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Start date: 2020Subject: search.filters.subject.Remote sensing

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    Upscaling effects on infectious disease emergence risk emphasize the need for local planning in primary prevention within biodiversity hotspots
    (Springer Nature Limited, 2025-10-27) Muylaert RL; Wilkinson DA; Dwiyanti EI; Hayman DTS
    Zoonotic risk assessments are increasingly vital in the wake of recent epidemics. The microbial diversity of parasitic organisms correlates with host species richness, with regions of high biodiversity facing elevated risks of emerging zoonotic infections. While habitat loss and fragmentation reduce species diversity, anthropogenic encroachment, particularly in forested areas, amplifies human exposure to novel pathogens. This study integrates host habitat, biodiversity, human encroachment, and population at risk to estimate novel disease emergence and epidemic risk at multiple spatial scales. Using Java, Indonesia, as a case study, we demonstrate that degrading spatial resolution leads to information loss, with optimal resolutions typically below 2000 m, ideally around 500 m when native-resolution processing is unfeasible. Gravity models of epidemic spread highlight Jakarta and West Java as high-risk areas, with varying contributions from surrounding regions. Our spatial analysis underscores the influence of population centers on forest management and agroforestry practices. These findings offer valuable insights for guiding pandemic prevention research and improving pathogen- and driver-based risk monitoring strategies.
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    Enhancing grassland nitrogen estimation : a multiscale approach through optical reflectance spectroscopy and hybrid modeling techniques : a thesis presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Earth Science at Massey University, Palmerston North, New Zealand
    (Massey University, 2025-01-21) Dehghan-Shoar, Mohammad Hossain
    Optical remote sensing technology has emerged as a powerful tool for assessing vegetation characteristics, particularly nitrogen (N) concentration (N%) in heterogeneous grasslands. Accurate estimation of N% is crucial for farmers, as it directly influences grassland productivity and plays a key role in sustainable land management. Accurate N assessments optimize fertilizer use, boosting productivity, lowering costs, and enhancing environmental modeling to address impacts such as N leaching and greenhouse gas emissions. Despite significant progress, challenges and knowledge gaps remain, highlighting the need for continued research to fully harness remote sensing’s potential in agricultural management and its impact on livestock productivity. This thesis aims to advance the accurate estimation of grassland N% by integrating physically-based, empirical-statistical, and hybrid models using optical reflectance spectroscopy data. The research focuses on three primary objectives: 1. To estimate N% in grasslands using optical reflectance spectroscopy, data will be collected across multiple scales, including ground-, leaf-, canopy-, and satellite-scale observations. 2. To improve the universality and adaptability of grassland N% models through a hybrid approach that combines data from various optical sensors across multiple scales. 3. To account for and quantify uncertainties in grassland N% prediction models. The thesis addresses the challenge of uncertainty by conducting a comprehensive analysis of its sources and developing methods, such as Physically Informed Neural Networks (PINN), to account for them. Key strategies include data fusion techniques for integrating diverse data sources and improving atmospheric correction methods. A unified methodology combining empirical-statistical and physically-based approaches is proposed to enhance generalization. Machine learning algorithms play a pivotal role in feature selection and optimization, further improving model accuracy and transferability. The developed methods undergo evaluation using independent validation data collected from heterogeneous grasslands across different periods and locations. Results demonstrate that integrating physically-based and empirical-statistical approaches significantly improves model accuracy and transferability, providing a deeper understanding of the factors influencing vegetation traits. This thesis highlights the importance of advanced techniques, including machine learning, deep learning algorithms, Radiative Transfer Models (RTM), and data fusion methods, for precisely characterizing vegetation traits, contributing to more sustainable and efficient grassland management practices.
