Effectiveness of upscaling the vegetation temperature condition index retrieved from Landsat data using an algorithm combining the trend surface analysis and the spatial variation weight method

Bai, Xuejiao; Kang, Hongxia; Wang, P. X.; Liu, Z.

doi:10.1080/22797254.2020.1847603

Cited by 1 publication

(1 citation statement)

References 26 publications

(30 reference statements)

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“…• El Nino-Southern Oscillation (ENSO) [48] • Vegetation Condition Index (VCI) [49] • Temperature Condition Index (TCI) [50,51] • Crop Water Stress Index (CSWI) [9] • Normalized Difference Infrared Index (NDII) [9] • Weighted Difference Vegetation Index (WDVI) [9] • Perpendicular Vegetation Index (PVI) [9] • Difference Vegetation Index (DVI) [9] • Infrared percentage vegetation index (IPVI) [9] • Atmospherically Resistant Vegetation Index (ARVI) [9] (Continued) Crop Moisture Index [13] Used to predict short-term Agricultural drought. The function of the evapotranspiration anomaly and the moisture excesses in the soil.…”

Section: Drought Types Drought Indicesmentioning

confidence: 99%

A Contemporary Review on Drought Modeling Using Machine Learning Approaches

Sundararajan

Garg²,

Srinivasan³

et al. 2021

Computer Modeling in Engineering &Amp; Sciences

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Drought is the least understood natural disaster due to the complex relationship of multiple contributory factors. Its beginning and end are hard to gauge, and they can last for months or even for years. India has faced many droughts in the last few decades. Predicting future droughts is vital for framing drought management plans to sustain natural resources. The data-driven modelling for forecasting the metrological time series prediction is becoming more powerful and flexible with computational intelligence techniques. Machine learning (ML) techniques have demonstrated success in the drought prediction process and are becoming popular to predict the weather, especially the minimum temperature using backpropagation algorithms. The favourite ML techniques for weather forecasting include support vector machines (SVM), support vector regression, random forest, decision tree, logistic regression, Naive Bayes, linear regression, gradient boosting tree, k-nearest neighbours (KNN), the adaptive neuro-fuzzy inference system, the feed-forward neural networks, Markovian chain, Bayesian network, hidden Markov models, and autoregressive moving averages, evolutionary algorithms, deep learning and many more. This paper presents a recent review of the literature using ML in drought prediction, the drought indices, dataset, and performance metrics.

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