Your search found 15 records
1 Nhamo, Luxon; Ebrahim, Girma Yimer; Mabhaudhi, T.; Mpandeli, S.; Magombeyi, Manuel; Chitakira, M.; Magidi, J.; Sibanda, M.. 2020. An assessment of groundwater use in irrigated agriculture using multi-spectral remote sensing. Physics and Chemistry of the Earth, 115:102810. [doi: https://doi.org/10.1016/j.pce.2019.102810]
Groundwater assessment ; Crop water use ; Irrigated farming ; Remote sensing ; Climate change ; Resilience ; Water management ; Water productivity ; Evapotranspiration ; Estimation ; Irrigated land ; Satellite imagery ; Dry season / South Africa / Limpopo / Venda-Gazankulu
(Location: IWMI HQ Call no: e-copy only Record No: H049420)
(2.23 MB)
Declining water resources in dry regions requires sustainable groundwater management as trends indicate increasing groundwater use, but without accountability. The sustainability of groundwater is uncertain, as little is known about its extent and availability, a challenge that requires a quantitative assessment of its current use. This study assessed groundwater use for irrigated agriculture in the Venda-Gazankulu area of Limpopo Province in South Africa using crop evapotranspiration and irrigated crop area derived from the normalised difference vegetation index (NDVI). Evapotranspiration data was derived from the Water Productivity through Open access of Remotely sensed Actual Evapotranspiration and Interception (WaPOR) dataset (250 m resolution), and irrigated areas were characterised using dry season NDVI data derived from Landsat 8. Field surveys were conducted for four years to assess accuracy and for post-classification correction. Daily ET for the dry season (May to September) was developed from the actual ET for the irrigated areas. The irrigated areas were overlaid on the ET map to calculate ET for only irrigated land parcels. Groundwater use during the 2015 dry period was 3627.49 billion m3 and the irrigated area during the same period was 26% of cultivated land. About 82 435 ha of cultivated area was irrigated using 44 million m3 /ha of water, compared to 186.93 million m3 /ha on a rainfed area of 237 847 ha. Groundwater management is essential for enhancing resilience in arid regions in the advent of water scarcity.
2 Nhamo, Luxon; Magidi, J.; Nyamugama, A.; Clulow, A. D.; Sibanda, M.; Chimonyo, V. G. P.; Mabhaudhi, T. 2020. Prospects of improving agricultural and water productivity through unmanned aerial vehicles. Agriculture, 10(7):256. [doi: https://doi.org/10.3390/agriculture10070256]
Agricultural productivity ; Water productivity ; Unmanned aerial vehicles ; Water management ; Plant health ; Crop yield ; Monitoring ; Vegetation index ; Remote sensing ; Evapotranspiration ; Water stress ; Irrigation scheduling ; Mapping ; Smallholders ; Farmers ; Models ; Disaster risk reduction ; Resilience ; Satellite imagery ; Cost benefit analysis
(Location: IWMI HQ Call no: e-copy only Record No: H049892)
(1.05 MB) (1.05 MB)
Unmanned Aerial Vehicles (UAVs) are an alternative to costly and time-consuming traditional methods to improve agricultural water management and crop productivity through the acquisition, processing, and analyses of high-resolution spatial and temporal crop data at field scale. UAVs mounted with multispectral and thermal cameras facilitate the monitoring of crops throughout the crop growing cycle, allowing for timely detection and intervention in case of any anomalies. The use of UAVs in smallholder agriculture is poised to ensure food security at household level and improve agricultural water management in developing countries. This review synthesises the use of UAVs in smallholder agriculture in the smallholder agriculture sector in developing countries. The review highlights the role of UAV derived normalised difference vegetation index (NDVI) in assessing crop health, evapotranspiration, water stress and disaster risk reduction. The focus is to provide more accurate statistics on irrigated areas, crop water requirements and to improve water productivity and crop yield. UAVs facilitate access to agro-meteorological information at field scale and in near real-time, important information for irrigation scheduling and other on-field decision-making. The technology improves smallholder agriculture by facilitating access to information on crop biophysical parameters in near real-time for improved preparedness and operational decision-making. Coupled with accurate meteorological data, the technology allows for precise estimations of crop water requirements and crop evapotranspiration at high spatial resolution. Timely access to crop health information helps inform operational decisions at the farm level, and thus, enhancing rural livelihoods and wellbeing.
