An assessment of groundwater use in irrigated agriculture using multi-spectral remote sensing

Nhamo, Luxon; Ebrahim, Girma Yimer; Mabhaudhi, Tafadzwanashe; Mpandeli, Sylvester; Magombeyi, M.; Chitakira, Munyaradzi; Magidi, James; Sibanda, Mbulisi

doi:10.1016/j.pce.2019.102810

Cited by 38 publications

(27 citation statements)

References 34 publications

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“…It is therefore evident that adaptation responses in the Olifants catchments are seen as a necessity to stabilize and increase crop yield under climatic and non-climatic stressors. The adaptive responses already perceived by farmers in the Olifants catchment are in agreement with the call for a proactive intervention, particularly, in regions faced with underdevelopment, marginalization and low adaptive capacity (Nhamo et al 2020). Farmers' perception of climate change in the Olifants catchment included their observation of increased temperature extreme, decreased rainfall, poor distribution and delay in the onset of rain, and drought during cropping period, among others.…”

Section: Discussionsupporting

confidence: 64%

“…South Africa is characterized as a water-scarce country and it is further subjected to the impacts of climate change due to its location in a semi-arid region (Nhamo et al 2020;Akanbi et al 2021). Agricultural production in the country is practised mostly under rain-fed conditions, which makes it particularly vulnerable to climate risk (Ndhleve et al 2017).…”

Section: Graphical Abstract Introductionmentioning

confidence: 99%

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Assessment of smallholder farmers’ perception and adaptation response to climate change in the Olifants catchment, South Africa

Olabanji

Davis

Ndarana

et al. 2021

Journal of Water and Climate Change

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Climate change is expected to affect the livelihood of rural farmers in South Africa particularly the smallholder farmers, due to their overwhelming dependence on rain-fed agriculture. This study examines smallholder farmers' perception of climate change, the adaptation strategies adopted and factors that influences their adaptive decisions. The unit of data collection was household interview and focus group discussion. Climate data for the Olifants catchment (1986–2015) were also collected to validate farmers' perception of climate change with actual climate trend. Data collected were analysed using descriptive statistics, Mann–Kendall trend, Sen's slope estimator and multinomial logit regression model. Results revealed that smallholder farmers are aware of climate change (98%), their perception of these changes aligns with actual meteorological data, as the Mann–Kendall test confirms a decreasing inter-annual rainfall trend (−0.172) and an increasing temperature trend (0.004). These changes in temperature and precipitation have prompted the adoption of various adaptation responses, among which the use of improved seeds, application of chemical fertilizer and changing planting dates were the most commonly practised. The main barriers to the adoption of adaptation strategies were lack of access to credit facility, market, irrigation, information about climate change and lack of extension service. The implication of this study is to provide information to policy-makers on the current adaptation responses adopted by farmers and ways in which their adaptive capacity can be improved in order to ensure food security.

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Section: Discussionsupporting

confidence: 64%

Section: Graphical Abstract Introductionmentioning

confidence: 99%

Assessment of smallholder farmers’ perception and adaptation response to climate change in the Olifants catchment, South Africa

Olabanji

Davis

Ndarana

et al. 2021

Journal of Water and Climate Change

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“…To analyse the effect of low, average, and high levels of vegetation coverage of aquifer surface on the estimation of , the percentiles of 10th, Mean and 90th of NDVI for the study aquifers were computed and utilized. The percentile of 10th, Mean, and 90th percentiles of NDVI can explain the low, average, and high levels of vegetation coverage of the aquifer surface, respectively 77 .…”

Section: Resultsmentioning

confidence: 99%

Normalized difference vegetation index as the dominant predicting factor of groundwater recharge in phreatic aquifers: case studies across Iran

