Drought Extent and Severity on Arable Lands in Romania Derived from Normalized Difference Drought Index (2001–2020)

Dobri, Radu-Vlad; Sfîcă, Lucian; Amihăesei, Vlad-Alexandru; Apostol, Liviu; Țîmpu, Simona

doi:10.3390/rs13081478

Cited by 27 publications

(17 citation statements)

References 82 publications

(113 reference statements)

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“…The aridity index is the opposite of the humidity measure [55]. The software has been created for the calculation of drought indices NDVI [56], PDSI [17,57], Effective Precipitation [58].…”

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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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“…The aridity index is the opposite of the humidity measure [55]. The software has been created for the calculation of drought indices NDVI [56], PDSI [17,57], Effective Precipitation [58].…”

mentioning

confidence: 99%

A Contemporary Review on Drought Modeling Using Machine Learning Approaches

Sundararajan

Garg²,

Srinivasan³

et al. 2021

Computer Modeling in Engineering &Amp; Sciences

View full text Add to dashboard Cite

show abstract

“…The algorithm used is a combination of the NDVI (greenness of vegetation) and NDWI (vegetation wetness). The NDDI algorithm is considered more accurate in detecting drought on agricultural land than using the NDVI or NDWI algorithms separately (Dobri et al, 2021). The NDDI transformation formula (Gu et al, 2007) is:…”

Section: Processingmentioning

confidence: 99%

“…The results of the Normalized Difference Drought Index (NDDI) transformation into a drought index are used to provide information on the dryness of agricultural land. This index is used because it is the result of calculations from the Normalized Difference Vegetation Index (NDVI) and Normalized Difference Water Index (NDWI) transformations so that it has a higher sensitivity level to drought (Dobri et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Agricultural Land Dryness Distribution Using the Normalized Difference Drought Index (NDDI) Algorithm on Landsat 8 Imagery in Eromoko, Indonesia

Mujiyo

Nurdianti

Komariah

et al. 2023

Environ. Nat. Resour. J.

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The study area, Eromoko, has agricultural land covering 79.76% of the area, which experiences drought every year, causing a decrease in crop yields. Information on agricultural land dryness is needed to reduce the impact of dryness conditions on the agricultural sector. The effect of drought can be minimized using the transformation of the Normalized Difference Drought Index (NDDI) algorithm on Landsat 8 Imagery because it is considered capable of being used for land drought analysis that is accurate and efficient in time and cost. This study created a model for estimating soil moisture with actual soil moisture as the dependent variable and NDDI as the independent variable in several agricultural land uses in Eromoko. The results showed that the estimation model could estimate soil moisture with accuracy in plantations at 85.31%, irrigated paddy fields at 75.99%, rainfed paddy fields at 76.62%, and moors at 88.48%. The dryness category in the study area is 3,314.82 ha (35% of the total area). The variability of land use greatly affects the drying conditions. Dryness conditions can be reduced by controlling the dryness factors. Mitigation efforts to maintain soil moisture include irrigation planning based on the estimation model, applying bio-mulch and organic mulch, organic fertilization, and meeting water requirements in the harvesting period.

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“…The reduction in stored volume from 2012 to 2018 highlights how the drought is a critical factor in the storage capacity of reservoirs, especially in semi-arid areas. Drought is a complex meteorological phenomenon influenced by different factors such as precipitation, soil characteristics, humidity, soil, and air temperature, wind speed, land use, and land cover (Dobri et al 2021;Benzougagh et al 2022). Under these conditions, water resources become extremely important for maintaining economic and social activities, but more robust studies of precipitation, evaporation, infiltration and other hydrological elements to local reservoir dynamics are needed.…”

Section: Figurementioning

confidence: 99%

Comparison Between Three Methods to Monitor Reservoir Extension in the Brazilian Semi-Arid Region

Carvalho

Bezerra

Alba

et al. 2023

Anu. Inst. Geociênc.

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The State of Pernambuco covers an extensive semi-arid area where the Caatinga biome dominates. This region is characterized by long periods of drought, highlighting the need for water resource optimization. This paper aimed to compare three methods to assess reservoir changes: MapBiomas' products, the Normalized Difference Water Index (NDWI), and a support vector machine (SVM) algorithm. Initially, we obtained the monthly precipitation from 1987 to 2019 and calculated the yearly accumulation. Mapbiomas, Landsat 7 ETM, and Landsat 8 OLI data from 2012-2018 were accessed and processed using the Google Earth Engine platform. We obtained the annual image with the median pixel criterion to determine the NDWI and quantify the annual reservoir area. For the supervised classification with SVM, samples from different land-use types of the study area were used to train the algorithm. From 2012 to 2018, a reservoir reduction of 63.42% was observed with MapBiomas images, 69.49% with NDWI images, and 67.69% using the SVM algorithm. The results obtained using NDWI were the most similar to those from the artificial intelligence classification, indicating that NDWI can be used to monitor the reservoir conditions.

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Drought Extent and Severity on Arable Lands in Romania Derived from Normalized Difference Drought Index (2001–2020)

Cited by 27 publications

References 82 publications

A Contemporary Review on Drought Modeling Using Machine Learning Approaches

A Contemporary Review on Drought Modeling Using Machine Learning Approaches

Agricultural Land Dryness Distribution Using the Normalized Difference Drought Index (NDDI) Algorithm on Landsat 8 Imagery in Eromoko, Indonesia

Comparison Between Three Methods to Monitor Reservoir Extension in the Brazilian Semi-Arid Region

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