3 Brewer, K.; Clulow, A.; Sibanda, M.; Gokool, S.; Naiken, V.; Mabhaudhi, Tafadzwanashe. 2022. Predicting the chlorophyll content of maize over phenotyping as a proxy for crop health in smallholder farming systems. Remote Sensing, 14(3):518. [doi: https://doi.org/10.3390/rs14030518]
Maize ; Chlorophylls ; Plant health ; Forecasting ; Smallholders ; Farming systems ; Precision agriculture ; Machine learning ; Unmanned aerial vehicles / South Africa / KwaZulu-Natal / Swayimani
(Location: IWMI HQ Call no: e-copy only Record No: H050903)
(6.76 MB) (6.76 MB)
Smallholder farmers depend on healthy and productive crop yields to sustain their socio-economic status and ensure livelihood security. Advances in South African precision agriculture in the form of unmanned aerial vehicles (UAVs) provide spatially explicit near-real-time information that can be used to assess crop dynamics and inform smallholder farmers. The use of UAVs with remote-sensing techniques allows for the acquisition of high spatial resolution data at various spatio-temporal planes, which is particularly useful at the scale of fields and farms. Specifically, crop chlorophyll content is assessed as it is one of the best known and reliable indicators of crop health, due to its biophysical pigment and biochemical processes that indicate plant productivity. In this regard, the study evaluated the utility of multispectral UAV imagery using the random forest machine learning algorithm to estimate the chlorophyll content of maize through the various growth stages. The results showed that the near-infrared and red-edge wavelength bands and vegetation indices derived from these wavelengths were essential for estimating chlorophyll content during the phenotyping of maize. Furthermore, the random forest model optimally estimated the chlorophyll content of maize over the various phenological stages. Particularly, maize chlorophyll was best predicted during the early reproductive, late vegetative, and early vegetative growth stages to RMSE accuracies of 40.4 µmol/m-2 , 39 µmol/m-2 , and 61.6 µmol/m-2 , respectively. The least accurate chlorophyll content results were predicted during the mid-reproductive and late reproductive growth stages to RMSE accuracies of 66.6 µmol/m-2 and 69.6 µmol/m-2 , respectively, as a consequence of a hailstorm. A resultant chlorophyll variation map of the maize growth stages captured the spatial heterogeneity of chlorophyll within the maize field. Therefore, the study’s findings demonstrate that the use of remotely sensed UAV imagery with a robust machine algorithm is a critical tool to support the decision-making and management in smallholder farms.
4 Masenyama, A.; Mutanga, O.; Dube, T.; Bangira, T.; Sibanda, M.; Mabhaudhi, T. 2022. A systematic review on the use of remote sensing technologies in quantifying grasslands ecosystem services. GIScience and Remote Sensing, 59(1):1000-1025. [doi: https://doi.org/10.1080/15481603.2022.2088652]
Grasslands ; Ecosystem services ; Remote sensing ; Technology ; Earth observation satellites ; Hydrological modelling ; Systematic reviews ; Biomass ; Leaf area index ; Canopy ; Vegetation index ; Sensors ; Water management ; Monitoring ; Machine learning ; Forecasting
(Location: IWMI HQ Call no: e-copy only Record No: H051246)
https://www.tandfonline.com/doi/pdf/10.1080/15481603.2022.2088652
https://vlibrary.iwmi.org/pdf/H051246.pdf
https://vlibrary.iwmi.org/pdf/H051246.pdf
(3.82 MB) (3.82 MB)
The last decade has seen considerable progress in scientific research on vegetation ecosystem services. While much research has focused on forests and wetlands, grasslands also provide a variety of different provisioning, supporting, cultural, and regulating services. With recent advances in remote sensing technology, there is a possibility that Earth observation data could contribute extensively to research on grassland ecosystem services. This study conducted a systematic review on progress, emerging gaps, and opportunities on the application of remote sensing technologies in quantifying all grassland ecosystem services including those that are related to water. The contribution of biomass, Leaf Area Index (LAI), and Canopy Storage Capacity (CSC) as water-related ecosystem services derived from grasslands was explored. Two hundred and twenty-two peer-reviewed articles from Web of Science, Scopus, and Institute of Electrical and Electronics Engineers were analyzed. About 39% of the studies were conducted in Asia with most of the contributions coming from China while a few studies were from the global south regions such as Southern Africa. Overall, forage provision, climate regulation, and primary production were the most researched grassland ecosystem services in the context of Earth observation data applications. About 39 Earth observation sensors were used in the literature to map grassland ecosystem services and MODIS had the highest utilization frequency. The most widely used vegetation indices for mapping general grassland ecosystem services in literature included the red and near-infrared sections of the electromagnetic spectrum. Remote sensing algorithms used within the retrieved literature include process-based models, machine learning algorithms, and multivariate techniques. For water-related grassland ecosystem services, biomass, CSC, and LAI were the most prominent proxies characterized by remotely sensed data for understanding evapotranspiration, infiltration, run-off, soil water availability, groundwater restoration and surface water balance. An understanding of such hydrological processes is crucial in providing insights on water redistribution and balance within grassland ecosystems which is important for water management.
