Zhurnal Radioelektroniki - Journal of Radio Electronics. eISSN 1684-1719. 2023. №1
ContentsFull text in Russian (pdf)
DOI: https://doi.org/10.30898/1684-1719.2023.1.8
USING A NEURAL NETWORK, RADAR
AND MULTISPECTRAL OPTICAL DATA OF SENTINEL-1,2
for THE moisture monitoring of vegetation covered soil
A. M. Zeyliger 1, K.V. Muzalevskiy 2, E.V. Zinchenko 3, O.S. Ermolaeva 4
1Saratov State Vavilov Agrarian University Saratov
410012, Russia, Saratov, pr-kt im. Petra Stolypina, 4, b. 3
2Kirensky Institute of Physics SB RAS
660036, Russia, Krasnoyarsk, Akademgorodok, 50, str. №383All Russia Research Institute of Irrigated Agriculture
400002, Russia, Volgograd, ul. im. Timiryazeva, 94Russian State Agrarian University - MTAA named after Timiryazev
127550, Russia, Moscow, str. Timiryazevskaya, 49
The paper was received November 7, 2022.
Abstract. In this article, a method for the moisture monitoring of vegetation covered soil was proposed using neural network, radar and optical multispectral data of Sentinel-1,2. Test site was chosen in the Volgograd region at an agriculture field. The moisture retrieval algorithm is based on the use of a neural network to predict reflection coefficient of an electromagnetic wave from the soil, followed by inversion into soil moisture using a dielectric model that takes into account the soil texture. The input parameter of the neural network is the ratio of the microwave radar vegetation index (calculated on the basis of Sentinel-1 data) to the multispectral optical index (calculated on 8-11 channels of the Sentinel-2). Such way calculated index reveals a significantly greater dependence on soil moisture than on vegetation height. The retrieved values of soil moisture were compared with the moisture content of in-situ selected soil samples, which were measured under laboratory conditions by the thermostatic-weight method. The proposed method with a determination coefficient of 0.435 and a standard deviation of 2.4 % allows predicting the soil moisture content of a test area covered with vegetation, relative to soil moisture measured in-situ. The conducted research creates the scientific basis for a new all-weather technology for remote sensing the moisture content of agricultural soils as an element of the precision farming system.
Key words: radiolocation, soil moisture, neural networks, permittivity.
Financing: RFBR grant No. 19-29-05261, “Cartographic modeling of water contents in soil cover on the basis of a complex geophysical soil moisture measurements for the aim of digital irrigated agriculture.”
Corresponding author: Muzalevskiy Konstantin, rsdkm@ksc.krasn.ru
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For citation:
Zeyliger A.M., Muzalevskiy K.V., Zinchenko E.V., Ermolaeva O.S. Using a neural network, radar and multispectral optical data of Sentinel-1,2 for the moisture monitoring of vegetation covered soil. Zhurnal radioelektroniki [Journal of Radio Electronics] [online]. 2023. №1. https://doi.org/10.30898/1684-1719.2023.1.8 (In Russian)