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    Monitoring and mapping rural urbanization and land use changes using Landsat data in the northeast subtropical region of Vietnam
    (National Authority for Elsevier B V on behalf of Remote Sensing and Space Sciences, 2020-04-01) Ha TV; Tuohy M; Irwin M; Tuan PV
    Rapid land use change has taken place in many neighboring provinces of the capital of Vietnam such as Thai Nguyen province over the past 2 decades due to urbanization and industrialization. Deriving accurate and updated land cover and land-use change information is essential for the environmental monitoring, evaluation and management. In this study, a robust classification algorithm, Random Forest (RF) was employed in R programming to map and monitor temporal and spatial characteristics of urban expansion and land-use change in Thai Nguyen province, Vietnam. The results showed that there has been a substantial and uneven urban growth and a significant loss of forest and cropland between 2000 and 2016. Most of the conversion of agriculture and forest into built-up and mining uses were largely detected in rural regions and suburbs of Thai Nguyen. Further GIS analysis revealed that rapid urban and industrial expansion was spatially occurred in the southern rural portions and central area of the province. This study also demonstrates the potential of Landsat data and combination of R programming language and GIS to provide a timely, accurate and economical means to map and analyze temporal land cover and land use changes for future national and local land development planning.
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    Geothermal exploration using hyperspectral and thermal remote sensing : inferring shallow hydrology of the Waiotapu Geothermal Field, New Zealand : a thesis presented in partial fulfilment of the requirements of the degree of Doctor of Philosophy in Earth Sciences at Massey University, (Manawatū Campus), Palmerston North, New Zealand
    (Massey University, 2023) Rodriguez-Gomez, Cecilia
    Geothermal areas can exhibit a series of surface manifestations (e.g. mineral alteration and deposition, thermal anomalies, hot springs, and characteristic plant species) which can be directly detected with remote sensing techniques within the visible, near-infrared, and thermal ranges. These surface manifestations are, to some extent, a reflection of the subsurface activity. There is a wealth of techniques, including geological, geophysical, and geochemical methods, which can be used to explore and monitor geothermal areas; however, remote sensing techniques from airborne and spaceborne platforms can provide a cost-effective alternative. In many cases, geothermal areas are densely covered by vegetation which can further increase the time and cost of exploration. However, vegetation has the capability to reflect the environment it lives in. Here, we propose vegetation can be utilised as a proxy for subsurface geothermal activity using a combination of hyperspectral (VNIR/SWIR), thermal infrared, and LiDAR imagery with rock/soil and plant elemental concentration values. These techniques are used in geothermal areas but have rarely been employed to analyse plants growing in the area. At Waiotapu Geothermal Field, less than 10% of the surface is directly exposed, areas where the hyperspectral airborne successfully identified three main lithologies and alteration minerals; acid-sulphate alteration, “mixed” alteration, and silica-sinter deposition. While plants cover the remaining 90% of the surface, with kanuka shrub (kunzea ericoides var. microflora) as the dominant species in soils >40 °C. As such, kanuka was selected for our investigation and four geothermally relevant elements were chosen (Ag, As, Ba, and Sb). In areas with near-neutral high-chloride springs with a significant upflow (e.g. Champagne Pool), Ag, As, and Sb are enriched in rock/soil samples and are uptaken by kanuka plants. Whereas high Ba concentrations were found in plants living in peripheral areas where water mixing is taking place. The foliar element concentration zonation maps were successfully developed through classification using Random Forest and regression with Kernel Partial Least Squares. Employing ICPMS data and laboratory, airborne, and satellite hyperspectral (VNIR/SWIR) remote sensing data to create models to predict the foliar element concentrations. The results correspond well with the geology and thermal profile of Waiotapu Geothermal Field. Additionally, thermal anomalies selected from airborne TIR broadband imagery were studied using point pattern analysis such as randomness test-statistics, to map their preferred patterns and orientation, which appear to be controlled by subsurface permeability and water flow. This research opens new opportunities for geothermal exploration and monitoring through plants using hyperspectral imaging, which can overcome the limitations of geothermal exploration methods in densely vegetated areas.
