a similar trend in the development of vegetation. Furthermore, the influence of climatic conditions on the vegetation indices was analyzed in individual years of the experiment. The values of vegetation indices show significant differences between the individual years. The results of vegetation indices obtained by the analysis of spectral images from Sentinel-2 satellites varied the most. The values of winter wheat yield varied between the individual years. Yield was the highest in 2017 (7.83 t/ha), while the lowest was recorded in 2020 (6.96 t/ha). There was no statistically significant difference between 2018 (7.27 t/ha) and 2019 (7.44 t/ha).

scholarly journals Influence of Soil Background on Spectral Reflectance of Winter Wheat Crop Canopy

2019 ◽  
Vol 11 (16) ◽  
pp. 1932 ◽  
Author(s):  
Elena Prudnikova ◽  
Igor Savin ◽  
Gretelerika Vindeker ◽  
Praskovia Grubina ◽  
Ekaterina Shishkonakova ◽  
...  
Keyword(s):  
Winter Wheat ◽  
Soil Type ◽  
Spectral Reflectance ◽  
Vegetation Index ◽  
Vegetation Indices ◽  
Soil Surface ◽  
Wheat Crop ◽  
Crop Canopy ◽  
Winter Wheat Crop ◽  
Type Factor

The spectral reflectance of crop canopy is a spectral mixture, which includes soil background as one of the components. However, as soil is characterized by substantial spatial variability and temporal dynamics, its contribution to the spectral reflectance of crops will also vary. The aim of the research was to determine the impact of soil background on spectral reflectance of crop canopy in visible and near-infrared parts of the spectrum at different stages of crop development and how the soil type factor and the dynamics of soil surface affect vegetation indices calculated for crop assessment. The study was conducted on three test plots with winter wheat located in the Tula region of Russia and occupied by three contrasting types of soil. During field trips, information was collected on the spectral reflectance of winter wheat crop canopy, winter wheat leaves, weeds and open soil surface for three phenological phases (tillering, shooting stage, milky ripeness). The assessment of the soil contribution to the spectral reflectance of winter wheat crop canopy was based on a linear spectral mixture model constructed from field data. This showed that the soil background effect is most pronounced in the regions of 350–500 nm and 620–690 nm. In the shooting stage, the contribution of the soil prevails in the 620–690 nm range of the spectrum and the phase of milky ripeness in the region of 350–500 nm. The minimum contribution at all stages of winter wheat development was observed at wavelengths longer than 750 nm. The degree of soil influence varies with soil type. Analysis of variance showed that normalized difference vegetation index (NDVI) was least affected by soil type factor, the influence of which was about 30%–50%, depending on the stage of winter wheat development. The influence of soil type on soil-adjusted vegetation index (SAVI) and enhanced vegetation index (EVI2) was approximately equal and varied from 60% (shooting phase) to 80% (tillering phase). According to the discriminant analysis, the ability of vegetation indices calculated for winter wheat crop canopy to distinguish between winter wheat crops growing on different soil types changed from the classification accuracy of 94.1% (EVI2) in the tillering stage to 75% (EVI2 and SAVI) in the shooting stage to 82.6% in the milky ripeness stage (EVI2, SAVI, NDVI). The range of the sensitivity of the vegetation indices to the soil background depended on soil type. The indices showed the greatest sensitivity on gray forest soil when the wheat was in the phase of milky ripeness, and on leached chernozem when the wheat was in the tillering phase. The observed patterns can be used to develop vegetation indices, invariant to second-type soil variations caused by soil type factor, which can be applied for the remote assessment of the state of winter wheat crops.

scholarly journals Analysis of dynamics for 15 vegetation indices based on Sentinel-2A image data for the test sites of winter wheat crop different on the state from each other within the forest-steep zone in Ukraine

2018 ◽  
pp. 32-39
Author(s):  
Galina Zholobak ◽  
Oksana Sybirtseva ◽  
Mariana Vakolyuk ◽  
Inna Romanciuc
Keyword(s):  
Winter Wheat ◽  
Vegetation Indices ◽  
Image Data ◽  
Early Summer ◽  
Wheat Crop ◽  
Winter Wheat Cultivars ◽  
Winter Wheat Crop ◽  
Analysis Of Dynamics ◽  
Sentinel 2A ◽  
Reflecting Surface

Dynamics of 15 vegetation indices estimated from the Sentinel-2A images within two test sites with the area of 1 ha for the production crops of two winter wheat cultivars (Bohdana and Skagen) are analyzed for winter dormancy and spring-early summer in 2016. The decrease of total nitrogen content in dry matter of the plant organs, which are formed the reflecting surface of the vegetation cover from the booting stage to milk one is consistent with the behavior of the Green NDVI (740, 560) for the both test sites of winter wheat cover. Dynamics of the other 14 indices have been analyzed under the conditions of the deterioration of phytosanitary situation for the winter wheat crop of Bohdana cultivar.

scholarly journals Within-Field Relationships between Satellite-Derived Vegetation Indices, Grain Yield and Spike Number of Winter Wheat and Triticale

Agronomy ◽  
2020 ◽  
Vol 10 (11) ◽  
pp. 1842
Author(s):  
Ewa Panek ◽  
Dariusz Gozdowski ◽  
Michał Stępień ◽  
Stanisław Samborski ◽  
Dominik Ruciński ◽  
...  
Keyword(s):  
Winter Wheat ◽  
Grain Yield ◽  
Precision Agriculture ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Vegetation Indices ◽  
Correlation Coefficients ◽  
Soil Nutrient ◽  
Growth Stages ◽  
Plant Growth Stages

