Yield loss assessment of grapes using composite drought index derived from landsat OLI and TIRS datasets

Abstract

Drought is one of the most challenging natural phenomena and occurs due to inadequate and erratic precipitation (snowfall and rainfall) as well as the effects of high temperature on agronomical and horticultural crops. Among horticultural crops, grapes suffer extensively from inadequate berry formation due to drought. Therefore, developing a comprehensive drought monitoring tool with a drought assessment system that incorporates multiple agrometeorological variables into a single drought indicator is critical for supporting growers. In this context, the objective of this research was to assess drought severity using the composite drought index (CDI) and determine the variations in grape yields in drought-affected vineyards. In this study, the CDI was developed for the years 2016–2020 by comprising five indices: the vegetation condition index (VCI), temperature condition index (TCI), deviation of NDVI from the long-term mean (NDVI DEV), normalized difference moisture index (NDMI) and precipitation condition index (PCI). Moreover, the quantitative principal component analysis (PCA) approach was used to assign a weight for each input parameter, and then the weights of all the indices were combined into one composite drought index. Finally, Bayesian regularized artificial neural networks (BRANNs) were used to evaluate the yield variation in each affected vineyard. The results were validated with the standard precipitation index (SPI) from 2016 to 2020. Moderate to severe drought was detected with the CDI across Kabul Province during 2016 and 2018. The correlation coefficient between the CDI and SPI-1 in the active growth stages (April to October) was relatively high, at 0.64. A validation was performed with the correlating yield losses, and losses of 3.4 ton/ha and 4.7 ton/ha were found in 2016 and 2018, respectively, under severe drought conditions. The findings suggested that the CDI can be used for vineyard drought detections and agricultural organization support to more precisely aid farmers during severe drought conditions in grape-growing regions based on satellite remote sensing datasets.