5 Brewer, K.; Clulow, A.; Sibanda, M.; Gokool, S.; Odindi, J.; Mutanga, O.; Naiken, V.; Chimonyo, V. G. P.; Mabhaudhi, Tafadzwanashe. 2022. Estimation of maize foliar temperature and stomatal conductance as indicators of water stress based on optical and thermal imagery acquired using an Unmanned Aerial Vehicle (UAV) platform. Drones, 6(7):169. [doi: https://doi.org/10.3390/drones6070169]
Crop growth stage ; Maize ; Temperature measurement ; Stomatal conductance ; Estimation ; Water stress ; Thermal infrared imagery ; Unmanned aerial vehicles ; Machine learning ; Forecasting ; Models ; Precision agriculture ; Smallholders ; Small-scale farming ; Crop water use ; Indicators / South Africa / KwaZulu-Natal / Swayimani
(Location: IWMI HQ Call no: e-copy only Record No: H051298)
https://www.mdpi.com/2504-446X/6/7/169/pdf?version=1657704795
https://vlibrary.iwmi.org/pdf/H051298.pdf
https://vlibrary.iwmi.org/pdf/H051298.pdf
(7.44 MB) (7.44 MB)
Climatic variability and extreme weather events impact agricultural production, especially in sub-Saharan smallholder cropping systems, which are commonly rainfed. Hence, the development of early warning systems regarding moisture availability can facilitate planning, mitigate losses and optimise yields through moisture augmentation. Precision agricultural practices, facilitated by unmanned aerial vehicles (UAVs) with very high-resolution cameras, are useful for monitoring farm-scale dynamics at near-real-time and have become an important agricultural management tool. Considering these developments, we evaluated the utility of optical and thermal infrared UAV imagery, in combination with a random forest machine-learning algorithm, to estimate the maize foliar temperature and stomatal conductance as indicators of potential crop water stress and moisture content over the entire phenological cycle. The results illustrated that the thermal infrared waveband was the most influential variable during vegetative growth stages, whereas the red-edge and near-infrared derived vegetation indices were fundamental during the reproductive growth stages for both temperature and stomatal conductance. The results also suggested mild water stress during vegetative growth stages and after a hailstorm during the mid-reproductive stage. Furthermore, the random forest model optimally estimated the maize crop temperature and stomatal conductance over the various phenological stages. Specifically, maize foliar temperature was best predicted during the mid-vegetative growth stage and stomatal conductance was best predicted during the early reproductive growth stage. Resultant maps of the modelled maize growth stages captured the spatial heterogeneity of maize foliar temperature and stomatal conductance within the maize field. Overall, the findings of the study demonstrated that the use of UAV optical and thermal imagery, in concert with prediction-based machine learning, is a useful tool, available to smallholder farmers to help them make informed management decisions that include the optimal implementation of irrigation schedules.
6 Mugiyo, H.; Chimonyo, V. G. P.; Kunz, R.; Sibanda, M.; Nhamo, L.; Masemola, C. R.; Modi, A. T.; Mabhaudhi, Tafadzwanashe. 2022. Mapping the spatial distribution of underutilised crop species under climate change using the MaxEnt model: a case of KwaZulu-Natal, South Africa. Climate Services, 28:100330. [doi: https://doi.org/10.1016/j.cliser.2022.100330]
Crop production ; Underutilized species ; Spatial distribution ; Climate change adaptation ; Food security ; Nutrition security ; Sorghum ; Cowpeas ; Amaranthus ; Taro ; Machine learning ; Models ; Forecasting / South Africa / KwaZulu-Natal
(Location: IWMI HQ Call no: e-copy only Record No: H051487)
https://www.sciencedirect.com/science/article/pii/S2405880722000486/pdfft?md5=6c4991c59d70a740431da7b2b337a330&pid=1-s2.0-S2405880722000486-main.pdf
https://vlibrary.iwmi.org/pdf/H051487.pdf
https://vlibrary.iwmi.org/pdf/H051487.pdf
(9.38 MB) (9.38 MB)
Knowing the spatial and temporal suitability of neglected and underutilised crop species (NUS) is important for fitting them into marginal production areas and cropping systems under climate change. The current study used climate change scenarios to map the future distribution of selected NUS, namely, sorghum (Sorghum bicolor), cowpea (Vigna unguiculata), amaranth (Amaranthus) and taro (Colocasia esculenta) in the KwaZulu-Natal (KZN) province, South Africa. The future distribution of NUS was simulated using a maximum entropy (MaxEnt) model using regional circulation models (RCMs) from the CORDEX archive, each driven by a different global circulation model (GCM), for the years 2030 to 2070. The study showed an increase of 0.1–11.8% under highly suitable (S1), moderately suitable (S2), and marginally suitable (S3) for sorghum, cowpea, and amaranth growing areas from 2030 to 2070 across all RCPs. In contrast, the total highly suitable area for taro production is projected to decrease by 0.3–9.78% across all RCPs. The jack-knife tests of the MaxEnt model performed efficiently, with areas under the curve being more significant than 0.8. The study identified annual precipitation, length of the growing period, and minimum and maximum temperature as variables contributing significantly to model predictions. The developed maps indicate possible changes in the future suitability of NUS within the KZN province. Understanding the future distribution of NUS is useful for developing transformative climate change adaptation strategies that consider future crop distribution. It is recommended to develop regionally differentiated climate-smart agriculture production guidelines matched to spatial and temporal variability in crop suitability.