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    Towards empirically validated models of soft-rock landslides' occurrence, activity, and sediment delivery : a thesis presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Earth Science at Massey University, Manawatū, New Zealand
    (Massey University, 2023) Williams, Forrest
    Within New Zealand, soft-rock landslides present a severe hazard to infrastructure and contribute to the degradation of river systems by delivering large amounts of sediment to waterways. Updates to New Zealand’s national policy statement for freshwater management necessitate accurate accounting of freshwater sediment sources, but current sediment budget models do not account for the sediment inputs from soft-rock, and other large slow-moving landslides. To understand which factors lead to the occurrence and continued activity of these landslides and the role they play in New Zealand’s river sediment dynamics, I have completed the following objectives. (i) I have mapped large landslides within the Whanganui-Rangitikei soft-rock hill country in the North Island of New Zealand and conducted a geostatistical analysis to determine which factors control their occurrence. (ii) I have developed a novel remote sensing framework for monitoring large, slow-moving landslides that is based upon time-series Interferometric Synthetic Aperture Radar (InSAR) and time-series sub-Pixel Offset Tracking (sPOT) analyses. Furthermore, I have shown that this framework can identify large landslide activity with an accuracy of 91% and measure the movement of landslides moving with an average velocity of 2.05 m/yr with a mean absolute error of 0.74 m/yr. (iii) I have applied this framework to the landslides of the Whanganui-Rangitikei soft-rock hill country and used its results to perform a geostatistical analysis to determine which factors control a landslide’s current activity state and to estimate the total sediment mass delivered by soft-rock landslides to the rivers of this region. In total, I mapped 1057 large landslides in this region and identified 66 of them as currently active. I find that low slopes, river incision, alignment between bedding planes and slopes, and forest cover are predictive of landslide occurrence, but that low slopes and high annual precipitation rates best predict the current activity states of these landslides. I also find that soft-rock landslides contribute a 10±2% of the total sediment mass delivered to the river systems of this region. Overall, this thesis advances our understanding of why soft-rock landslides occur and provides a framework that will allow future studies to monitor these landslides at region to country-wide scales.
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    Mineral prospecting via biogeochemical signals and surface indicators using hyperspectral remote sensing : a thesis presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Earth Science at Massey University, Palmerston North, New Zealand
    (Massey University, 2022) Chakraborty, Rupsa
    Preliminary steps of mineral exploration have traditionally included drilling and other destructive, expensive, and time-consuming techniques. To meet the ever-increasing demand for mineral resources pertaining to the increase in population and technological demand, it is very important to develop environmentally friendly, faster, and cheaper prospecting methods. In this study, we have targeted three known regions of mesothermal gold mineralisation in the South Island, New Zealand to develop hyperspectral remote sensing-based prospect models combined with biogeochemical data. The three study sites have geological similarities around the gold mineralisation including the major pathfinder elements. On the contrary the environmental settings, and other surficial and near-surface processes including the soil and groundwater interactions with the host rock, are vastly different. This led to a wide variation in the physico-chemical properties of the soil cover and the subsequent uptake by the overlying vegetation. The Pinus radiata plantation at the Hyde-Macraes Shear Zone was the first study site to test the feasibility of using biogeochemical responses overlying the gold mineralisation through hyperspectral remote sensing for gold prospecting. Pinus radiata is known to be an accumulator of metals and metalloids with roots reaching as deep as the shear zone beneath it. The data showed a good spatial elemental trend along the shear zone for both the bark and the needle samples although the regression models performed much better with R2CV >0.7 for the bark samples. After confirming the feasibility of utilising the vegetation cover as a medium, the second site in the Rise and Shine Shear Zone was examined to assess the limits of the airborne hyperspectral data over variably exposed soil. The potentially high As anomalies indicating the gold mineralisation were classified coupled with a thorough understanding of the soil cover and its relation to the lithology. The orthogonal total variation component analysis transformed data produced the best-performing models using random forest classification with an accuracy ~50% for the high concentration As zonation. Finally, the third study site in Reefton exhibited a multi-species natural forest overlying the gold mineralisation. Apart from varying elemental responses among the different species the Reefton study area also manifested regions contaminated by previous mining activity which likely impacted the elemental uptake in the overlying vegetation. The regression models performed poorly but the spatial predictions rendered some valid correlations based on ground knowledge from previous studies.
