CERES :: Browsing by Author "Waine, Toby W."

Browsing by Author "Waine, Toby W."

Now showing 1 - 20 of 35

Open Access
An agent-based model of farmer decision making: application to shared water resources in Arid and semi-arid regions
(Elsevier, 2025-04-01) El Fartassi, Imane; Milne, Alice E.; Metcalfe, Helen; El Alami, Rafiq; Diarra, Alhousseine; Alonso-Chavez, Vasthi; Zawadzka, Joanna; Waine, Toby W.; Corstanje, Ron
The study presents an agent-based modelling framework that integrates behavioural and biophysical models to investigate shared irrigation water management in an arid region. The behavioural model simulates farmers' decisions about their water irrigation sources (dam or groundwater) and whether to continue cultivating in the face of drought. This model was parameterised using survey data. The biophysical model component quantifies the impact of water availability and irrigation sources on soil salinity accumulation and its effects on crop productivity. Applied to the Al Haouz Basin, in Morocco, the integrated model reveals several key findings: (1) Increased groundwater access through water abstraction authorization can initially boost productivity but leads to widespread salinisation and farm abandonment, particularly under climate change scenarios. (2) Scenarios with reduced dam water availability demonstrate that mixed irrigation strategies mitigate short-term productivity losses but fail to prevent long-term soil salinity issues. (3) Land abandonment is significantly influenced by the level of water abstraction authorizations, with higher abstraction leading to more severe environmental degradation and social impacts. (4) Policy scenarios reveal that there is a theoretical optimal level of groundwater abstraction that maximises productivity while minimising land abandonment and salinity build-up. These results highlight the complex trade-offs between short-term gains and long-term sustainability, emphasising the need for holistic water governance policies that balance individual and collective interests.
Open Access
The application of time-series MODIS NDVI profiles for the acquisition of crop information across Afghanistan
(Taylor and Francis, 2014-08-26) Simms, Daniel M.; Waine, Toby W.; Taylor, John C.; Juniper, Graham R.
We investigated and developed a prototype crop information system integrating 250 m Moderate Resolution Imaging Spectroradiometer (MODIS) normalized difference vegetation index (NDVI) data with other available remotely sensed imagery, field data, and knowledge as part of a wider project monitoring opium and cereal crops. NDVI profiles exhibited large geographical variations in timing, height, shape, and number of peaks, with characteristics determined by underlying crop mixes, growth cycles, and agricultural practices. MODIS pixels were typically bigger than the field sizes, but profiles were indicators of crop phenology as the growth stages of the main first-cycle crops (opium poppy and cereals) were in phase. Profiles were used to investigate crop rotations, areas of newly exploited agriculture, localized variation in land management, and environmental factors such as water availability and disease. Near-real-time tracking of the current years’ profile provided forecasts of crop growth stages, early warning of drought, and mapping of affected areas. Derived data products and bulletins provided timely crop information to the UK Government and other international stakeholders to assist the development of counter-narcotic policy, plan activity, and measure progress. Results show the potential for transferring these techniques to other agricultural systems.
Open Access
Bias correction of high-resolution regional climate model precipitation output gives the best estimates of precipitation in Himalayan catchments
(American Geophysical Union (AGU), 2019-12-14) Bannister, Daniel; Orr, Andrew; Jain, Sanjay K.; Holman, Ian P.; Momblanch, Andrea; Phillips, Tony; Adeloye, Adebayo J.; Snapir, Boris; Waine, Toby W.; Hosking, J. Scott; Allen‐Sader, Clare
The need to provide accurate estimates of precipitation over catchments in the Hindu Kush, Karakoram, and Himalaya mountain ranges for hydrological and water resource systems assessments is widely recognised, as is identifying precipitation extremes for assessing hydro‐meteorological hazards. Here, we investigate the ability of bias‐corrected Weather Research and Forecasting model output at 5 km grid spacing to reproduce the spatiotemporal variability of precipitation for the Beas and Sutlej river basins in the Himalaya, measured by 44 stations spread over the period 1980 to 2012. For the Sutlej basin, we find that the raw (uncorrected) model output generally underestimated annual, monthly, and (particularly low‐intensity) daily precipitation amounts. For the Beas basin, the model performance was better, although biases still existed. It is speculated that the cause of the dry bias over the Sutlej basin is a failure of the model to represent an early‐morning maximum in precipitation during the monsoon period, which is related to excessive precipitation falling upwind. However, applying a non‐linear bias‐correction method to the model output resulted in much better results, which were superior to precipitation estimates from reanalysis and two gridded datasets. These findings highlight the difficulty in using current gridded datasets as input for hydrological modelling in Himalayan catchments, suggesting that bias‐corrected high‐resolution regional climate model output is in fact necessary. Moreover, precipitation extremes over the Beas and Sutlej basins were considerably under‐represented in the gridded datasets, suggesting that bias‐corrected regional climate model output is also necessary for hydro‐meteorological risk assessments in Himalayan catchments.
