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 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 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 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 The influence of different nitrogen forms and application time on winter wheat

2017 ◽  
Vol 24 (3) ◽  
Author(s):  
Vita Smalstienė ◽  
Irena Pranckietienė ◽  
Rūta Dromantienė ◽  
Gvidas Šidlauskas
Keyword(s):  
Winter Wheat ◽  
Ammonium Nitrate ◽  
Triticum Aestivum L ◽  
Nitrogen Fertilizers ◽  
Wheat Crop ◽  
Nitrogen Form ◽  
Amide Nitrogen ◽  
Nitrogen Forms ◽  
Ammonium N ◽  
Winter Wheat Crop

The research was carried out at the Experimental Station of Aleksandras Stulginskis University during 2015–2016 on medium textured loamy carbonaceous leached soil – Cal(ca)ri-Epihypogleyic Luvisols. The soil of the experimental field was the following: pHKCl 6.8–7.2; phosphorus (P2O5) – 423– 429 mg kg–1; potassium (K2O) – 157–163 mg kg–1; humus – 2.47–2.82%. The researchers explored the winter wheat crop (Triticum aestivum L.) variety ‘Skagen’ fertilized with amide (N-NH2), ammonium (N-NH4) and nitrate (N-NO3) forms of nitrogen fertilizers in different tillering stages (BBCH 21–29). 7 days after winter wheat was fertilized, the level of mineral nitrogen in the soil was on average 23.9% higher using ammonium–nitrate nitrogen form fertilizers than using amide nitrogen form ones. The index of chlorophyll and the area of leafs were essentially higher when ammonium– nitrate and amide forms of nitrogen fertilizers were used. The biggest effect on the index of chlorophyll and the area of leafs was achieved 16 days after the start of vegetation when plants were fertilized with ammonium–nitrate fertilizers. Plants fertilized with ammonium–nitrate fertilizers gave the biggest yield 4 days after the start of vegetation. Data of the experiment showed strong and statistically reliable bonds of the correlation between the grain yield and the time of fertilization with nitrogen fertilizers (ήamide nitrogen fertilizers = 0.850* and ήammonium–nitrate fertilizers = 0.878*).

scholarly journals Assessing Wheat Yellow Rust Disease through Hyperspectral Remote Sensing

2014 ◽  
Vol XL-8 ◽  
pp. 1413-1416 ◽  
Author(s):  
G. Krishna ◽  
R. N. Sahoo ◽  
S. Pargal ◽  
V. K. Gupta ◽  
P. Sinha ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Root Mean Square Error ◽  
Winter Wheat ◽  
Cross Validation ◽  
Yellow Rust ◽  
Wheat Crop ◽  
Mean Square ◽  
Rust Disease ◽  
Winter Wheat Crop ◽  
Mlr Model

The potential of hyperspectral reflectance data was explored to assess severity of yellow rust disease (Biotroph Pucciniastriiformis) of winter wheat in the present study. The hyperspectral remote sensing data was collected for winter wheat (Triticum aestivum L.) cropat different levels of disease infestation using field spectroradiometer over the spectral range of 350 to 2500 nm. The partial least squares (PLS) and multiple linear (MLR) regression techniques were used to identify suitable bands and develop spectral models for assessing severity of yellow rust disease in winter wheat crop. The PLS model based on the full spectral range and n = 36, yielded a coefficient of determination (R2) of 0.96, a standard error of cross validation (SECV) of 12.74 and a root mean square error of cross validation (RMSECV) of 12.41. The validation analysis of this PLS model yielded r2 as 0.93 with a SEP (Standard Error of Prediction) of 7.80 and a RMSEP (Root Mean Square Error of prediction) of 7.46. The loading weights of latent variables from PLS model were used to identify sensitive wavelengths. To assess their suitability multiple linear regression (MLR) model was applied on these wavelengths which resulted in a MLR model with three identified wavelength bands (428 nm, 672 nm and 1399 nm). MLR model yielded acceptable results in the form of r2 as 0.89 for calibration and 0.90 for validation with SEP of 3.90 and RMSEP of 3.70. The result showed that the developed model had a great potential for precise delineation and detection of yellow rust disease in winter wheat crop.

