scholarly journals Possibility of determining the nitrogen content in winter wheat plants during the earing phase using remote sensing data

2020 ◽  
Vol 26 (3) ◽  
pp. 199-209
Author(s):  
Fedor Eroshenko ◽  
Irina Storchak ◽  
Irina Engovatova ◽  
Andrey Likhovid
Keyword(s):  
Remote Sensing ◽  
Winter Wheat ◽  
Nitrogen Content ◽  
Growth And Development ◽  
Vegetation Index ◽  
Local Level ◽  
Remote Sensing Data ◽  
Reflection Index ◽  
Sensing Data ◽  
Agricultural Enterprise

The study examined the possibility of using remote sensing Data (RED, NIR, NDVI) for monitoring winter wheat crops in production conditions for nitrogen content in plants. This work is divided into two stages: 1) analysis of the correlation between NDVI indicators and nitrogen content on production crops of the North-Caucasian FNAC; 2) comparative analysis of the correlation between nitrogen content and remote sensing data in the conditions of the “Rodina” agricultural enterprise in the Shpakovsky district of the Stavropol territory. Selection of plant samples (sheaf material) was carried out according to the generally accepted method. Repeatability — 4-fold. The chemical composition of plant organs was determined using the method of V.T. Kurkaev and co-authors, and the chlorophyll content was determined by Y.I. Milaeva and N.P. Primak. We used the earth remote sensing data provided by the Terra satellite and obtained by the Modis scanning Spectroradiometer. At the first stage, the relationships between the nitrogen content in winter wheat plants and the values of the normalized difference vegetation index (NDVI) were studied. At the early stages of growth and development of winter wheat plants, high correlation coefficients between these indicators were obtained. Thus, the correlation coefficient on average for the fields in 2012 was equal to -0.89, and in 2013 and 2014 — -0.82. In later phases of growth and development of winter wheat plants, this relationship was not observed. At the second stage, it was found that it is advisable to use the red reflection index to assess the nitrogen content at the local level (a separate agricultural enterprise) in the earing phase. In this case, there is a stable inverse correlation — the average for three years of research was -0.71. When other remote sensing indicators (NDVI and NIR) are used in the analysis, the links are either absent or less apparent.

scholarly journals Assessment of Winter Wheat Plant Development during the Seeding and Tillering Stages According to the Earth Remote Sensing Data

2021 ◽  
Vol 31 (1) ◽  
pp. 21-36
Author(s):  
Irina G. Storchak ◽  
Fedor V. Eroshenko ◽  
Lusine R. Oganyan ◽  
Elena O. Shestakova ◽  
Anastasiya A. Kalashnikova
Keyword(s):  
Remote Sensing ◽  
Winter Wheat ◽  
Vegetation Index ◽  
Initial Period ◽  
Remote Sensing Data ◽  
The State ◽  
Spectral Brightness ◽  
Earth Remote Sensing ◽  
Sensing Data ◽  
The Earth

Introduction. The importance of controlling the organogenesis stages is that it provides the opportunity to create favorable conditions during the development of certain elements of the yield structure by caring crops and influence the grain quality. The objective of the work is to define a connection between the Earth remote sensing data and the state of winter wheat plants in the initial period of their growth and development. Materials and Methods. The object of the study was the winter wheat plantings. The wheat varieties “Odisseya”, “Olympus”, “Niva Stavropolya”, “Victoria 11”, “Nastya” and “Firuza 40” were sown by plot in the experimental field. The industrial crops of winter wheat were studied to assess the state of the plants in the tillering stage in 2012–2013, 2013–2014 and 2015–2016 agricultural years. The Earth remote sensing data were obtained using the “VEGA” service of FBSI “Space Research Institute of the Russian Academy of Sciences”. Results. The obtained function of the dependence of NDVI of winter wheat crops on the height and development stage of plants is a polynomial of the third degree and is characterized by high accuracy (Rcorr = 0.98). The analysis of the NDVI, RED and NIR data of fields on dates close to the dates of collecting plant samples showed a considerable degree of relationship between the vegetation index NDVI and height of winter wheat plants. In 2013, the correlation coefficient was 0.60, in 2014 – 0.66, in 2016 – 0.80, and in 2013–2016 on average about 0.85. Discussion and Conclusion. The studies have shown that the Earth remote sensing data can be used to assess the state and degree of the development of winter wheat crops during the seeding and tillering stages, including in the production conditions. To improve the accuracy of the assessment, it is better to use the spectral brightness values in the infrared region of the spectrum.

scholarly journals Features of the dynamics of the vegetation index of ndvi in different soil-climatic zones of the Stavropol territory

2019 ◽  
pp. 12-18
Author(s):  
И СТОРЧАК ◽  
I. STORCHAK ◽  
Ф. Ерошенко ◽  
F. Eroshenko ◽  
Е ШЕСТАКОВА ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Winter Wheat ◽  
Correlation Coefficient ◽  
Vegetation Index ◽  
Wheat Yield ◽  
Remote Sensing Data ◽  
Climatic Zones ◽  
Earth Remote Sensing ◽  
Sensing Data ◽  
The Relationship

