Vegetation Index-Based Remote Sensing Approaches for Evaluating Mulberry (Morus Spp.) Leaf Yield

Abstract:

Mulberry (Morus spp.) is the sole host plant of the silkworm (Bombyx mori L.), and its leaf yield and biochemical quality directly govern silkworm growth, cocoon yield and overall sericultural productivity. Conventional methods of mulberry leaf yield estimation rely heavily on field-based measurements and visual assessments, which are labour-intensive, subjective and inadequate for large-scale planning and forecasting. In this context, satellite-based remote sensing, particularly vegetation index (VI)–based approaches, has emerged as a robust, non-destructive and spatially explicit tool for evaluating mulberry canopy condition, biomass and leaf quality. This review critically synthesizes existing research on vegetation index–based remote sensing techniques for the assessment of mulberry leaf yield and canopy biochemical attributes. Core yield-sensitive indices such as NDVI, EVI, SAVI and OSAVI, along with ratio- and difference-based indices (RVI, DVI, IPVI), soil and atmosphere-adjusted indices (ARVI, MSAVI) and high-biomass indices (WDRVI) are examined for their utility in estimating mulberry leaf biomass. In addition, chlorophyll, nitrogen, water and pigment-sensitive indices, including GNDVI, NDRE, chlorophyll indices, MCARI, TCARI, MTCI, PRI, NDWI, MSI and senescence-related indices, are reviewed for their potential in assessing leaf nutritional quality and physiological status. The synthesis highlights the growing relevance of multispectral and red-edge based indices from sensors such as Sentinel-2, MODIS and UAV platforms in capturing mulberry canopy dynamics. Overall, vegetation index–based remote sensing offers a powerful framework for timely estimation of mulberry leaf yield and quality, enabling improved DFL planning, cocoon production forecasting and sustainable intensification of sericulture.