7 Mabhaudhi, Tafadzwanashe; Bangira, T.; Sibanda, M.; Cofie, Olufunke. 2022. Use of drones to monitor water availability and quality in irrigation canals and reservoirs for improving water productivity and enhancing precision agriculture in smallholder farms. Colombo, Sri Lanka: International Water Management Institute (IWMI). CGIAR Initiative on West and Central African Food Systems Transformation. 36p.
Water availability ; Water quality ; Monitoring ; Irrigation canals ; Reservoirs ; Water productivity ; Precision agriculture ; Smallholders ; Unmanned aerial vehicles ; Imagery ; Remote sensing ; Floods ; Mapping ; Water levels ; Parameters
(Location: IWMI HQ Call no: e-copy only Record No: H051656)
(735 KB)
The report provides a methodology protocol for measuring temporal and spatial changes in water quantity and quality using drone imagery. The procedure is informed by the need for effective and sustainable water resource use to enhance water productivity under climate change. It is based on a literature review that allows the identification of appropriate processes, materials, and procedures for water monitoring, including mapping spatial and temporal dynamics of reservoirs, measurement of water quality parameters, and flood mapping of irrigation canals.
8 Gokool, S.; Mahomed, M.; Kunz, R.; Clulow, A.; Sibanda, M.; Naiken, V.; Chetty, K.; Mabhaudhi, Tafadzwanashe. 2023. Crop monitoring in smallholder farms using unmanned aerial vehicles to facilitate precision agriculture practices: a scoping review and bibliometric analysis. Sustainability, 15(4):3557. (Special issue: Advanced Technologies, Techniques and Process for the Sustainable Precision Agriculture) [doi: https://doi.org/10.3390/su15043557]
Precision agriculture ; Crop monitoring ; Smallholders ; Farming systems ; Unmanned aerial vehicles ; Bibliometric analysis ; Food security ; Machine learning ; Remote sensing ; Technology
(Location: IWMI HQ Call no: e-copy only Record No: H051762)
https://www.mdpi.com/2071-1050/15/4/3557/pdf?version=1676858967
https://vlibrary.iwmi.org/pdf/H051762.pdf
https://vlibrary.iwmi.org/pdf/H051762.pdf
(1.95 MB) (1.95 MB)
In this study, we conducted a scoping review and bibliometric analysis to evaluate the state-of-the-art regarding actual applications of unmanned aerial vehicle (UAV) technologies to guide precision agriculture (PA) practices within smallholder farms. UAVs have emerged as one of the most promising tools to monitor crops and guide PA practices to improve agricultural productivity and promote the sustainable and optimal use of critical resources. However, there is a need to understand how and for what purposes these technologies are being applied within smallholder farms. Using Biblioshiny and VOSviewer, 23 peer-reviewed articles from Scopus and Web of Science were analyzed to acquire a greater perspective on this emerging topical research focus area. The results of these investigations revealed that UAVs have largely been used for monitoring crop growth and development, guiding fertilizer management, and crop mapping but also have the potential to facilitate other PA practices. Several factors may moderate the potential of these technologies. However, due to continuous technological advancements and reductions in ownership and operational costs, there remains much cause for optimism regarding future applications of UAVs and associated technologies to inform policy, planning, and operational decision-making.
