CC BY
23
Usmanov Rustam Makhmudovich, senior researcher,
Institute of genetics and plant experimental biology E-mail: [email protected] Madaminov Ruslan Ramanberdiyevich, postgraduate student, Khorezm Mamun academy E-mail: [email protected] Bahodirov Zafar Abduvaliyevich, postgraduate student, Research Institute of Soil Science and Agrochemistry E-mail: [email protected]
ANALYSIS OF IMPACTS OF SOIL SALINITY ON WINTER WHEAT DEVELOPMENT WITH SPECTRAL VEGETATION INDICES
Abstract: Relationship between the condition of agricultural crops and stress factors are studied with the application of modern technologies in the article. Herein, correlation relationship between Normalized Difference Vegetation Index (NDVI) and LeafArea Index (LAI) spectral vegetation indices, which represent winter wheat plant condition and salinity stress are highlighted in details.
Keywords: winter wheat, Khorezm region, spectral vegetation indices, NDVI, LAI, soil salinity.
Introduction extensively used to detect not only seasonal variability of the
The application of new technologies is one of the most vegetation cover but also local scale spatial variability [4; 5]. important issues in increasing agricultural crop harvest, mak- Research objectives and methods
ing soil characteristics more acceptable, as well as in saving the costs for land cultivation, machinery, manpower, fertilizers and fuel materials.
Many researches were carried out by many scientists on the impacts of different factors on the growth, development and crop harvest of winter wheat in irrigated soil conditions of Uzbekistan. But, many of them were conducted by using traditional methods, and technologies, based on modern methods, is not widely used in studying vegetation cover and plant physiology in Uzbekistan. Therefore, we decided to apply methods of modern information technologies, as the methods of remote sensing, spectral vegetation indices and geoinformation systems.
Remote sensing data are considered a convenient source to perform several vegetation indices in either simple or complicated band ratio combinations. Satellite images offer a large amount of data that could be analyzed, processed and stored to better understand several vegetation indices based on the type of the satellite sensor used. Hypothetical backgrounds have been implemented to improve and enhance the optimization of particular satellite sensors to support certain vegetation indices [1; 2; 3].
Spectral vegetation indices are mathematical combinations of different spectral bands mostly in the visible and near-infrared regions of the electromagnetic spectrum. Vegetation activities can be measured comprehensively through semi-analytical methods of spectral band ratios that have been
The object of the research is winter wheat, cultivated in different districts of Khorezm region in Uzbekistan.
Khorezm region is situated in the northwest of Uzbekistan, on the left bank of the Amudarya River, the area of the region covers 6800 km2. According to hydro-geological, soil and climatic conditions, Khorezm differs from other regions of Uzbekistan. The entire irrigated area in the region shows secondary salinization problems, and 81% of the area has problems with waterlogging [6]. Shallow groundwater level between 0.5 m and 2.0 m causes secondary soil salinization and waterlogging.
The climate of the region can be described as "typically arid continental" with considerable seasonal and daily temperature fluctuations: long hot dry summers, sporadic rains or snow in autumn-spring and very cold temperatures in winter [6; 7]. Cotton, winter wheat and rice are the main agricultural crops of Khorezm region.
In determining NDVI, one of the spectral vegetation indices, hand-held GreenSeeker was used, and in determining the leaf area index AccuPAR Ceptometer LP-80 was used. In assessing soil salinity Digital Meter Hanna Instruments pH/EC/°C -mod. Combo was used. The analyses of the obtained data from these instruments are carried out based on the manuals of these instruments. Field observations are carried out according to traditional methods based on generally accepted guides in Uzbekistan.
Research works were carried out in six farms in different parts of Khorezm region. They are "Farrukh" and "Bakhtiyor shofyor" farms in Urgench district on the bank of the Amudarya River; "Qadam Jigildak" and "Khushnud Nodirbek" farms in Khiva district on the Polvon channel; "Yuldosh majid" and "Khorazm giyohlari" farm also in Khiva district near desert, at the end of irrigation channels. These farms differ according to the level of irrigation water supply. As the farms in Urgench district are on the river, irrigation water is always available for them, the farms on Polvon channel is supplied with irrigation in middle level. And the least supplied farms are near the desert. As they are situated at the end of irrigation system, they always faced with the irrigation problems.
Results of the research
1) NDVI
NDVI is one of the most-used vegetation indices (VI): NDVI = (pNIR - pRed)/(pNIR + pRed) where pNIR and pRed are reflectance values of Red and Near Infrared light received at the sensors. The Simple Ratio (SR) and NDVI are built on the observation that chlorophylls a and b in green leaves strongly absorb light in the Red, with maximum absorption at about 690 nm, while the cell walls strongly scatter (reflect and transmit) light in the NIR region (about 850 nm). This results in a strong absorption contrast across a narrow wavelength band of 650-850 nm, captured by the NDVI and other VIs. NDVI and related VIs are functional variants of the SR. NDVI normalizes values between -1 to +1; dense vegetation has a high NDVI, while soil values are low but positive, and water is negative due to its strong absorption of NIR.
Statistical analyses of the results of NDVI are shown in (Table 1). According to the table, the values of NDVI for the selected areas vary between 0.21 and 0.727. Its arithmetical mean value is 0.43 and median value is 0.382. Variance of these values makes up 0.03 and the average error is 0.012.
