Application of the adaptive index dSAVI to reduce the influence of soil background in the early vegetation stages of crops based on Sentinel-2 data in precision agriculture technologies
Heading:
| 1Zatserkovnyi, V, Vorokh, V, Stakh, iv, I, Tsiupa, I, Pastushenko, T 1Department of Geoinformatics, Taras Shevchenko National University of Kyiv, Kyiv, Ukraine |
| Spase Sci.&Tecnol. 2026, 32 ;(1):46-56 |
| https://doi.org/10.15407/knit2026.01.046 |
| Publication Language: English |
Abstract: Vegetation indices are a fundamental tool for monitoring crop conditions; however, during the early growth stages, NDVI values are significantly distorted by the influence of soil background, moisture, and microrelief. The classical SAVI index partially reduces this dependency through a fixed coefficient yet its constant value does not reflect the actual variation in the vegetation — soil signal ratio within a field.
This study proposes a modified adaptive vegetation index — dSAVI, in which the coefficient is determined dynamically using the formula = 1 — NDVI², where NDVI is the normalized NDVI value within the field. All calculations were performed in Google Earth Engine using Sentinel-2 L2A data with consistent cloud and shadow masking (SCL classes 3, 8, 9, 10, 11 excluded). Median composites were generated for early (01 March — 15 April, 2025) and late (01 July — 31 August, 2025) phenological periods, followed by computation of NDVI, SAVI, OSAVI, dSAVI, and a difference map Δ = dSAVI — SAVI.
Comparison of the maps showed that the modified dSAVI index significantly suppresses the “patchiness” caused by soil background at early vegetation dates, particularly in areas with bare or lighter soil. The Δ map revealed zones of potential soil effects, while the coefficient served as a diagnostic indicator of the compensation degree. The correlation between Δ and (r 0.8) confirmed the physical validity of the approach.
The proposed dSAVI vegetation index provides more stable early-season zoning and better compatibility with VRA fertilizer maps. The method is simple to implement, requires no manual parameter adjustment, and can be scaled to multiple fields. Under current conditions, the use of precision agriculture technologies by small and medium-scale Ukrainian farmers is sporadic, often limited to NDVI. Therefore, the introduction of adaptive vegetation indices such as dSAVI can be a step toward improving monitoring accuracy and overall agricultural efficiency.
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| Keywords: vegetation indices; precision agriculture; remote sensing; Sentinel-2; SAVI; MSAVI; OSAVI; dSAVI; NDVI; soil background; crop condition mapping |
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