9 Buthelezi, S.; Mutanga, O.; Sibanda, M.; Odindi, J.; Clulow, A. D.; Chimonyo, V. G. P.; Mabhaudhi, Tafadzwanashe. 2023. Assessing the prospects of remote sensing maize leaf area index using UAV-derived multi-spectral data in smallholder farms across the growing season. Remote Sensing, 15(6):1597. (Special issue: Retrieving Leaf Area Index Using Remote Sensing) [doi: https://doi.org/10.3390/rs15061597]
Maize ; Leaf area index ; Vegetation index ; Remote sensing ; Unmanned aerial vehicles ; Multispectral imagery ; Small-scale farming ; Smallholders ; Growth stages ; Monitoring ; Forecasting ; Models ; Machine learning ; Agricultural productivity ; Crop yield / South Africa / KwaZulu-Natal / Swayimane
(Location: IWMI HQ Call no: e-copy only Record No: H051818)
https://www.mdpi.com/2072-4292/15/6/1597/pdf?version=1678869485
https://vlibrary.iwmi.org/pdf/H051818.pdf
https://vlibrary.iwmi.org/pdf/H051818.pdf
(3.96 MB) (3.96 MB)
Maize (Zea Mays) is one of the most valuable food crops in sub-Saharan Africa and is a critical component of local, national and regional economies. Whereas over 50% of maize production in the region is produced by smallholder farmers, spatially explicit information on smallholder farm maize production, which is necessary for optimizing productivity, remains scarce due to a lack of appropriate technologies. Maize leaf area index (LAI) is closely related to and influences its canopy physiological processes, which closely relate to its productivity. Hence, understanding maize LAI is critical in assessing maize crop productivity. Unmanned Aerial Vehicle (UAV) imagery in concert with vegetation indices (VIs) obtained at high spatial resolution provides appropriate technologies for determining maize LAI at a farm scale. Five DJI Matrice 300 UAV images were acquired during the maize growing season, and 57 vegetation indices (VIs) were generated from the derived images. Maize LAI samples were collected across the growing season, a Random Forest (RF) regression ensemble based on UAV spectral data and the collected maize LAI samples was used to estimate maize LAI. The results showed that the optimal stage for estimating maize LAI using UAV-derived VIs in concert with the RF ensemble was during the vegetative stage (V8–V10) with an RMSE of 0.15 and an R2 of 0.91 (RRMSE = 8%). The findings also showed that UAV-derived traditional, red edge-based and new VIs could reliably predict maize LAI across the growing season with an R2 of 0.89–0.93, an RMSE of 0.15–0.65 m2/m2 and an RRMSE of 8.13–19.61%. The blue, red edge and NIR sections of the electromagnetic spectrum were critical in predicting maize LAI. Furthermore, combining traditional, red edge-based and new VIs was useful in attaining high LAI estimation accuracies. These results are a step towards achieving robust, efficient and spatially explicit monitoring frameworks for sub-Saharan African smallholder farm productivity.
10 Masenyama, A.; Mutanga, O.; Dube, T.; Sibanda, M.; Odebiri, O.; Mabhaudhi, T. 2023. Inter-seasonal estimation of grass water content indicators using multisource remotely sensed data metrics and the cloud-computing Google Earth Engine platform. Applied Sciences, 13(5):3117. (Special issue: Remote Sensing Applications in Agricultural, Earth and Environmental Sciences) [doi: https://doi.org/10.3390/app13053117]
Grasslands ; Plant water relations ; Estimation ; Remote sensing ; Datasets ; Leaf area index ; Vegetation index ; Climatic factors ; Indicators ; Satellite observation ; Forecasting ; Spatial distribution ; Models / South Africa / KwaZulu-Natal / Vulindlela
(Location: IWMI HQ Call no: e-copy only Record No: H051820)
https://www.mdpi.com/2076-3417/13/5/3117/pdf?version=1677581546
https://vlibrary.iwmi.org/pdf/H051820.pdf
https://vlibrary.iwmi.org/pdf/H051820.pdf
(4.12 MB) (4.12 MB)
Indicators of grass water content (GWC) have a significant impact on eco-hydrological processes such as evapotranspiration and rainfall interception. Several site-specific factors such as seasonal precipitation, temperature, and topographic variations cause soil and ground moisture content variations, which have significant impacts on GWC. Estimating GWC using multisource data may provide robust and accurate predictions, making it a useful tool for plant water quantification and management at various landscape scales. In this study, Sentinel-2 MSI bands, spectral derivatives combined with topographic and climatic variables, were used to estimate leaf area index (LAI), canopy storage capacity (CSC), canopy water content (CWC) and equivalent water thickness (EWT) as indicators of GWC within the communal grasslands in Vulindlela across wet and dry seasons based on single-year data. The results illustrate that the use of combined spectral and topo-climatic variables, coupled with random forest (RF) in the Google Earth Engine (GEE), improved the prediction accuracies of GWC variables across wet and dry seasons. LAI was optimally estimated in the wet season with an RMSE of 0.03 m-2 and R2 of 0.83, comparable to the dry season results, which exhibited an RMSE of 0.04 m-2 and R2 of 0.90. Similarly, CSC was estimated with high accuracy in the wet season (RMSE = 0.01 mm and R2 = 0.86) when compared to the RMSE of 0.03 mm and R 2 of 0.93 obtained in the dry season. Meanwhile, for CWC, the wet season results show an RMSE of 19.42 g/m-2 and R2 of 0.76, which were lower than the accuracy of RMSE = 1.35 g/m-2 and R 2 = 0.87 obtained in the dry season. Finally, EWT was best estimated in the dry season, yielding a model accuracy of RMSE = 2.01 g/m-2 and R2 = 0.91 as compared to the wet season (RMSE = 10.75 g/m-2 and R2 = 0.65). CSC was best optimally predicted amongst all GWC variables in both seasons. The optimal variables for estimating these GWC variables included the red-edge, near-infrared region (NIR) and short-wave infrared region (SWIR) bands and spectral derivatives, as well as environmental variables such as rainfall and temperature across both seasons. The use of multisource data improved the prediction accuracies for GWC indicators across both seasons. Such information is crucial for rangeland managers in understanding GWC variations across different seasons as well as different ecological gradients.
