Review of In-Situ and Remote Sensing-Based Indices and Their Applicability for Integrated Drought Monitoring in South Africa

Mukhawana, Mxolisi B.; Kahler, David M.

doi:10.3390/w15020240

Cited by 11 publications

(18 citation statements)

References 70 publications

(204 reference statements)

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“…There are two types of connections that drought parameters can show: linear and nonlinear [24,25]. Drought frequency and intensity have been effectively proved by the probability density functions (PDFs) of drought indices [26,27,28].…”

Section: Objectivesmentioning

confidence: 99%

Drought Analysis and Forecasting in Odisha using Machine Learning Techniques

Neena Uthaman

2024

cana

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Drought is a natural phenomenon that damages agricultural land severely. The severity of drought must be reduced to decrease its impact on agricultural productivity. The study of drought was carried out for the state Odisha which experienced drought 8 times during the last 20 years due to failure of monsoon. Analysis for the data was explored by explorative analysis.The drought forecasting was carried out using machine learning techniques like the Auto-regressive model (AR), Long Short-Term Memory (LSTM), and Auto-regressive Integrated Moving Average (ARIMA) using daily rainfall data collected for 28 years (1993-2020). Further using this data each district was categorised into four different categories namely Flood (FL), No Drought (ND), Moderate Drought (MD), and Severe Drought (SD). To classify the districts after forecasting, classification models were used like Support Vector Classifier (SVC) and Naïve Bayes. The results of the forecasting model as well as the classification model were compared. It becomes important to forecast drought for proper planning and management of the water resource system to decrease the damage due to such calamities. This study is valuable for the government, farmers, and other stakeholders to understand the pattern and reason behind the severity of drought to take relevant precautionary measures and improve decisions and facilities to tackle such natural calamities.

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Section: Objectivesmentioning

confidence: 99%

Drought Analysis and Forecasting in Odisha using Machine Learning Techniques

Neena Uthaman

2024

cana

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“…Field phenotyping using modern high-throughput infrastructures and precision agriculture techniques is better developed in South Africa compared to other countries on the continent ( Nyaga et al., 2021 ; Mukhawana et al., 2023 ). Some research groups are making efforts in championing field phenotyping and precision agriculture through workshops and implementation of UAV remote sensing applications and other approaches for agricultural monitoring (stress detection, nutrient and irrigation management) ( https://www.fabinet.up.ac.za/index.php/research-groups/remote-sensing ).…”

Section: Overview Of the Status Of Field Phenotyping In Africamentioning

confidence: 99%

Field phenotyping for African crops: overview and perspectives

Cudjoe,

Virlet,

Castle

et al. 2023

Front. Plant Sci.

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Improvements in crop productivity are required to meet the dietary demands of the rapidly-increasing African population. The development of key staple crop cultivars that are high-yielding and resilient to biotic and abiotic stresses is essential. To contribute to this objective, high-throughput plant phenotyping approaches are important enablers for the African plant science community to measure complex quantitative phenotypes and to establish the genetic basis of agriculturally relevant traits. These advances will facilitate the screening of germplasm for optimum performance and adaptation to low-input agriculture and resource-constrained environments. Increasing the capacity to investigate plant function and structure through non-invasive technologies is an effective strategy to aid plant breeding and additionally may contribute to precision agriculture. However, despite the significant global advances in basic knowledge and sensor technology for plant phenotyping, Africa still lags behind in the development and implementation of these systems due to several practical, financial, geographical and political barriers. Currently, field phenotyping is mostly carried out by manual methods that are prone to error, costly, labor-intensive and may come with adverse economic implications. Therefore, improvements in advanced field phenotyping capabilities and appropriate implementation are key factors for success in modern breeding and agricultural monitoring. In this review, we provide an overview of the current state of field phenotyping and the challenges limiting its implementation in some African countries. We suggest that the lack of appropriate field phenotyping infrastructures is impeding the development of improved crop cultivars and will have a detrimental impact on the agricultural sector and on food security. We highlight the prospects for integrating emerging and advanced low-cost phenotyping technologies into breeding protocols and characterizing crop responses to environmental challenges in field experimentation. Finally, we explore strategies for overcoming the barriers and maximizing the full potential of emerging field phenotyping technologies in African agriculture. This review paper will open new windows and provide new perspectives for breeders and the entire plant science community in Africa.

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“…They further confirmed that this multivariate drought index captures the duration of drought reasonably well, affirming the Frank‐copula as the best‐fitted copula function for the basin. Other studies (Cheng et al., 2023; Derradji et al., 2023; Jiao et al., 2021; Mukhawana et al., 2023; Nugraha et al., 2023) categorized drought based on remote sensing approaches. Despite the importance of multivariate approaches to drought assessment (Hao & AghaKouchak, 2013), there is still a gap in the use of MSDI‐based drought indicators to provide information on drought propagation and projection.…”

Section: Introductionmentioning

confidence: 99%

Multivariate Drought Monitoring, Propagation, and Projection Using Bias‐Corrected General Circulation Models

et al. 2023

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Understanding how droughts are characterized, propagated, and projected, particularly multivariate droughts, is necessary to explain the variability and changes in drought characteristics. This study aims to understand multimodel global drought monitoring, propagation, and projection by utilizing a multivariate standardized drought index (MSDI) during the historical (1959–2014) and future (2045–2100) periods under two socioeconomic pathways SSPs (370 and 585), derived from the bias‐corrected Coupled Model Intercomparison Project Phase 6 (CMIP6). Based on the energy metrics, the multivariate bias correction method outperformed other techniques in correcting the biases in the CMIP6 drought representation. The drought indicators demonstrate distinct categories for meteorological, hydrological, and multivariate droughts. There were significant high cross correlations between Heatwave Total Length (HWTL) and MSDI in Africa and South America for all lagged times. Europe and North America generally saw the maximum MSDI drought duration (228 months) during the historical period. For future projections, Africa recorded the maximum drought duration (197 months), while Europe witnessed the minimum drought duration for SSP 370 (171 months), and North America (149 months) for SSP 585. Furthermore, during the historical period in tropical Africa, the propagation of meteorological to hydrological drought was slower during the wet months than during the dry months. Under the SSP 370 future projection, there was a shift in the long period of meteorological‐hydrological propagation from the middle and late wet months to the beginning of the wet months in tropical Africa. Therefore, tracking and projecting drought characteristics is vital for understanding the risk of drought‐related consequences.

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Review of In-Situ and Remote Sensing-Based Indices and Their Applicability for Integrated Drought Monitoring in South Africa

Cited by 11 publications

References 70 publications

Drought Analysis and Forecasting in Odisha using Machine Learning Techniques

Drought Analysis and Forecasting in Odisha using Machine Learning Techniques

Field phenotyping for African crops: overview and perspectives

Multivariate Drought Monitoring, Propagation, and Projection Using Bias‐Corrected General Circulation Models

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