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    Mapping of multitemporal rice (Oryza sativa L.) growth stages using remote sensing with multi-sensor and machine learning : a thesis dissertation presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Earth Science at Massey University, Manawatū, New Zealand
    (Massey University, 2022) Ramadhani, Fadhlullah
    Rice (Oryza Sativa) plays a pivotal role in food security for Asian countries, especially in Indonesia. Due to the increasing pressure of environmental changes, such as land use and climate, rice cultivation areas need to be monitored regularly and spatially to ensure sustainable rice production. Moreover, timely information of rice growth stages (RGS) can lead to more efficient of inputs distribution from water, seed, fertilizer, and pesticide. One of the efficient solutions for regularly mapping the rice crop is using Earth observation satellites. Moreover, the increasing availability of open access satellite images such as Landsat-8, Sentinel-1, and Sentinel-2 provides ample opportunities to map continuous and high-resolution rice growth stages with greater accuracy. The majority of the literature has focused on mapping rice area, cropping patterns and relied mainly on the phenology of vegetation. However, the mapping process of RGS was difficult to assess the accuracy, time-consuming, and depended on only one sensor. In this work, we discuss the use of machine learning algorithms (MLA) for mapping paddy RGS with multiple remote sensing data in near-real-time. The study area was Java Island, which is the primary rice producer in Indonesia. This study has investigated: (1) the mapping of RGS using Landsat-8 imagery and different MLAs, and their rigorous performance was evaluated by conducting a multitemporal analysis; (2) the temporal consistency of predicting RGS using Sentinel-2, MOD13Q1, and Sentinel-1 data; (3) evaluating the correlation of local statistics data and paddy RGS using Sentinel-2, PROBA-V, and Sentinel-1 with MLAs. The ground truth datasets were collected from multi-year web camera data (2014-2016) and three months of the field campaign in different regions of Java (2018). The study considered the RGS in the analysis to be vegetative, reproductive, ripening, bare land, and flooding, and MLAs such as support vector machines (SVMs), random forest (RF), and artificial neural network (ANN) were used. The temporal consistency matrix was used to compare the classification maps within three sensor datasets (Landsat-8 OLI, Sentinel-2, and Sentinel-2, MOD13Q1, Sentinel-1) and in four periods (5, 10, 15, 16 days). Moreover, the result of the RGS map was also compared with monthly data from local statistics within each sub-district using cross-correlation analysis. The result from the analysis shows that SVM with a radial base function outperformed the RF and ANN and proved to be a robust method for small-size datasets (< 1,000 points). Compared to Sentinel-2, Landsat-8 OLI gives less accuracy due to the lack of a red-edge band and larger pixel size (30 x 30 m). Integration of Sentinel-2, MOD13Q1, and Sentinel-1 improved the classification performance and increased the temporal availability of cloud-free maps. The integration of PROBA-V and Sentinel-1 improved the classification accuracy from the Landsat-8 result, consistent with the monthly rice planting area statistics at the sub-district level. The western area of Java has the highest accuracy and consistency since the cropping pattern only relied on rice cultivation. In contrast, less accuracy was noticed in the eastern area because of upland rice cultivation due to limited irrigation facilities and mixed cropping. In addition, the cultivation of shallots to the north of Nganjuk Regency interferes with the model predictions because the cultivation of shallots resembles the vegetative phase due to the water banks. One future research idea is the auto-detection of the cropping index in the complex landscape to be able to use it for mapping RGS on a global scale. Detection of the rice area and RGS using Google Earth Engine (GEE) can be an action plan to disseminate the information quickly on a planetary scale. Our results show that the multitemporal Sentinel-1 combined with RF can detect rice areas with high accuracy (>91%). Similarly, accurate RGS maps can be detected by integrating multiple remote sensing (Sentinel-2, Landsat-8 OLI, and MOD13Q1) data with acceptable accuracy (76.4%), with high temporal frequency and lower cloud interference (every 16 days). Overall, this study shows that remote sensing combined with the machine learning methodology can deliver information on RGS in a timely fashion, which is easy to scale up and consistent both in time and space and matches the local statistics. This thesis is also in line with the existing rice monitoring projects such as Crop Monitor, Crop Watch, AMIS, and Sen4Agri to support disseminating information over a large area. To sum up, the proposed workflow and detailed map provide a more accurate method and information in near real-time for stakeholders, such as governmental agencies against the existing mapping method. This method can be introduced to provide accurate information to rice farmers promptly with sufficient inputs such as irrigation, seeds, and fertilisers for ensuring national food security from the shifting planting time due to climate change.