11 Bangira, T.; Mutanga, O.; Sibanda, M.; Dube, T.; Mabhaudhi, Tafadzwanashe. 2023. Remote sensing grassland productivity attributes: a systematic review. Remote Sensing, 15(8):2043. [doi: https://doi.org/10.3390/rs15082043]
Grasslands ; Productivity ; Prediction ; Remote sensing ; Estimation ; Monitoring ; Techniques ; Ecosystem services ; Leaf area index ; Above ground biomass ; Canopy ; Chlorophylls ; Nitrogen content ; Vegetation index
(Location: IWMI HQ Call no: e-copy only Record No: H051841)
https://www.mdpi.com/2072-4292/15/8/2043/pdf?version=1681347101
https://vlibrary.iwmi.org/pdf/H051841.pdf
https://vlibrary.iwmi.org/pdf/H051841.pdf
(3.26 MB) (3.26 MB)
A third of the land on the Earth is composed of grasslands, mainly used for forage. Much effort is being conducted to develop tools to estimate grassland productivity (GP) at different extents, concentrating on spatial and seasonal variability pertaining to climate change. GP is a reliable indicator of how well an ecosystem works because of its close connection to the ecological system equilibrium. The most commonly used proxies of GP in ecological studies are aboveground biomass (AGB), leaf area index (LAI), canopy storage capacity (CSC), and chlorophyll and nitrogen content. Grassland science gains much information from the capacity of remote sensing (RS) techniques to calculate GP proxies. An overview of the studies on RS-based GP prediction techniques and a discussion of current matters determining GP monitoring are critical for improving future GP prediction performance. A systematic review of articles published between 1970 and October 2021 (203 peer-reviewed articles from Web of Science, Scopus, and ScienceDirect databases) showed a trend in the choice of the sensors, and the approaches to use are largely dependent on the extent of monitoring and assessment. Notably, all the reviewed articles demonstrate the growing demand for high-resolution sensors, such as hyperspectral scanners and computationally efficient image-processing techniques for the high prediction accuracy of GP at various scales of application. Further research is required to attract the synthesis of optical and radar data, multi-sensor data, and the selection of appropriate techniques for GP prediction at different scales. Mastering and listing major uncertainties associated with different algorithms for the GP prediction and pledging to reduce these errors are critical.