2) LAI
LAI is a mathematical construct that does not have a direct relationship to photosynthetically active radiation (PAR) or processes that depend on PAR. LAI is usually defined as the one-sided area of leaves in a canopy per unit ground area of canopy cover but non-flat leaves complicate the definition. LAI is related to light interception by a canopy (R.) by: R. = Rs (1 - exp-tLAI)
Table 1. - Statistical analyses of the
where k is a factor that accounts for leaf angles and other factors that affect absorption of Rs within a canopy. Plants with relatively vertical leaves (erectophiles) typically absorb less light per unit leaf area than plants with relatively horizontal leaves (planophiles). The coefficient k also depends on the arrangement of plants within a stand, because isolated plants receive light from all sides of their canopy whereas a dense stand of plants is only illuminated at the top of the canopy. The fraction of light absorbed by the canopy (PAR) depends not only on R. but also on the spectral properties of the leaves. Some leaves have reflective surfaces to minimize heat gain while others absorb nearly all of the incident radiation between 400 and 700 nm.
According to the statistical analyses of the results, the values of LAI for the selected areas vary between 1.42 and 6.29. Its arithmetical mean is 3.41 and median value is 2.99. Variance of these values makes up 2.37 and the average error is 0.86 (Table 1).
3) Soil salinity (EC)
Soil salinity is an important issue constraining the productivity of irrigation agriculture around the world. The standard method for soil salinity assessment is based on a laboratory method that is cumbersome and gives rise to limitations for data-intensive works. The use of sensors for the assessment of the apparent electrical conductivity (EC) of soils offers a way to overcome these constraints. These sensors are based on three electromagnetic phenomena, namely, electrical resistivity, electromagnetic induction, and reflectometry.
Electrical conductivity (EC) is a numerical expression of the inherent ability of a medium to carry an electric current. Because the EC and total salt concentration of an aqueous solution are closely related, EC is commonly used as an expression of the total dissolved salt concentration of an aqueous sample, even though it is also affected by the temperature of the sample and by the mobility, valences and relative concentrations of the individual ions comprising the solution (water itself is a very poor conductor of electricity). Furthermore, not all dissolved solutes exist as charged-species; some are non-ionic and some of the ions combine to form ion-pairs which are less charged (they may even be neutral) and, thus, contribute proportionately less to electrical conduction than when fully dissociated.
results on the studied indicators
№ Indexes Min. Max. Arithmetical mean Median Variance Average error
1. NDVI 0.21 0.727 0.430 0.382 0.03 0.01
2. LAI 1.42 6.29 3.41 2.99 2.37 0.86
3. EC 1.84 17.72 6.91 6.12 0.60 0.57
Relationship between soil salinity and vegetation indexes
On the next step of the research, relationship between soil salinity and spectral vegetation indices, those define condition ofvegetation, were studied. Here statistical analyses were carried out on correlation of EC values of soil salinity and the values of NDVI and LAI. The correlation matrix of the results is shown in (Table 2).
Analyses on soil salinity were carried out on the base of the values of EC meter by Hanna instruments. Results of statistical analyses on soil salinity are shown in (Table 1). According to the table, the values of salinity vary between 1.84 and 17.72. Its arithmetical mean is 6.91 and median value is 6.12. Variance of these values is 0.6 and the average error is 0.57. In the observed points the values of salinity increases to the lower layers on the soil profile.
Table 2.- Matrix of correlation relationship between soil salinity and spectral vegetation indices
Correlation matrix (Pearson):
Variables LAI NDVI EC
LAI 1 0.892 0.902
NDVI 0.892 1 0.808
EC 0.902 0.808 1
The obtained results show that there is a significant rela- and it makes up 78-82%. The relationship between these val-tionship between soil salinity and spectral vegetation indices, ues is shown in the following diagram:
Scatter plots:
Figure 1. Diagrams of relationship between soil salinity and spectral vegetation indices
Conclusions
There is correlation between soil salinity, one of the stress factors for agricultural crops, and spectral vegetation index, that is peculiar for winter wheat plant and by this point, it is possible to study the impacts of stress factors on plants.
Modern methods, as remote sensing technologies, have a number of advantages in agricultural production and in assessment of the condition of agricultural crops, and increase the accuracy and validity of the analyses, as well as define its convenience and speed.
References:
1. Michel M. Verstraete, Bernard Pinty, Ranga B. Myneni, Potential and limitations of information extraction on the terrestrial biosphere from satellite remote sensing, Remote Sensing of Environment,- Vol. 58.- Issue 2.- P. 201-214.
2. Nadine Gobron, Bernard Pinty, Michel M. Verstraete, and Jean-Luc Widlowski, Advanced Vegetation Indices Optimized for Up-Coming Sensors: Design, Performance, and Applications, ieee transactions on geoscience and remote sensing,-Vol. 38.- No. 6.- P. 2489-2505.
3. Psilovikos A. and Elhag M. Forecasting of Remotely Sensed Daily Evapotranspiration Data over Nile Delta Region, Egypt, Water Resour. Manag., 27, 2013.- P. 4115-4130.
4. Broge N. H. and Mortensen J. V. Deriving green crop area index and canopy chlorophyll density of winter wheat from spectral reflectance data, Remote Sens. Environ., 81, 2002.- P. 45-57.
5. Xiao X., He L., Salas W., Li C., Moore B., Zhao R., Frolking S., and Boles S. Quantitative relationships between field-measured leaf area index and vegetation index derived from vegetation images for paddy rice fields, Int. J. Remote Sens., 23, 2002.- P. 3595-3604.
6. Abdullaev U. Republic of Uzbekistan. Land degradation assessment in dry lands (LADA). State Design and Research Institute (Uzgip), - Tashkent, 2003.
7. Bogushevskiy A. A., Golovanov A. I., Kutergin V. A., Mamaev M. G. Markov E. S., Raevskaya N. G. and Fokeev P. I. General information about irrigation and irrigation systems (In Russian), In Markov E. S. Textbook on agricultural hydrotechnical amelioration, 1981.- P. 7-10. Kolos,- Moscow.
