CC BY
3«GLOBALLASHUV VA IQLIM O'ZGARISHI SHAROITIDA OZIQ-OVQAT XAVFSIZLIGINI TA'MINLASH: MUAMMO VA YECHIMLAR» RESPUBLIKA ILMIY-AMALIY ANJUMANI 2024 YIL 15 - MAY
REMOTE SENSING-BASED SALINITY INDICES FOR SOIL
SALINITY PROCESSES
1Abdikairov B.E., 2Juliev M.K., 3Gafurova L.A., 4Kholmurodova M.D., 5Djanpulatova Z.A.
1Institute of Agriculture and Agrotechnologies of Karakalpakstan, Nukus, Uzbekistan 2'4'5Institute of Fundamental and Applied Research at TIIAME NRU, Tashkent, Uzbekistan 2'3Turin Polytechnic University in Tashkent, Tashkent, Uzbekistan https://doi.org/10.5281/zenodo.11421311
Abstract. Soil is an important part of biosphere and timely and effective management is essentialfor sustainable development and saving its fertility. Currently, field experiments are time and cost ineffective due to huge financial and labor costs. Modern techniques as Remote sensing and the results of application are crucial for both scientific community and decision makers in order to plan a long term management system. There are enough techniques for determining soil salinity indicators including such kind of indexes NDVI (Normalized Difference Vegetation Index), SAVI (Soil Adjusted Vegetation Index), NDSI (Normalized Difference Salinity Index). In this paper, we investigated the most applied indexes for soil salinization process worldwide.
Keywords: Soil, salinization, degradation, index.
Аннотация. Почва является важной частью биосферы, и своевременное и эффективное управление имеет важное значение для устойчивого развития и сохранения ее плодородия. В настоящее время полевые эксперименты неэффективны с экономически точки зрения и по времени из-за огромных финансовых и трудовых затрат. Современные методы, такие как дистанционное зондирование, и результаты их применения имеют решающее значение как для научного сообщества, так и для лиц, принимающих решения, для планирования долгосрочной системы управления. Существует достаточно методов определения показателей засоления почвы, включая такие индексы NDVI (Normalized Difference Vegetation Index), SAVI (Soil Adjusted Vegetation Index), NDSI (Normalized Difference Salinity Index). В этой статье мы изучали наиболее применяемые показатели процесса засоления почв во всем мире.
Ключевые слова: Почва, засоление, деградация, индекс.
Annotatsiya. Tuproq biosferaning muhim qismi bo 'lib, uning barqaror rivojlanishi va unumdorligini saqlash uchun o 'z vaqtida va samarali boshqarish juda muhimdir. Hozirgi vaqtda dala tajribalari katta moliyaviy va mehnat xarajatlari tufayli iqtisodiy va vaqt tomonidan samarasiz hisoblanadi. Masofadan zondlash kabi zamonaviy texnologiyalar va ularni qo 'llash natijalari uzoq muddatli boshqaruv tizimini rejalashtirish uchun va ilmiy hamjamiyat, ham qaror qabul qiluvchilar uchun juda muhimdir. Tuproqning sho'rlanish ko'rsatkichlarini aniqlash uchun etarli indekslar mavjud, ular qatoridan NDVI (Normalized Difference Vegetation Index), SAVI (Soil Adjusted Vegetation Index), NDSI (Normalized Difference Salinity Index) indekslar orin olgan. Ushbu maqolada biz butun dunyo bo'ylab tuproq sho'rlanish jarayoni uchun eng ko'p qo'llaniladigan indekslarni o'rganib chiqdik.
Kalitso'zlar: Tuproq, sho'rlanish, degradatsiya, indeks.