12 Abrahams, M.; Sibanda, M.; Dube, T.; Chimonyo, V. G. P.; Mabhaudhi, Tafadzwanashe. 2023. A systematic review of UAV applications for mapping neglected and underutilised crop species’ spatial distribution and health. Remote Sensing, 15(19):4672. (Special issue: Crops and Vegetation Monitoring with Remote/Proximal Sensing II) [doi: https://doi.org/10.3390/rs15194672]
Underutilized species ; Mapping ; Unmanned aerial vehicles ; Plant health ; Crop production ; Remote sensing ; Machine learning ; Food security ; Precision agriculture ; Spatial distribution ; Stomatal conductance ; Smallholders ; Farmland ; Vegetation index ; Systematic reviews
(Location: IWMI HQ Call no: e-copy only Record No: H052234)
https://www.mdpi.com/2072-4292/15/19/4672/pdf?version=1695462413
https://vlibrary.iwmi.org/pdf/H052234.pdf
https://vlibrary.iwmi.org/pdf/H052234.pdf
(11.70 MB) (11.7 MB)
Timely, accurate spatial information on the health of neglected and underutilised crop species (NUS) is critical for optimising their production and food and nutrition in developing countries. Unmanned aerial vehicles (UAVs) equipped with multispectral sensors have significantly advanced remote sensing, enabling the provision of near-real-time data for crop analysis at the plot level in small, fragmented croplands where NUS are often grown. The objective of this study was to systematically review the literature on the remote sensing (RS) of the spatial distribution and health of NUS, evaluating the progress, opportunities, challenges, and associated research gaps. This study systematically reviewed 171 peer-reviewed articles from Google Scholar, Scopus, and Web of Science using the PRISMA approach. The findings of this study showed that the United States (n = 18) and China (n = 17) were the primary study locations, with some contributions from the Global South, including southern Africa. The observed NUS crop attributes included crop yield, growth, leaf area index (LAI), above-ground biomass (AGB), and chlorophyll content. Only 29% of studies explored stomatal conductance and the spatial distribution of NUS. Twenty-one studies employed satellite-borne sensors, while only eighteen utilised UAV-borne sensors in conjunction with machine learning (ML), multivariate, and generic GIS classification techniques for mapping the spatial extent and health of NUS. The use of UAVs in mapping NUS is progressing slowly, particularly in the Global South, due to exorbitant purchasing and operational costs, as well as restrictive regulations. Subsequently, research efforts must be directed toward combining ML techniques and UAV-acquired data to monitor NUS’ spatial distribution and health to provide necessary information for optimising food production in smallholder croplands in the Global South.
13 Sibanda, M.; Ndlovu, H. S.; Brewer, K.; Buthelezi, S.; Matongera, T. N.; Mutanga, O.; Odidndi, J.; Clulow, A. D.; Chimonyo, V. G. P.; Mabhaudhi, Tafadzwanashe. 2023. Remote sensing hail damage on maize crops in smallholder farms using data acquired by remotely piloted aircraft system. Smart Agricultural Technology, 6:100325. [doi: https://doi.org/10.1016/j.atech.2023.100325]
Crop damage ; Hail damage ; Maize ; Remote sensing ; Smallholders ; Farmers ; Farmland ; Small-scale farming ; Unmanned aerial vehicles ; Plant health ; Leaf area index ; Vegetation index ; Agricultural productivity ; Climate change / South Africa / KwaZulu Natal / Swayimane
(Location: IWMI HQ Call no: e-copy only Record No: H052320)
https://www.sciencedirect.com/science/article/pii/S2772375523001545/pdfft?md5=79fa6611a7221a58090e695d915834b8&pid=1-s2.0-S2772375523001545-main.pdf
https://vlibrary.iwmi.org/pdf/H052320.pdf
https://vlibrary.iwmi.org/pdf/H052320.pdf
(17.40 MB) (17.4 MB)
Smallholder farmers reside in marginal environments typified by dryland maize-based farming systems. Despite the significant contribution of smallholder farmers to food production, they are vulnerable to extreme weather events such as hailstorms, floods and drought. Extreme weather events are expected to increase in frequency and intensity under climate change, threatening the sustainability of smallholder farming systems. Access to climate services and information, as well as digital advisories such as Robust spatially explicit monitoring techniques from remotely piloted aircraft systems (RPAS), could be instrumental in understanding the impact and extent of crop damage. It could assist in providing adequate response mechanisms suitable for bolstering crop productivity in a spatially explicit manner. This study, therefore, sought to evaluate the utility of drone-derived multispectral data in estimating crop productivity elements (Equivalent water thickness (EWT), Chlorophyll content, and leaf area index (LAI)) in maize smallholder croplands based on the random forest regression algorithm. A hailstorm occurred in the study area during the reproductive stages 2 to 3 and 3 to 4. EWT, Chlorophyll content, and LAI were measured before and after the storm. Results of this study showed that EWT, Chlorophyll content, and LAI could be optimally estimated based on the red edge and its spectral derivatives. Specifically, EWT was estimated to a rRMEs 2.7% and 59%, RMSEs of 5.31 gm- 2 and 27.35 gm-2, R2 of 0.88 and 0.77, while chlorophyll exhibited rRMSE of 28% and 25%, RMSEs of 87.4 µmol m- 2 and 76.2 µmol m- 2 and R2 of 0.89 and 0.80 and LAI yielded a rRMSE of 10.9% and 15.2%, RMSEs of 0.6 m2 /m2 and 0.19 m2 /m2 before and after the hail damage, respectively. Overall, the study underscores the potential of RPAS-based remote sensing as a valuable resource for assessing crop damage and responding to the impact of hailstorms on crop productivity in smallholder croplands. This offers a means to enhance agricultural resilience and adaptability under climate change.
