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ISSN 2070-7401 (Print), ISSN 2411-0280 (Online)
Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa
CURRENT PROBLEMS IN REMOTE SENSING OF THE EARTH FROM SPACE

  

Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2024, Vol. 21, No. 3, pp. 171-187

Analysis of the relationship between structural and spectral-reflective characteristics of vegetation in arid grassland landscapes

S.S. Shinkarenko 1 , S.А. Bartalev 1 
1 Space Research Institute RAS, Moscow, Russia
Accepted: 20.05.2024
DOI: 10.21046/2070-7401-2024-21-3-171-187
The article presents the results of comparing structural (canopy, above-ground phytomass) and spectral reflectance characteristics of pasture vegetation in arid landscapes of the southern European part of Russia using Sentinel-2 and MODIS data. Ground data were obtained in May 2020–2022 in the Astrakhan and Volgograd regions, Stavropol Krai, Republics of Kalmykia and Dagestan using standard geobotanical methods on 10×10 m plots. A significant correlation was found between the structural characteristics and brightness of 10-meter spatial resolution Sentinel-2 data, as well as vegetation indices NDVI (Normalized Difference Vegetation Index), SAVI (Soil-Adjusted Vegetation Index), PVI (Perpendicular Vegetation Index) and EVI (Enhanced Vegetation Index). The strongest significant correlation was observed between phytomass (R = 0.74, p < 0.001) and projective cover (R = 0.76, p < 0.001) with NDVI values. The division of vegetation into life forms of dominant species in phytocenoses, such as perennial grasses, subshrubs, annuals, and ephemerals, did not increase the strength of the correlation between structural and spectral reflectance characteristics. The separation of data by years of study allowed establishing a closer relationship between NDVI, canopy, and phytomass. The use of the non-parametric regression Random forest method increased the accuracy of phytomass determination (R 2 = 0.62 and R2 = 0.55) rather than projective cover. Adding such features as precipitation sums for the period preceding the obtained ground data and coefficients of VV- and VH-polarization backscatter from Sentinel-1 data did not improve the accuracy. Additionally, daily MODIS NDVI data with cloudiness influence removed were used in the study, thus helping to obtain NDVI values directly on the dates of field research. This provided a stronger correlation between structural and spectral reflectance characteristics compared to Sentinel-2 data, which had a time difference of up to 12 days compared to ground data. For all types of vegetation, a negative correlation with brightness in the visible and near-infrared ranges was observed, for the soil was brighter than the vegetation cover. Thus, spectral reflectance characteristics are determined not only by photosynthesizing phytomass but also by the degree of soil overlap with vegetation, including dried-up one. This may lead to uncertainties and weaken the correlation between structural and spectral reflectance characteristics.
Keywords: arid landscapes, pasture vegetation, spectrometry, vegetation indices, MODIS, Sentinel-2
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