Introduction
Soil is one of the most essential natural resources, acting as a major source of food, ecological security, and a cornerstone of rural rejuvenation [1]. It is the top layer of the Earth's surface that serves as the carrier and foundation for the majority of the activities that occur in the
«GLOBALLASHUV VA IQLIM O'ZGARISHI SHAROITIDA OZIQ-OVQAT XAVFSIZLIGINI TA'MINLASH: MUAMMO VA YECHIMLAR» RESPUBLIKA ILMIY-AMALIY ANJUMANI 2024 YIL 15 - MAY
biosphere. From the earliest days of Western culture, the Greeks designated this element, along with fire, air, and water, as one of the four main pillars of our natural system, a notion that has endured the test of time [2]. Inefficient use of soil resources may lead to soil degradation including soil salinization.
In arid and semi-arid locations, soil salinization caused by natural processes and human activities poses a serious environmental danger [3]. Soil damaged by salt decreases agricultural production, ecosystem health, and water quality. [4] also mentioned that soil salinity frequently poses a danger to the viability of irrigated agriculture in arid areas. According to [5] the salt-affected area worldwide encompasses 831 million hectares, with 434 million ha of sodic soils and 397 million ha of saline soil. Excessive salinity can occur due to drought, climate change, limited water supplies, and land-use changes. Regular monitoring is necessary to understand the dynamic nature of this occurrence, including its extent, severity, and geographic fluctuation.
Traditional techniques of soil salinity measurement often rely on expensive field soil sample collection and laboratory equipment analysis, making regular large-scale soil salinity monitoring problematic [6]. Today, satellite platforms are increasingly replacing traditional soil salinity monitoring methods due to their ability to give vast amounts of information at cheap cost and regular intervals [7]. Remote sensing (RS) technology can rapidly collect large-scale ground object information in a repeatable manner, which is useful for long-term soil salinization studies [8]. Several spatiotemporal monitoring systems have been developed and implemented across the world to assess the level of soil degradation caused by salt and the danger of its expansion and aggravation [9].
The object of this paper is to analyze indexes for soil salinity identification and italicize the most used in international research activity.
Methodology
Soil salinity analyzing and estimation are possible with the application of indexes [10] shown below in the table 1.
Salinity indices Band ratios
Normalized difference salinity index I\rr>SI = {R+NIR)
Vegetation soil salinity index VSSI = 2 X G- 5 X (R +NIR)
Brightness index BI = VR- H- MIR2
Salinity index- 1 SI = y/(BxR)
Salinity index-2 SI = x/iCJ.x-R)
Salinity index-3 SI = \/(<J'2 + R2 -+- NIR2)
Salinity index-4 SI — x/(G2 -b R2)
Salinity index-5 SJ=f
Salinity index-6
Salinity index-7 SI - Ji^Sl
Salinity index-8
Salinity index-9
]VIodified soil adjusted vegetation Index 2 MSA V/, = (2N7B | 1 y/(2N7B + 1 ) ' — S (NIR li ) ) /2
Ivloisture stress index MSI — '■■'•Z',"''
Normalized difference vegetation index NDVI ">
Normalized difference water index NDWI - sw"'n i • i s m•
Soil adjusted vegetation index (L = 0.5) SAVI = (1 + L) x NIR —R/L + NIR -+- R
Transformed normalized difference vegetation index TMDVI = ^/^ty + 0.5)
«GLOBALLASHUV VA IQLIM O'ZGARISHI SHAROITIDA OZIQ-OVQAT XAVFSIZLIGINI TA'MINLASH: MUAMMO VA YECHIMLAR» RESPUBLIKA ILMIY-AMALIY ANJUMANI 2024 YIL 15 - MAY
Table 1. Indices for identification of salinization
Here, B=Blue, G=Green, R=Red, NIR=Near infrared, SWIR=Shortwave infrared
Results and Discussion
Satellite images are used to detect surface salinity by analysing the spectral reflection of salt crusts and salt deposits on soil surfaces. In dry and semi-arid situations, soluble salts in the lower soil profile ascend to the surface layer by capillary action, forming thin white salt crusts.
Integrating remote sensing data and processes with geographic information systems (GIS) is advantageous because GIS illustrates the geographical distribution of salinity and its accompanying environmental concerns, and mapping soil salinity has become a straightforward procedure during the last three decades.