The aims of this study were to: (i) evaluate the relationships between vegetation indices (VIs) derived from Sentinel-2 imagery and grain yield (GY) and the number of spikes per square meter (SN) of winter wheat and triticale; (ii) determine the dates and plant growth stages when the above relationships were the strongest at individual field scale, thus allowing for accurate yield prediction. Observations of GY and SN were performed at harvest on six fields (three locations in two seasons: 2017 and 2018) in three regions of Poland, i.e., northeastern (A—Brożówka), central (B—Zdziechów) and southeastern Poland (C—Kryłów). Vegetation indices (Normalized Difference Vegetation Index (NDVI), Soil-Adjusted Vegetation Index (SAVI), modified SAVI (mSAVI), modified SAVI 2 (mSAVI2), Infrared Percentage Vegetation Index (IPVI), Global Environmental Monitoring Index (GEMI), and Ratio Vegetation Index (RVI)) calculated for sampling points from mid-March until mid-July, covering within-field soil and topographical variability, were included in the analysis. Depending on the location, the highest correlation coefficients (of about 0.6–0.9) for most of VIs with GY and SN were obtained about 4–6 weeks before harvest (from the beginning of shooting to milk maturity). Therefore, satellite-derived VIs are useful for the prediction of within-field cereal GY as well as SN variability. Information on GY, predicted together with the results for soil nutrient availability, is the basis for the formulation of variable fertilize rates in precision agriculture. All examined VIs were similarly correlated with GY and SN via the commonly used NDVI. The increase in NDVI by 0.1 unit was related to an average increase in GY by about 2 t ha−1.

Volunteer Barley (Hordeum vulgare) Control in Winter Wheat (Triticum aestivum) with MON 37500

1999 ◽  
Vol 13 (1) ◽  
pp. 88-93 ◽  
Author(s):  
Sandra L. Shinn ◽  
Donald C. Thill ◽  
William J. Price
Keyword(s):  
Winter Wheat ◽  
Spring Barley ◽  
Wheat Crop ◽  
Growth Stages ◽  
Visible Injury ◽  
Yield And Quality ◽  
Control Volunteer ◽  
Winter Wheat Crop ◽  
Grain Yield And Quality ◽  
Biomass Reduction

Spring barley often is grown in rotation with winter wheat, and sometimes barley can overwinter in the subsequent winter wheat crop reducing grain yield and quality. Studies were established during 1996 and 1997 in winter wheat fields in southeastern Washington and near Moscow, ID, respectively, to evaluate control of ‘Steptoe’ volunteer barley with MON 37500, diclofop, and fenoxaprop/2,4-D/MCPA. Herbicides were applied to volunteer barley at two growth stages: two leaves to four tillers and more than four tillers with stems beginning to elongate. MON 37500 at 0.018, 0.026, and 0.035 kg ai/ha visibly controlled volunteer barley 83% or more at both application times. Diclofop at 1.12 kg ai/ha did not control volunteer barley, whereas fenoxaprop/2,4-D/MCPA at 0.66 kg ai/ha controlled volunteer barley 64 to 97% in 1996, but only 0 to 23% in 1997. In 1996 and 1997, volunteer barley density was reduced 80 to 99% in MON 37500-treated plots compared to the untreated control plots. Wheat grain grade was #1 for all MON 37500 treatments compared to grade #4 in 1996 and #3 in 1997 in the untreated plots. Grain price was reduced by dockage (barley kernels) for MON 37500-treated wheat $0 to $3.12/metric ton (MT), whereas price was reduced $23 to $26/MT for grain from untreated plots. In greenhouse studies, visible injury and height and biomass reduction varied among the 36 barley varieties treated with MON 37500.

scholarly journals Winter Wheat Mapping Based on Sentinel-2 Data in Heterogeneous Planting Conditions

2019 ◽  
Vol 11 (22) ◽  
pp. 2647
Author(s):  
Dongyan Zhang ◽  
Shengmei Fang ◽  
Bao She ◽  
Huihui Zhang ◽  
Ning Jin ◽  
...  
Keyword(s):  
Winter Wheat ◽  
Land Surface ◽  
Vegetation Indices ◽  
Percentage Error ◽  
Growth Stages ◽  
Distance Method ◽  
Water Index ◽  
Spectral Bands ◽  
Heading Stage ◽  
Sentinel 2

Monitoring and mapping the spatial distribution of winter wheat accurately is important for crop management, damage assessment and yield prediction. In this study, northern and central Anhui province were selected as study areas, and Sentinel-2 imagery was employed to map winter wheat distribution and the results were verified with Planet imagery in the 2017–2018 growing season. The Sentinel-2 imagery at the heading stage was identified as the optimum period for winter wheat area extraction after analyzing the images from different growth stages using the Jeffries–Matusita distance method. Therefore, ten spectral bands, seven vegetation indices (VI), water index and building index generated from the image at the heading stage were used to classify winter wheat areas by a random forest (RF) algorithm. The result showed that the accuracy was from 93% to 97%, with a Kappa above 0.82 and a percentage error lower than 5% in northern Anhui, and an accuracy of about 80% with Kappa ranging from 0.70 to 0.78 and a percentage error of about 20% in central Anhui. Northern Anhui has a large planting scale of winter wheat and flat terrain while central Anhui grows relatively small winter wheat areas and a high degree of surface fragmentation, which makes the extraction effect in central Anhui inferior to that in northern Anhui. Further, an optimum subset data was obtained from VIs, water index, building index and spectral bands using an RF algorithm. The result of using the optimum subset data showed a high accuracy of classification with a great advantage in data volume and processing time. This study provides a perspective for winter wheat mapping under various climatic and complicated land surface conditions and is of great significance for crop monitoring and agricultural decision-making.

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