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 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 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.

Estimation of winter wheat yield by using remote sensing data and crop model

2012 ◽  
Author(s):  
Jianmao Guo ◽  
Tengfei Zheng ◽  
Qi Wang ◽  
Jia Yang ◽  
Junyi Shi ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Winter Wheat ◽  
Wheat Yield ◽  
Remote Sensing Data ◽  
Crop Model ◽  
Sensing Data ◽  
Winter Wheat Yield

scholarly journals RELATIONSHIPS BETWEEN RICE GROWTH PARAMETERS AND REMOTE SENSING DATA

2010 ◽  
Vol 4 (1) ◽  
Author(s):  
I Wayan Nuarsa ◽  
Fumihiko Nishio
Keyword(s):  
Remote Sensing ◽  
Spectral Characteristic ◽  
Vegetation Index ◽  
Growth Parameters ◽  
Life Stage ◽  
Remote Sensing Data ◽  
Sensing Data ◽  
Rice Growth ◽  
Vegetation Water ◽  
The Relationship

Rice is an agriculture plants that has the specific characteristic in the life stage due to the growth stage having different proportion of vegetation, water, and soil. Vegetation index is one of the satellite remote sensing parameter that is widely used to monitor the global vegetation cover. The objective of the study is to know the spectral characteristic of rice plant in the life stage and find the relationship between the rice growth parameters and the remote sensing data by the Landsat ETM data using the correlation and regression analysis. The result of study shows that the spectral characteristic of the rice before one month of age is defferent comparing after one month. All of the examined vegetation index has close linear relationship with rice coverage. Difference Vegetation Index (DVI) is the best vegetation index which estimates rice coverage with equation y = 1.762x + 2.558 and R degree value was 0.946. Rice age has a high quadratic relationship with all of evaluated vegetation index. Transformed Vegetation Index (TVI) is the best vegetation to predict the age of the rice. Formula y = 0.013x - 1.625x + 145.8 is the relationship form between the rice age and the TVI with R = 0.939. Peak of the vegetation index of rice is in the rice age of 2 months. This period is the transition of vegetative and generative stages. Keywords: Vegetation index, Rice growth, Spectral characteristic, Landsat ETM.

scholarly journals Performance evaluation of remote sensing data with machine learning technique to determine soil color

2020 ◽  
Vol 53 (1) ◽  
pp. 97
Author(s):  
Laleh Parviz
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Correlation Coefficient ◽  
Stepwise Regression ◽  
Vegetation Index ◽  
Remote Sensing Data ◽  
Soil Color ◽  
Machine Learning Technique ◽  
Sensing Data ◽  
Learning Technique

<p>The aim of the present research is the determination of soil color by spectral bands and indices obtained from MODIS images. For this purpose, soil samples were collected from East Azerbaijan Province (Iran) and their color and texture were investigated through Munsell color system and hydrometer method, respectively. Stepwise regression, principle component analysis and sensitivity function methods were employed to find the dominant indices and bands using artificial neural network (ANN) as one of the machine learning techniques. The improved indices as the model input had better performance, for example, the calculation of correlation coefficient between indices and hue showed 51.48% increase of correlation coefficient with comparison of the normalized difference vegetation index (NDVI) to modified soil adjustment vegetation index (MSAVI) and 54.54% correlation enhancement of soil adjustment vegetation index (SAVI) compared to MSAVI. Stepwise regression method along with error criteria decline may enhance the performance of soil color model. In comparison with multivariate regression, ANN model exhibited better performance (with a 12.61% mean absolute error [MAE] decline). Temporal variation of modified perpendicular drought index (MPDI) as well as band 31 could justify the Munsell soil color components variations specifically chroma and hue. MPDI and thermal bands could be employed as a precise indicator in soil color analysis. Thus, remote sensing data combined with machine learning technique can clarify the procedure potential for soil color determination.</p>

scholarly journals Assimilating Soil Moisture Retrieved from Sentinel-1 and Sentinel-2 Data into WOFOST Model to Improve Winter Wheat Yield Estimation

2019 ◽  
Vol 11 (13) ◽  
pp. 1618 ◽  
Author(s):  
Wen Zhuo ◽  
Jianxi Huang ◽  
Li Li ◽  
Xiaodong Zhang ◽  
Hongyuan Ma ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Winter Wheat ◽  
Cloud Model ◽  
Wheat Yield ◽  
Remote Sensing Data ◽  
Yield Estimation ◽  
Sensing Data ◽  
Winter Wheat Yield ◽  
Sentinel 2

Crop yield estimation at a regional scale over a long period of time is of great significance to food security. In past decades, the integration of remote sensing observations and crop growth models has been recognized as a promising approach for crop growth monitoring and yield estimation. Optical remote sensing data are susceptible to cloud and rain, while synthetic aperture radar (SAR) can penetrate through clouds and has all-weather capabilities. This allows for more reliable and consistent crop monitoring and yield estimation in terms of radar sensor data. The aim of this study is to improve the accuracy for winter wheat yield estimation by assimilating time series soil moisture images, which are retrieved by a water cloud model using SAR and optical data as input, into the crop model. In this study, SAR images were acquired by C-band SAR sensors boarded on Sentinel-1 satellites and optical images were obtained from a Sentinel-2 multi-spectral instrument (MSI) for Hengshui city of Hebei province in China. Remote sensing data and ground data were all collected during the main growing season of winter wheat. Both the normalized difference vegetation index (NDVI), derived from Sentinel-2, and backscattering coefficients and polarimetric indicators, computed from Sentinel-1, were used in the water cloud model to derive time series soil moisture (SM) images. To improve the prediction of crop yields at the field scale, we incorporated remotely sensed soil moisture into the World Food Studies (WOFOST) model using the Ensemble Kalman Filter (EnKF) algorithm. In general, the trend of soil moisture inversion was consistent with the ground measurements, with the coefficient of determination (R2) equal to 0.45, 0.53, and 0.49, respectively, and RMSE was 9.16%, 7.43%, and 8.53%, respectively, for three observation dates. The winter wheat yield estimation results showed that the assimilation of remotely sensed soil moisture improved the correlation of observed and simulated yields (R2 = 0.35; RMSE =934 kg/ha) compared to the situation without data assimilation (R2 = 0.21; RMSE = 1330 kg/ha). Consequently, the results of this study demonstrated the potential and usefulness of assimilating SM retrieved from both Sentinel-1 C-band SAR and Sentinel-2 MSI optical remote sensing data into WOFOST model for winter wheat yield estimation and could also provide a reference for crop yield estimation with data assimilation for other crop types.

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