One of the most used indexes is NDVI (Normalized difference vegetation index) which is used to determine the status of the vegetation in a given region. NDVI values vary from -1 to +1, with negative values indicating water and clouds, positive values around zero indicating barren land, and higher values ranging from sparse vegetation (0.1-0.5) to thick vegetation.
Based on the results of NDVI, it is possible to predict land conditions in the study area which may be a basis for preliminary assessment of soil salinity condition. Cause, the more salinity level, the less vegetation cover.
NDSI (Normalized difference salinity index) has the ability to enhance and delineate soil salinity features in images [11].
From the research of [12] SAVI (Soil adjusted vegetation index) was found as a strong predictor of soil salinity.
Soil salinity and condition vary with time, especially in places affected by wind, sand, and drainage. That is why it is important timely monitoring of salinization process. As mentioned above, field experiments are time and cost inefficient. Remote sensing data is able to evaluate quickly large areas and estimate real conditions of soil cover. There are lots of spectral indexes for determining both salinity and land cover indicators. According to [11] Normalized difference
523000 526000 529000 532000 535000
523000 526000 529000 532OCX} 535000
«GLOBALLASHUV VA IQLIM O'ZGARISHI SHAROITIDA OZIQ-OVQAT XAVFSIZLIGINI TA'MINLASH: MUAMMO VA YECHIMLAR» RESPUBLIKA ILMIY-AMALIY ANJUMANI 2024 YIL 15 - MAY
salinity index (NDSI) models accurately predicted soil salinity spatial variation at Wonji sugar cane irrigation farm of Ethiopia (Figure 1).
Figure 1. Application of NDSI index for soil salinity identification [13] applied NDVI for global mapping of soil salinity change (Figure 2) where NDVI was the best approach to estimate global vegetation cover and identify soil salinity change. Figure 2. Global mapping of soil salinity change
[14] selected SAVI index as the selected method reduces spectral variance due to soil background at Al-Hassa Oasis of Arabian Peninsula (Figure 3).
Southern
«GLOBALLASHUV VA IQLIM O'ZGARISHI SHAROITIDA OZIQ-OVQAT XAVFSIZLIGINI TA'MINLASH: MUAMMO VA YECHIMLAR» RESPUBLIKA ILMIY-AMALIY ANJUMANI 2024 YIL 15 - MAY
Figure 3. Application of SAVI and NDSI index for soil salinity detection
Conclusion
Soil salinization is a major issue affecting soil fertility, crop production, and sustainable agricultural growth globally. Timely and effective system for prevention soil salinization is valuable approach. Currently, it is possible to identify soil salinity distribution quickly by the support of Remote sensing data including different spectral indices which include such salinity and land cover change predictors SAVI, NDSI, NDVI. Application of them may save financial and labor resources and lead to effective research activity.
Conflict of interests: The authors declare no conflict of interest
REFERENCES
1. Y. Liu, W. Shen, K. Fan, W. Pei, and S. Liu, "Spatial Distribution, Source Analysis, and Health Risk Assessment of Heavy Metals in the Farmland of Tangwang Village, Huainan City, China," Agronomy, vol. 14, no. 2, p. 394, Feb. 2024, doi: 10.3390/agronomy14020394.
2. M. M. Jordán Vidal, "Criteria for Assessing the Environmental Quality of Soils in a Mediterranean Region for Different Land Use," Soil Systems, vol. 7, no. 3, p. 75, Aug. 2023, doi: 10.3390/soilsystems7030075.
3. A. Zarei, M. Hasanlou, and M. Mahdianpari, "A comparison of machine learning models for soil salinity estimation using multi-spectral earth observation data," in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, F. F. S. Paparoditis N. Mallet C. ,. Lafarge F. ,. Yang M. Y. ,. Jiang S. ,. Shaker A. ,. Zhang H. ,. Liang X. ,. Osmanoglu B. ,. Soergel U. ,. Honkavaara E. ,. Scaioni M. ,. Zhang J. ,. Peled A. ,. Wu L. ,. Li R. ,. Yoshimura M. ,. Di K. ,. Altan O. ,. Abdulmuttalib H. M., Ed., Copernicus GmbH, 2021, pp. 257-263. doi: 10.5194/isprs-annals-V-3-2021-257-2021.