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    Towards developing support tools for sustainable control of gastrointestinal nematodes in sheep : a thesis presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Veterinary Science at Massey University, Palmerston North, Manawatū, New Zealand
    (Massey University, 2021) Ikurior, Seer
    Gastrointestinal nematode (GIN) parasitism is a major animal health challenge for sheep. Parasitized animals typically display a number of clinical signs, including a reduction in voluntary feed intake, altered grazing behaviour and lethargy. The aim of this thesis was to use remote sensing technologies to advance the development of a methodology where early changes in animal behaviour can be used to help identify sheep suffering ill effects of GIN parasitism, especially in a pre-clinical situation. It was hypothesised that lambs with even modest worm burdens will be less active, graze for less time and spend more time resting than those herd mates that were less heavily parasitized. The movement and behavioural activity of young and mature, infected and uninfected sheep were monitored in a series of studies using global positioning system (GPS) and tri-axial accelerometer sensors. Key behaviours were identified using machine learning techniques. Also assessed was the influence of host genotype on movement activity. Accelerometry data accurately identified grazing, resting and walking activities of sheep. The sensors were able to identify the effects of GIN parasitism on movement and behaviour in sheep. Clear evidence was found that GIN were associated with reduced movement and overall activity in growing lambs, with reductions in time spent ‘grazing’ and ‘walking’ occurring concomitantly with increases in ‘resting’ activity, and before effects were recorded on growth rates. Host genotype also had an effect on movement activity of lambs in untreated sheep, but not in treated individuals. Adult sheep, however, showed no consistent changes in movement and behaviour associated with parasitism, as measured by faecal egg counts. Overall, the findings in this thesis have demonstrated the potential value in remote monitoring of sheep as a diagnostic marker to detect the generally subtle behavioural changes associated with changing GIN infection status. Such monitoring could therefore be used as the basis for deciding whether animals need to be treated with anthelmintic on the basis of individual need, and such decisions could be taken early, i.e. before animals have failed to grow adequately or started to manifest more overt signs of clinical illness such as weight loss.
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    Transforming scientific research and development in precision agriculture : the case of hyperspectral sensing and imaging : a thesis presented in partial fulfilment of the requirements for the degree of Doctor in Philosophy in Agriculture at Massey University, Manawatū, New Zealand
    (Massey University, 2021) Cushnahan, Megan
    There has been increasing social and academic debate in recent times surrounding the arrival of agricultural big data. Capturing and responding to real world variability is a defining objective of the rapidly evolving field of precision agriculture (PA). While data have been central to knowledge-making in the field since its inception in the 1980s, research has largely operated in a data-scarce environment, constrained by time-consuming and expensive data collection methods. While there is a rich tradition of studying scientific practice within laboratories in other fields, PA researchers have rarely been the explicit focal point of detailed empirical studies, especially in the laboratory setting. The purpose of this thesis is to contribute to new knowledge of the influence of big data technologies through an ethnographic exploration of a working PA laboratory. The researcher spent over 30 months embedded as a participant observer of a small PA laboratory, where researchers work with nascent data rich remote sensing technologies. To address the research question: “How do the characteristics of technological assemblages affect PA research and development?” the ethnographic case study systematically identifies and responds to the challenges and opportunities faced by the science team as they adapt their scientific processes and resources to refine value from a new data ecosystem. The study describes the ontological characteristics of airborne hyperspectral sensing and imaging data employed by PA researchers. Observations of the researchers at work lead to a previously undescribed shift in the science process, where effort moves from the planning and performance of the data collection stage to the data processing and analysis stage. The thesis develops an argument that changing data characteristics are central to this shift in the scientific method researchers are employing to refine knowledge and value from research projects. Importantly, the study reveals that while researchers are working in a rapidly changing environment, there is little reflection on the implications of these changes on the practice of science-making. The study also identifies a disjunction to how science is done in the field, and what is reported. We discover that the practices that provide disciplinary ways of doing science are not established in this field and moments to learn are siloed because of commercial constraints the commercial structures imposed in this case study of contemporary PA research.
