Application of Multispectral and Radar Data for Assessing Soil Moisture of Arable Lands and The Condition of Reclamation Systems (a Case Study of Khabarovsk Territory)
DOI:
https://doi.org/10.7868/S3034519726020078Keywords:
moisture, cropland, NDWI, radar satellites, reclamation systems, Khabarovsk TerritoryAbstract
Recently, great attention has been paid in the Russian Federation to collecting and analyzing information on the state of reclamation systems and reclaimed lands, including the use of modern remote sensing methods. This article discusses the use of multispectral and radar satellite data to assess the moisture content of cropland and the state of reclamation systems in Khabarovsk Krai. For the period from April to October 2024, NDWI values were calculated using Sentinel-2 satellite data, and the Dubois moisture model was built using ALOS-2 data. Soil moisture was also estimated using SMAP mission data. 984 fields with soybean, buckwheat, and grain crops, as well as unused land, were considered. It was found that field waterlogging can be determined using NDWI values and the Dubois model using images from late April to May. Moreover, NDWI values can be used to identify fallow land. NDWI values were calculated for fields of four reclamation systems in Khabarovsk Territory. Significant differences were found between the average NDWI values for May 20, 2024, for fields using the Bazki system (-0.42) and the three other systems. A cluster analysis of soybean and buckwheat fields within the Bazki system revealed six soybean fields and ten buckwheat fields with signs of spring waterlogging. Overall, the use of radar and multispectral data allows for both regional (SMAP-based moisture model) and field-level assessments of agricultural land moisture dynamics (NDWI and Dubois model). Further research involves acquiring and processing radar data from various satellites to increase the number of radar images, which will enable the use of a comprehensive multispectral radar model.
References
1. Асеева Т.А., Синеговский М.О. Современное состояние отрасли растениеводства Хабаровского края // Вестник российской сельскохозяйственной науки. 2023. № 3. С. 4-6. https://doi.org/10.31857/2500-2082/2023/3/4-6.
2. Зверьков М.С., Брыль С.В. Оценка мелиоративного состояния гидромелиоративной системы с использованием данных дистанционного зондирования земли и беспилотного летательного аппарата // Природообустройство. 2021. № 2. С. 6–16. https://doi.org/10.26897/1997-6011-2021-2-6-16.
3. Хашиев А. Б., Бабаков В.П. Мелиорация в земледелии Хабаровского края // Аграрная наука. 2021. № 1. С. 104-107. https://doi.org/10.32634/0869-8155-2021-344-1-104-107.
4. Шевченко В.А., Исаева С.Д., Дедова Э.Б., Наумова Т.В. Современные вопросы совершенствования организационно-правовой базы мелиорации земель // Вестник российской сельскохозяйственной науки. 2023. № 1. С. 63-67. https://doi.org/10.31857/2500-2082/2023/1/63-67.
5. Якушев В.П., Захарян Ю.Г., Блохина С.Ю. Состояние и перспективы использования дистанционного зондирования Земли в сельском хозяйстве // Современные проблемы дистанционного зондирования Земли из космоса. 2022. Т. 19. № 1. С. 287-294. https://doi.org/10.21046/2070-7401-2022-19-1-287-294.
6. Bandak S., Movahedi Naeini S.A.R., Komaki C.B. et al. Satellite-Based Estimation of Soil Moisture Content in Croplands: A Case Study in Golestan Province, North of Iran // Remote Sensing. 2023. No.15(8). Р. 2155. https://doi.org/2155.10.3390/rs15082155.
7. Chung J., Lee Y., Kim J. et al. Soil Moisture Content Estimation Based on Sentinel-1 SAR Imagery Using an Artificial Neural Network and Hydrological Components // Remote Sensing. 2022. Vol. 14(3). P. 465.
8. Dubois P.C., van Zyl J., Engman T. Measuring soil moisture with imaging radars // IEEE Transactions on Geoscience and Remote Sensing. 1995. Vol. 33(4). P. 915-926.
9. Imtiaz F., Farooque A.A., Randhawa G.S. et al. An inclusive approach to crop soil moisture estimation: Leveraging satellite thermal infrared bands and vegetation indices on Google Earth engine // Agricultural Water Management. 2021. Vol. 306. P. 109172. https://doi.org/10.1016/j.agwat.2024.109172.
10. McFeeters S.K. The use of the Normalized Difference Water Index (NDWI) in the delineation of open water features // International Journal of Remote Sensing. 1996. Vol.17. No. 7. P. 1425–1432.
11. Rashid Md.B. Monitoring of drainage system and waterlogging area in the human-induced Ganges-Brahmaputra tidal delta plain of Bangladesh using MNDWI index // Heliyon. 2023. Vol. 9. No. 6. P. e17412. https://doi.org/10.1016/j.heliyon.2023.e17412.
12. Roy P.D., Dey S., Bhogapurapu N. Retrieval of Surface Soil Moisture at Field Scale Using Sentinel-1 SAR Data // Sensors. 2025. Vol. 25(10). P. 3065. https://doi.org/10.3390/s25103065.
13. Ulaby F.T., Long D. Microwave Radar and Radiometric Remote Sensing // Michigan Press. Publisher: Univ. of Michigan Press. Ann Arbor, MI, USA, 2014. P. 1116.
14. Wig E., Michaelides R., Zebker H. Fine-Resolution Measurement of Soil Moisture from Cumulative InSAR Closure Phase // IEEE Transactions on Geoscience and Remote Sensing. 2024. Vol. 62. P. 5212315. https://doi.org/10.36227/techrxiv.23929068.v1.
Исследование выполнено за счет гранта Российского научного фонда № 23-76-00007, https://rscf.ru/project/23-76-00007/.
