Estimation of Rice Chlorophyll Content in Salt-Affected Soils Using UAV-Based Multispectral Sensing
DOI:
https://doi.org/10.23917/forgeo.16868Keywords:
Chlorophyll, Rice, Salinity, Multispectral UAV, Vegetation indicesAbstract
The decline in rice productivity in coastal areas is often associated with agricultural land salinization. Chlorophyll, as an indicator of plant health, can be monitored using remote sensing technology. This study aims to evaluate the capability of multispectral UAV imagery to estimate rice chlorophyll content and compare the effectiveness of several vegetation indices in saline coastal paddy fields. Data were collected over ±30 hectares of rice fields in Kendal Regency using a DJI Phantom 4 Multispectral UAV during the vegetative stage (30–45 days after planting). Field measurements included chlorophyll content (SPAD) and soil electrical conductivity (EC). The results showed a weak and statistically insignificant correlation between soil salinity and rice chlorophyll content (r = 0.119; p = 0.571). These findings suggest that under the specific conditions of this study, characterized by moderate salinity levels (±4.47 dS/m) and the use of rice varieties with varying degrees of tolerance (Ciherang as tolerant and IR32 as moderately tolerant), the spatial variability of chlorophyll was more strongly influenced by phenological stages and micro-environmental factors than by salinity stress. Among the evaluated indices, red-edge-based indices showed the best performance, with CIre (R² = 0.831) and NDRE (R² = 0.822) yielding the lowest estimation errors, while NDVI (R²= 0.297) was limited by spectral saturation and CIg (R²=0.415) was affected by its sensitivity to plant canopy structure. These results indicate that red-edge-based indices are highly effective for mapping chlorophyll variability. The limited impact of salinity observed in this study is likely due to the tolerance of the rice varieties and to salinity levels remaining below critical thresholds. Thus, CIre and NDRE are recommended as the most effective indices for estimating rice chlorophyll in coastal paddy fields using multispectral UAV imagery.
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