Abstract. Currently, in the agricultural sector, research results are being actively used to predict crop yields using Earth remote sensing data. It is known that the resulting regression models depend on soil and climatic conditions of cultivation. In order to determine the degree of development and condition of plants, you can use the vegetation index NDVI. The advantage of this method is the objectivity of the estimates, and the ability to apply them to large areas. Unfortunately, studies of the influence of soil-climatic zones (CLC) of cultivation on the relationship between the yield of winter wheat and Earth remote sensing data are practically not conducted. The aim of the work was to identify the influence of the conditions of various soil-climatic zones of the Stavropol Territory on the features of the connections of Earth remote sensing data with the productivity of winter wheat crops. The studies were carried out on the basis of the FSUE „North Caucasus Federal Scientific Agrarian Center“. The objects of research were crops of winter wheat of the Stavropol Territory. In the course of work, the statistical data of the Ministry of Agriculture of the Stavropol Territory was used. The NDVI vegetation index was obtained using the VEGA service of the Space Research Institute of the Russian Academy of Sciences. The relationship between NDVI and winter wheat yield for the soil and climatic zones of the Stavropol Territory has been established. The resulting models have a high degree of confidence (the coefficient of approximation is within 0.5–90.82, the correlation coefficient is 0.77–0.90). The regression model of the connection of the average NDVI for the vegetative-generative period and the grain yield of the Stavropol Territory, built using data from soil-climatic zones, has a fairly high accuracy (correlation coefficient 0.82, approximation coefficient 0.72). The use of Earth remote sensing data calculated by soil and climatic zones significantly increases the correlation between the NDVI vegetation index and the productivity of winter wheat sowing. This makes it possible to more accurately predict the yield for the entire Stavropol Territory.

scholarly journals Analysis of Influencing Factors on Winter Wheat Yield Estimations Based on a Multisource Remote Sensing Data Fusion

2021 ◽  
Vol 37 (5) ◽  
pp. 991-1003
Author(s):  
Yan Li ◽  
Yan Zhao Ren ◽  
Wan Lin Gao ◽  
Sha Tao ◽  
Jing Dun Jia ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Winter Wheat ◽  
Spatial Resolution ◽  
Vegetation Index ◽  
Time Window ◽  
Wheat Yield ◽  
Remote Sensing Data ◽  
Sensing Data ◽  
Winter Wheat Yield ◽  
Window Selection

HighlightsThe potential of fusing GF-1 WFV and MODIS data by the ESTARFM algorithm was demonstrated.A better time window selection method for estimating yields was provided.A better vegetation index suitable for yield estimation based on spatiotemporally fused data was identified.The effect of the spatial resolution of remote sensing data on yield estimations was visualized.Abstract. The accurate estimation of crop yields is very important for crop management and food security. Although many methods have been developed based on single remote sensing data sources, advances are still needed to exploit multisource remote sensing data with higher spatial and temporal resolution. More suitable time window selection methods and vegetation indexes, both of which are critical for yield estimations, have not been fully considered. In this article, the Chinese GaoFen-1 Wide Field View (GF-1 WFV) and Terra Moderate Resolution Imaging Spectroradiometer (MODIS) data were fused by the Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM) to generate time-series data with a high spatial resolution. Then, two time window selection methods involving distinguishing or not distinguishing the growth stages during the monitoring period, and three vegetation indexes, the normalized difference vegetation index (NDVI), two-band enhanced vegetation index (EVI2) and wide dynamic range vegetation index (WDRVI), were intercompared. Furthermore, the yield estimations obtained from two different spatial resolutions of fused data and MODIS data were analyzed. The results indicate that taking the growth stage as the time window unit division basis can allow a better estimation of winter wheat yield; and that WDRVI is more suitable for yield estimations than NDVI or EVI2. This study demonstrates that the spatial resolution has a great influence on yield estimations; further, this study identifies a better time window selection method and vegetation index for improving the accuracy of yield estimations based on a multisource remote sensing data fusion. Keywords: Remote sensing, Spatiotemporal data fusion, Winter wheat, Yield estimation.

Effect of Slope Mesorelief on the Spatial Variability of Soil Properties and Vegetation Index Based on Remote Sensing Data

2019 ◽  
Vol 55 (9) ◽  
pp. 1329-1337
Author(s):  
N. V. Gopp ◽  
T. V. Nechaeva ◽  
O. A. Savenkov ◽  
N. V. Smirnova ◽  
V. V. Smirnov
Keyword(s):  
Remote Sensing ◽  
Spatial Variability ◽  
Soil Properties ◽  
Vegetation Index ◽  
Remote Sensing Data ◽  
Sensing Data

scholarly journals Detecting Forest Degradation in the Three-North Forest Shelterbelt in China from Multi-Scale Satellite Images

2021 ◽  
Vol 13 (6) ◽  
pp. 1131
Author(s):  
Tao Yu ◽  
Pengju Liu ◽  
Qiang Zhang ◽  
Yi Ren ◽  
Jingning Yao
Keyword(s):  
Remote Sensing ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Net Primary Production ◽  
Remote Sensing Data ◽  
Forest Degradation ◽  
Observation Data ◽  
Area Index ◽  
Sensing Data ◽  
Multi Scale

Detecting forest degradation from satellite observation data is of great significance in revealing the process of decreasing forest quality and giving a better understanding of regional or global carbon emissions and their feedbacks with climate changes. In this paper, a quick and applicable approach was developed for monitoring forest degradation in the Three-North Forest Shelterbelt in China from multi-scale remote sensing data. Firstly, Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Ratio Vegetation Index (RVI), Leaf Area Index (LAI), Fraction of Photosynthetically Active Radiation (FPAR) and Net Primary Production (NPP) from remote sensing data were selected as the indicators to describe forest degradation. Then multi-scale forest degradation maps were obtained by adopting a new classification method using time series MODerate Resolution Imaging Spectroradiometer (MODIS) and Landsat Enhanced Thematic Mapper Plus (ETM+) images, and were validated with ground survey data. At last, the criteria and indicators for monitoring forest degradation from remote sensing data were discussed, and the uncertainly of the method was analyzed. Results of this paper indicated that multi-scale remote sensing data have great potential in detecting regional forest degradation.

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