4. J. Herrero, C. Castañeda, and R. Gómez-Báguena, "A Heritage Agronomic Study as a Database for Monitoring the Soil Salinity of an Irrigated District in NE Spain," Agronomy, vol. 12, no. 1. MDPI, 2022. doi: 10.3390/agronomy12010126.
5. G. Sahbeni, "A PLSR model to predict soil salinity using Sentinel-2 MSI data," Open Geosciences, vol. 13, no. 1, pp. 977-987, 2021, doi: 10.1515/geo-2020-0286.
6. L. Xie, X. Feng, C. Zhang, Y. Dong, J. Huang, and J. Cheng, "A Framework for Soil Salinity Monitoring in Coastal Wetland Reclamation Areas Based on Combined Unmanned Aerial Vehicle (UAV) Data and Satellite Data," Drones, vol. 6, no. 9. MDPI, 2022. doi: 10.3390/drones6090257.
7. B. Meng et al., "Evaluation of Remote Sensing Inversion Error for the Above-Ground Biomass of Alpine Meadow Grassland Based on Multi-Source Satellite Data," Remote Sensing, vol. 9, no. 4, p. 372, Apr. 2017, doi: 10.3390/rs9040372.
8. G. Hong et al., "Extraction and Analysis of Soil Salinization Information in an Alar Reclamation Area Based on Spectral Index Modeling," Applied Sciences, vol. 13, no. 6, 2023, doi: 10.3390/app13063440.
9. A. Chaaou, M. Chikhaoui, M. Naimi, A. Kerkour El Miad, and M. Seif-Ennasr, "Mapping Soil Salinity Risk by Using an Index Approach," Environmental Sciences Proceedings, vol. 16, no. 1, 2022, doi: 10.3390/environsciproc2022016024.
10. O. M. Kilic et al., "Soil salinity assessment of a natural pasture using remote sensing techniques in central Anatolia, Turkey," PLoS ONE, vol. 17, no. 4 April, 2022, doi: 10.1371/journal.pone.0266915.
«GLOBALLASHUV VA IQLIM O'ZGARISHI SHAROITIDA OZIQ-OVQAT XAVFSIZLIGINI TA'MINLASH: MUAMMO VA YECHIMLAR» RESPUBLIKA ILMIY-AMALIY ANJUMANI 2024 YIL 15 - MAY
11. E. Asfaw, K. V. Suryabhagavan, and M. Argaw, "Soil salinity modeling and mapping using remote sensing and GIS: The case of Wonji sugar cane irrigation farm, Ethiopia," Journal of the Saudi Society of Agricultural Sciences, vol. 17, no. 3. King Saud University, pp. 250-258,
2018. doi: 10.1016/j.jssas.2016.05.003.
12. H. Nouri et al., "Soil salinity mapping of urban greenery using remote sensing and proximal sensing techniques; The case of veale gardens within the adelaide parklands," Sustainability (Switzerland), vol. 10, no. 8. MDPI, 2018. doi: 10.3390/su10082826.
13. K. Ivushkin, H. Bartholomeus, A. K. Bregt, A. Pulatov, B. Kempen, and L. de Sousa, "Global mapping of soil salinity change," Remote Sensing of Environment, vol. 231. Elsevier Inc.,
2019. doi: 10.1016/j.rse.2019.111260.
14. A. Allbed, L. Kumar, and Y. Y. Aldakheel, "Assessing soil salinity using soil salinity and vegetation indices derived from IKONOS high-spatial resolution imageries: Applications in a date palm dominated region," Geoderma, vol. 230-231. Elsevier, pp. 1-8, 2014. doi: 10.1016/j.geoderma.2014.03.025.