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 The affectivity using of the winter wheat by detailed methoda of the structure harvest

2019 ◽  
Vol 2019 (2) ◽  
pp. 6-14
Author(s):  
G.F. Оlkhovskyi ◽  
М.А. Bobro ◽  
О.F. Chechui
Keyword(s):  
Winter Wheat ◽  
Key Words ◽  
Wheat Yield ◽  
Wheat Crop ◽  
Yield Structure ◽  
Winter Wheat Yield ◽  
Individual Features ◽  
The Individual ◽  
The Relationship

The most difficult but most informative method of determining the structure of winter wheat yield with the use of large bunches of samples is presented. The role of the stem in the formation of allthe elements of winter wheat yield structure is determined. The advantage of our method is that it allows to get deeper information about the structure of the wheat crop, as it reveals the relationship between the individual elements of the crop structure and shows the amplitude of fluctuations in individual features of thewheat crop structure. Key words: winter wheat, yield structure, stem, weight and number of grains.

scholarly journals Comparison of biophysical and satellite predictors for wheat yield forecasting in Ukraine

2015 ◽  
Vol XL-7/W3 ◽  
pp. 39-44 ◽  
Author(s):  
A. Kolotii ◽  
N. Kussul ◽  
A. Shelestov ◽  
S. Skakun ◽  
B. Yailymov ◽  
...  
Keyword(s):  
Winter Wheat ◽  
Crop Yield ◽  
Wheat Yield ◽  
Biophysical Model ◽  
Wheat Crop ◽  
Biophysical Parameters ◽  
Model Based ◽  
Winter Wheat Crop ◽  
Yield Forecasting ◽  
Crop Yield Forecasting

Winter wheat crop yield forecasting at national, regional and local scales is an extremely important task. This paper aims at assessing the efficiency (in terms of prediction error minimization) of satellite and biophysical model based predictors assimilation into winter wheat crop yield forecasting models at different scales (region, county and field) for one of the regions in central part of Ukraine. Vegetation index NDVI, as well as different biophysical parameters (LAI and fAPAR) derived from satellite data and WOFOST crop growth model are considered as predictors of winter wheat crop yield forecasting model. Due to very short time series of reliable statistics (since 2000) we consider single factor linear regression. It is shown that biophysical parameters (fAPAR and LAI) are more preferable to be used as predictors in crop yield forecasting regression models at each scale. Correspondent models possess much better statistical properties and are more reliable than NDVI based model. The most accurate result in current study has been obtained for LAI values derived from SPOT-VGT (at 1 km resolution) on county level. At field level, a regression model based on satellite derived LAI significantly outperforms the one based on LAI simulated with WOFOST.

scholarly journals Winter Wheat Response to Preplant Applications of Aminocyclopyrachlor

2011 ◽  
Vol 25 (1) ◽  
pp. 51-57 ◽  
Author(s):  
Andrew R. Kniss ◽  
Drew J. Lyon
Keyword(s):  
Winter Wheat ◽  
Yield Loss ◽  
Wheat Yield ◽  
Field Studies ◽  
Wheat Crop ◽  
Yield Losses ◽  
Fallow Period ◽  
Winter Wheat Yield ◽  
Winter Wheat Crop ◽  
Preceding Winter

Field studies were conducted in Wyoming and Nebraska in 2007 through 2009 to evaluate winter wheat response to aminocyclopyrachlor. Aminocyclopyrachlor was applied at rates between 15 and 120 g ai ha−1 6, 4, and 2 mo before winter wheat planting (MBP). Redroot pigweed control was 90% with aminocyclopyrachlor rates of 111 and 50 g ha−1 when applied 4 or 2 MBP. Aminocyclopyrachlor at 37 g ha−1 controlled Russian thistle 90% when applied 6 MBP. At Sidney, NE, winter wheat yield loss was > 10% at all aminocyclopyrachlor rates when applied 2 or 4 MBP, and at all rates > 15 g ha−1 when applied 6 MBP. At Lingle, WY, > 40% winter wheat yield loss was observed at all rates when averaged over application timings. Although the maturing wheat plants looked normal, few seed were produced in the aminocyclopyrachlor treatments, and therefore preharvest wheat injury ratings of only 5% corresponded to yield losses ranging from 23 to 90%, depending on location. The high potential for winter wheat crop injury will almost certainly preclude the use of aminocyclopyrachlor in the fallow period immediately preceding winter wheat.

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