Spectral library of crops and discrimination of major vegetables grown in the eastern Himalayan ecosystem: A proximal hyperspectral remote sensing approach

Abstract

Identifying, characterising and mapping, unique vegetable crops grown on small mixed lands in the eastern Himalayan mountain ecosystem (EHME) using traditional methods is a challenge. The exploration of high-resolution, multispectral optical satellite data for vegetable mapping is hindered by heavy rainfall, clouds, and year-round fog/snow cover. Hyperspectral imaging is restricted in the region that borders several international countries because of security concerns. In the global literature on spectroscopy, information on vegetable crops cultivated in the Himalayan ecosystem is marginal or never covered. Thus, we used a portable Field Spec Handheld2 (ASD) spectroradiometer for spectral characterization of 28 important vegetables, from 10 different families. During the summer and winter seasons of 2020–2022, they were grown without stress and in good health in the experimental field at Umiam, Meghalaya. The ground radiometer was able to achieve a spectral resolution of <3 nm in the 325 to 1075 nm wavelength range. At critical growth stages, data were acquired on cloudless days in 1100 and 1300 nm, with a height of 1 m above the crop and a field of view (FOV) <25°. Crop-specific sensitive wavebands were identified and estimated 12 vegetation indices (VI) that relate to biophysical and biochemical properties for crop discrimination. The near infrared region (NIR: 700–1070 nm) was effective for crop discrimination. Of the 10 families, the mean NDVI values varied in the following descending order: Umbelliferae(0.878) > Malvaceae(0.863) > Cucurbitaceae(0.802) > Lamiaceae(0.732) > Solanaceae(0.721) > Leguminosae(0.651) > Cruciferae (0.607) > Amarantha ceae(0.604) > Zingiberaceae(0.595) > Liliaceae(0.531). Among vegetables, the highest NDVI was reported for Fenugreek (0.922) and the lowest for Broccoli (0.421). The position on the red edge (700-750 nm) varied greatly. Similarly, crop families had varying VI values for chlorophyll, carotenoid, xanthophyll and anthocyanin pigments. The ranges of these indices were defined as healthy or stressed crops for the region. From the multi-season spectral signature, a spectral crop library (CSL) for all vegetables was developed under clearly defined conditions (e.g. growth stage, time and year, soil, cultivar type, and climate). The CSL generated from the study provides a reference spectral source for spatial mapping and quantitative spectral modelling to assess crop conditions and prescribe measures. This will be a crucial step in promoting precision agriculture in the eastern Himalayas and other similar ecosystems.