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    New sensing methods for scheduling variable rate irrigation to improve water use efficiency and reduce the environmental footprint : a thesis presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Soil Science at Massey University, Palmerston North, New Zealand
    (Massey University, 2020) El-Naggar, Ahmed
    Irrigation is the largest user of allocated freshwater, so conservation of water use should begin with improving the efficiency of crop irrigation. Improved irrigation management is necessary for humid areas such as New Zealand in order to produce greater yields, overcome excessive irrigation and eliminate nitrogen losses due to accelerated leaching and/or denitrification. The impact of two different climatic regimes (Hawkes Bay, Manawatū) and soils (free and imperfect drainage) on irrigated pea (Pisum sativum., cv. ‘Ashton’) and barley (Hordeum vulgare., cv. ‘Carfields CKS1’) production was investigated. These experiments were conducted to determine whether variable-rate irrigation (VRI) was warranted. The results showed that both weather conditions and within-field soil variability had a significant effect on the irrigated pea and barley crops (pea yield - 4.15 and 1.75 t/ha; barley yield - 4.0 and 10.3 t/ha for freely and imperfectly drained soils, respectively). Given these results, soil spatial variability was characterised at precision scales using proximal sensor survey systems: to inform precision irrigation practice. Apparent soil electrical conductivity (ECa) data were collected by a Dualem-421S electromagnetic (EM) survey, and the data were kriged into a map and modelled to predict ECa to depth. The ECa depth models were related to soil moisture (θv), and the intrinsic soil differences. The method was used to guide the placement of soil moisture sensors. After quantifying precision irrigation management zones using EM technology, dynamic irrigation scheduling for a VRI system was used to efficiently irrigate a pea crop (Pisum sativum., cv. ‘Massey’) and a French bean crop (Phaseolus vulgaris., cv. ‘Contender’) over one season at the Manawatū site. The effects of two VRI scheduling methods using (i) a soil water balance model and (ii) sensors, were compared. The sensor-based technique irrigated 23–45% less water because the model-based approach overestimated drainage for the slower draining soil. There were no significant crop growth and yield differences between the two approaches, and water use efficiency (WUE) was higher under the scheduling regime based on sensors. ii To further investigate the use of sensor-based scheduling, a new method was developed to assess crop height and biomass for pea, bean and barley crops at high field resolution (0.01 m) using ground-based LiDAR (Light Detection and Ranging) data. The LiDAR multi-temporal, crop height maps can usefully improve crop coefficient estimates in soil water balance models. The results were validated against manually measured plant parameters. A critical component of soil water balance models, and of major importance for irrigation scheduling, is the estimation of crop evapotranspiration (ETc) which traditionally relies on regional climate data and default crop factors based on the day of planting. Therefore, the potential of a simpler, site-specific method for estimation of ETc using in-field crop sensors was investigated. Crop indices (NDVI, and canopy surface temperature, Tc) together with site-specific climate data were used to estimate daily crop water use at the Manawatū and Hawkes Bay sites (2017-2019). These site-specific estimates of daily crop water use were then used to evaluate a calibrated FAO-56 Penman-Monteith algorithm to estimate ETc from barley, pea and bean crops. The modified ETc–model showed a high linear correlation between measured and modelled daily ETc for barley, pea, and bean crops. This indicates the potential value of in-field crop sensing for estimating site-specific values of ETc. A model-based, decision support software system (VRI–DSS) that automates irrigation scheduling to variable soils and multiple crops was then tested at both the Manawatū and Hawkes Bay farm sites. The results showed that the virtual climate forecast models used for this study provided an adequate prediction of evapotranspiration but over predicted rainfall. However, when local data was used with the VRI–DSS system to simulate results, the soil moisture deficit showed good agreement with weekly neutron probe readings. The use of model system-based irrigation scheduling allowed two-thirds of the irrigation water to be saved for the high available water content (AWC) soil. During the season 2018 – 2019, the VRI–DSS was again used to evaluate the level of available soil water (threshold) at which irrigation should be applied to increase WUE and crop water productivity (WP) for spring wheat (Triticum aestivum L., cv. ‘Sensas’) on the sandy loam and silt loam soil zones at the Manawatū site. Two irrigation thresholds (40% and 60% AWC), were investigated in each soil zone along with a rainfed control. Soil water uptake pattern was affected mainly by the soil type rather than irrigation. The soil iii water uptake decreased with soil depth for the sandy loam whereas water was taken up uniformly from all depths of the silt loam. The 60% AWC treatments had greater irrigation water use efficiency (IWUE) than the 40% AWC treatments, indicating that irrigation scheduling using a 60% AWC trigger could be recommended for this soil-crop scenario. Overall, in this study, we have developed new sensor-based methods that can support improved spatial irrigation water management. The findings from this study led to a more beneficial use of agricultural water.