14 Sibanda, M.; Buthelezi, S.; Mutanga, O.; Odindi, J.; Clulow, A. D.; Chimonyo, V. G. P.; Gokool, S.; Naiken, V.; Magidi, J.; Mabhaudhi, Tafadzwanashe. 2023. Exploring the prospects of UAV-remotely sensed data in estimating productivity of maize crops in typical smallholder farms of Southern Africa. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, X-1/W1-2023:1143-1150. (ISPRS Geospatial Week 2023, Cairo, Egypt, 2-7 September 2023) [doi: https://doi.org/10.5194/isprs-annals-X-1-W1-2023-1143-2023]
Agricultural productivity ; Small farms ; Smallholders ; Maize ; Yield forecasting ; Models ; Remote sensing ; Unmanned aerial vehicles ; Vegetation index / Southern Africa / South Africa / KwaZulu-Natal
(Location: IWMI HQ Call no: e-copy only Record No: H052490)
https://isprs-annals.copernicus.org/articles/X-1-W1-2023/1143/2023/isprs-annals-X-1-W1-2023-1143-2023.pdf
https://vlibrary.iwmi.org/pdf/H052490.pdf
https://vlibrary.iwmi.org/pdf/H052490.pdf
(1.59 MB) (1.59 MB)
This study estimated maize grain biomass, and grain biomass as a proportion of the absolute maize plant biomass using UAV-derived multispectral data. Results showed that UAV-derived data could accurately predict yield with R2 ranging from 0.80 - 0.95, RMSE ranging from 0.03 - 0.94 kg/m2 and RRMSE ranging from 2.21% - 39.91% based on the spectral datasets combined. Results of this study further revealed that the VT-R1 (56-63 days after emergence) vegetative growth stage was the most optimal stage for the early prediction of maize grain yield (R2 = 0.85, RMSE = 0.1, RRMSE = 5.08%) and proportional yield (R2 = 0.92, RMSE = 0.06, RRMSE = 17.56%), with the Normalized Difference Vegetation Index (NDVI), Enhanced Normalized Difference Vegetation Index (ENDVI), Soil Adjusted Vegetation Index (SAVI) and the red edge band being the most optimal prediction variables. The grain yield models produced more accurate results in estimating maize yield when compared to the biomass and proportional yield models. The results demonstrate the value of UAV-derived data in predicting maize yield on smallholder farms – a previously challenging task with coarse spatial resolution satellite sensors.
15 Gokool, S.; Mahomed, M.; Brewer, K.; Naiken, V.; Clulow, A.; Sibanda, M.; Mabhaudhi, Tafadzwanashe. 2024. Crop mapping in smallholder farms using unmanned aerial vehicle imagery and geospatial cloud computing infrastructure. Heliyon, 10(5):E26913. [doi: https://doi.org/10.1016/j.heliyon.2024.e26913]
Crops ; Mapping ; Unmanned aerial vehicles ; Imagery ; Machine learning ; Smallholders ; Farmers ; Land use ; Land cover / South Africa / KwaZulu-Natal / Swayimane
(Location: IWMI HQ Call no: e-copy only Record No: H052587)
https://www.cell.com/action/showPdf?pii=S2405-8440%2824%2902944-X
https://vlibrary.iwmi.org/pdf/H052587.pdf
https://vlibrary.iwmi.org/pdf/H052587.pdf
(6.41 MB) (6.41 MB)
Smallholder farms are major contributors to agricultural production, food security, and socioeconomic growth in many developing countries. However, they generally lack the resources to fully maximize their potential. Subsequently they require innovative, evidence-based and lowercost solutions to optimize their productivity. Recently, precision agricultural practices facilitated by unmanned aerial vehicles (UAVs) have gained traction in the agricultural sector and have great potential for smallholder farm applications. Furthermore, advances in geospatial cloud computing have opened new and exciting possibilities in the remote sensing arena. In light of these recent developments, the focus of this study was to explore and demonstrate the utility of using the advanced image processing capabilities of the Google Earth Engine (GEE) geospatial cloud computing platform to process and analyse a very high spatial resolution multispectral UAV image for mapping land use land cover (LULC) within smallholder farms. The results showed that LULC could be mapped at a 0.50 m spatial resolution with an overall accuracy of 91%. Overall, we found GEE to be an extremely useful platform for conducting advanced image analysis on UAV imagery and rapid communication of results. Notwithstanding the limitations of the study, the findings presented herein are quite promising and clearly demonstrate how modern agricultural practices can be implemented to facilitate improved agricultural management in smallholder farmers.
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