Modeling the weight moisture content of soils in Anabar tundra using ground-based research and remote sensing data of the earth

Abstract

Presently, the climate is warming, with average annual temperatures rising every year, and the Arctic remaining one of the most vulnerable regions. This has a noticeable effect on the northern forests, which leads to the displacement of the tundra zone. Assessing the consequences of a projected increase in air temperature under climate change conditions requires spatial and instant monitoring of hard-to-reach areas. In the last few years, the use of remote sensing techniques and combinations of satellite imagery to calculate vegetation indices has generated great interest in obtaining high-definition ground data. The article presents the results of modeling soil moisture in Anabar tundra, which is located in the north-west of Yakutia. The study was conducted on the border of tundra and forest tundra in three reference areas (tundra, tundra young forest, tundra indigenous forest). The simulation is presented by comparing the field data and the normalized difference index of humidity NDMI. The NDMI index uses the NIR and SWIR channels, calculates a multi-channel raster object and creates a raster image with index values from -1 to 1, which show the humidity level with a spatial resolution of 10 meters. The data showed that the value of humidity pixels according to NDMI in the tundra zone is 0.04, in the tundra young forest is 0.09 and, in the tundra, native forest is 0.15. It was found that the index data correlated with field humidity data obtained from a depth of 0.2 m. A comparison of remote sensing decryption data with field data made it possible to simulate the spatial distribution of soil moisture over the vast study area by extrapolation.