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Yield Assessment of Grapes in Drought Prone Areas Using Satellite Remote Sensing-based Time-Series Datasets and Machine Learning Approach

SARA TOKHI, ARAB 筑波大学 DOI:10.15068/0002006186

2023.01.17

概要

Grapes are one of the most sensitive horticultural crops to climate change effects, especially drought. Drought has a significant impact on grape yield and grapevines throughout the world. To minimize drought's impact on vineyards and support farmers’ livelihoods from micro to regional scale assessment and interventions are required. The remote sensing datasets consisting of vegetation signatures of grapevines and climatic factors can be trained using machine learning approaches to predict the long- term changes in yield assessments and weather predictions for interventions to support growers. Thus, the primary goal of this study was to develop yield assessment models and drought monitoring systems with numerous agrometeorological factors that can predict drought severity utilizing timescale satellite datasets and machine learning techniques.

First, yield prediction was performed at the micro-scale during drought-affected periods by combining satellite-derived datasets with machine learning methods. The ground reference data were collected during a field survey in the Shakardara district of Kabul Province. The satellite-based vegetation indices such as the normalized difference vegetation index (NDVI), leaf area index (LAI), and normalized difference water index (NDWI) were mapped using Landsat 8 surface reflectance images for the years 2017–2019. Furthermore, moving averages and exponential smoothing techniques was used per-pixel. In 2018, NDVI had the maximum performance (r2 = 0.79) of all the vegetative indices; however, in 2019, the LAI performance was greater than the other indices (r2 = 0.79). Artificial neural network- based machine learning showed that NDVI was the most accurate of all vegetative indices in 2017 (R= 0.94), 2018 (R = 0.95), and 2019 (R = 0.92).

Second, grape yield loss assessment was conducted in drought-affected vineyards at macro-scale using a composite drought index derived from satellite remote sensing-based time-series datasets. Since a single index is not able to predict yield loss, appropriately using a composite index is significant. The primary data were collected during a field survey in Kabul Province, Afghanistan. The composite drought index (CDI) was created for the five years (2016 to 2020) using five indices, such as vegetation condition index (VCI), temperature condition index (TCI), deviation of NDVI (NDVI DEV), normalized difference moisture index (NDMI), and precipitation condition index (PCI). Furthermore, each input parameter was given a weight using the principal component analysis (PCA) method, and the weights of all the indices were then added together to create a composite drought index. Moreover, the yield fluctuation in each damaged vineyard was assessed using Bayesian regularized artificial neural networks (BRANNs). According to the CDI, there was moderate to severe drought in Kabul Province in 2016 and 2018.The related yield losses were 3.4 tons per hectare and 4.7 tons per hectare.

Third, drought severity analysis was carried out for regional vineyard production management using satellite remote sensing and climate datasets at a regional scale. In this research, the standard vegetation index (SVI) and standardized precipitation index (SPI) for the years 2013–2021 were developed. The results showed that the most drought-affected years were 2018 and 2021. In 2018, 4785.03 ha and in 2021, 1825.83 ha were extremely affected by drought. The multi-linear regression result was better than the linear model for regional drought validation (r2 = 0.79).

Fourth, land suitability analysis was performed from micro to regional scales in drought prone areas using satellite remote sensing and multi-criteria decision analysis. In this context, the main goal of this research is to integrate bio-physical and socio-economic criteria. In this research, the same criteria were used for both micro and macro-scale analysis. However, for regional scale, the socio-economic criteria were not available. Thus, vegetation indices, topographic maps (elevation, aspect), and climatic datasets were used. Finally, a weighted overlay method based on the analytical hierarchy overlay process (AHP) for micro-to-macro scales and a fuzzy overlay method were used for regional suitability determination. Based on the results of both physical and socioeconomic suitability, 46 percent of the micro-scale sites are very suitable. However, on macro scale, highly suitable (13%) areas and on regional scale, highly suitable (23%) regions for grape production were reported.

In conclusion, the integrated models of remote sensing, GIS and machine learning were employed to realize yield variation and water stress on vineyards from micro to regional scales during drought-prone years. The generated models could be applied from micro to regional scales for grape yield prediction, yield loss, and drought severity assessment to identify less productive land. These models will assist policymakers to reduce the effects of drought and design drought-severity-based subsidy programs in drought-prone regions in order to improve farmers' livelihoods.

Keywords: Grape yield assessment, Micro-scale, Macro-scale, Regional-scale, Yield predication, Composite drought index, Regional drought distribution, Satellite remote sensing, Time-series datasets, Machine learning, Vegetation indices, Physical and socio-economic suitability, GIS

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