Parizi

Hosseini

Ataie-Ashtiani

et al. 2020

Sci Rep

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The estimation of long-term groundwater recharge rate ($${GW}_{r}$$ GW r ) is a pre-requisite for efficient management of groundwater resources, especially for arid and semi-arid regions. Precise estimation of $${GW}_{r}$$ GW r is probably the most difficult factor of all measurements in the evaluation of GW resources, particularly in semi-arid regions in which the recharge rate is typically small and/or regions with scarce hydrogeological data. The main objective of this study is to find and assess the predicting factors of $${GW}_{r}$$ GW r at an aquifer scale. For this purpose, 325 Iran’s phreatic aquifers (61% of Iran’s aquifers) were selected based on the data availability and the effect of eight predicting factors were assessed on $${GW}_{r}$$ GW r estimation. The predicting factors considered include Normalized Difference Vegetation Index (NDVI), mean annual temperature ($$T$$ T ), the ratio of precipitation to potential evapotranspiration ($${P/ET}_{P}$$ P / E T P ), drainage density ($${D}_{d}$$ D d ), mean annual specific discharge ($${Q}_{s}$$ Q s ), Mean Slope ($$S$$ S ), Soil Moisture ($${SM}_{90}$$ SM 90 ), and population density ($${Pop}_{d}$$ Pop d ). The local and global Moran’s I index, geographically weighted regression (GWR), and two-step cluster analysis served to support the spatial analysis of the results. The eight predicting factors considered are positively correlated to $${GW}_{r}$$ GW r and the NDVI has the greatest influence followed by the $$P/{ET}_{P}$$ P / ET P and $${SM}_{90}$$ SM 90 . In the regression model, NDVI solely explained 71% of the variation in $${GW}_{r}$$ GW r , while other drivers have only a minor modification (3.6%). The results of this study provide new insight into the complex interrelationship between $${GW}_{r}$$ GW r and vegetation density indicated by the NDVI. The findings of this study can help in better estimation of $${GW}_{r}$$ GW r especially for the phreatic aquifers that the hydrogeological ground-data requisite for establishing models are scarce.

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“…Between June and October, it does not rain in the study area, and rainfed agriculture is entirely absent. Therefore, the presence of healthy and green crops signifies the presence of irrigation [38]. Thus, vegetation indices' seasonal pattern provided the foundation for separating irrigated from rainfed areas through time-series NDVI data.…”

Section: Landcover Classes and Reference Datamentioning

confidence: 99%

Application of the Random Forest Classifier to Map Irrigated Areas Using Google Earth Engine

et al. 2021

Self Cite

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Improvements in irrigated areas' classification accuracy are critical to enhance agricultural water management and inform policy and decision-making on irrigation expansion and land use planning. This is particularly relevant in water-scarce regions where there are plans to increase the land under irrigation to enhance food security, yet the actual spatial extent of current irrigation areas is unknown. This study applied a non-parametric machine learning algorithm, the random forest, to process and classify irrigated areas using images acquired by the Landsat and Sentinel satellites, for Mpumalanga Province in Africa. The classification process was automated on a big-data management platform, the Google Earth Engine (GEE), and the R-programming was used for post-processing. The normalised difference vegetation index (NDVI) was subsequently used to distinguish between irrigated and rainfed areas during 2018/19 and 2019/20 winter growing seasons. High NDVI values on cultivated land during the dry season are an indication of irrigation. The classification of cultivated areas was for 2020, but 2019 irrigated areas were also classified to assess the impact of the Covid-19 pandemic on agriculture. The comparison in irrigated areas between 2019 and 2020 facilitated an assessment of changes in irrigated areas in smallholder farming areas. The approach enhanced the classification accuracy of irrigated areas using ground-based training samples and very high-resolution images (VHRI) and fusion with existing datasets and the use of expert and local knowledge of the study area. The overall classification accuracy was 88%.

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An assessment of groundwater use in irrigated agriculture using multi-spectral remote sensing

Cited by 38 publications

References 34 publications

Assessment of smallholder farmers’ perception and adaptation response to climate change in the Olifants catchment, South Africa

Assessment of smallholder farmers’ perception and adaptation response to climate change in the Olifants catchment, South Africa

Normalized difference vegetation index as the dominant predicting factor of groundwater recharge in phreatic aquifers: case studies across Iran

Application of the Random Forest Classifier to Map Irrigated Areas Using Google Earth Engine

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