Open Access
Deriving crop productivity indicators from satellite synthetic aperture radar to assess wheat production at field-scale.
(Cranfield University, 2021-09) Vavlas, Nikolaos-Christos; Waine, Toby W.; Richter, G. M.; Burgess, Paul J.; Meersmans, Jeroen
The deployment of high-revisit satellite-based radar sensors raises the question of whether the data collected can provide quantitative information to improve agricultural productivity. This thesis aims to develop and test mathematical algorithms to describe the dynamic backscatter of high-resolution Synthetic Aperture Radar (Sentinel-1) in order to describe the development and productivity of wheat at field-scale. A time series of the backscatter ratio (VH/VV), collected over a cropping season, could be characterised by a growth and a senescence logistic curve and related to critical phases of crop development. The curve parameters, referred to as Crop Productivity Indicators (CPIs), compared well with the crop production for three years at the Rothamsted experimental farm. The combination of different parameters (e.g. midpoints of the two curves) helped to define CPIs, such as duration, that significantly (r = 0.61, p = 0.05) correlated with measured yields. Field observations were used to understand the wheat evolution by sampling canopy characteristics across the seasons. The correlation between the samples and the CPIs showed that structural changes, like biomass increase, influence the CPIs during the growth phase, and that declining plant water content was correlated with VH/VV values during maturation. The methodology was upscaled to other farms in Hertfordshire and Norfolk. The ANOVA identified significant effects (p<0.001) of farm management, year (weather conditions) and the interaction between soil type and year on the selected CPIs. Multilinear regression models between yields and selected CPIs displayed promising predictive power (R²= 0.5) across different farms in the same year. However, these models could not explain yield differences within high-yielding farms across seasons because of the dominant effect of weather patterns on the CPIs in each year. The potential impact of the research includes estimation of yield across the landscape, phenology monitoring and indication biophysical parameters. Future work on SAR-derived CPIs should focus on improving the correlations with biophysical properties, applying of the methodology in other crops, with different soils and climates.
Open Access
Deriving wheat crop productivity indicators using Sentinel-1 time series
(MDPI, 2020-07-24) Vavlas, Nikolaos-Christos; Waine, Toby W.; Meersmans, Jeroen; Burgess, Paul J.; Fontanelli, Giacomo; Richter, Goetz M.
High-frequency Earth observation (EO) data have been shown to be effective in identifying crops and monitoring their development. The purpose of this paper is to derive quantitative indicators of crop productivity using synthetic aperture radar (SAR). This study shows that the field-specific SAR time series can be used to characterise growth and maturation periods and to estimate the performance of cereals. Winter wheat fields on the Rothamsted Research farm in Harpenden (UK) were selected for the analysis during three cropping seasons (2017 to 2019). Average SAR backscatter from Sentinel-1 satellites was extracted for each field and temporal analysis was applied to the backscatter cross-polarisation ratio (VH/VV). The calculation of the different curve parameters during the growing period involves (i) fitting of two logistic curves to the dynamics of the SAR time series, which describe timing and intensity of growth and maturation, respectively; (ii) plotting the associated first and second derivative in order to assist the determination of key stages in the crop development; and (iii) exploring the correlation matrix for the derived indicators and their predictive power for yield. The results show that the day of the year of the maximum VH/VV value was negatively correlated with yield (r = −0.56), and the duration of “full” vegetation was positively correlated with yield (r = 0.61). Significant seasonal variation in the timing of peak vegetation (p = 0.042), the midpoint of growth (p = 0.037), the duration of the growing season (p = 0.039) and yield (p = 0.016) were observed and were consistent with observations of crop phenology. Further research is required to obtain a more detailed picture of the uncertainty of the presented novel methodology, as well as its validity across a wider range of agroecosystems
Open Access
Detection of soil compaction using soil electrical conductivity
(Cranfield University, 2007-09) Krajco, Jozef; Waine, Toby W.; Godwin, R. J.
Conventional methods for soil compaction mapping, such as penetrometers, although accurate, work as stop-and-go providing point measurements. This process is both time consuming and labour intensive. On-the-go electrical Conductivity (EC) measurements such as electromagnetic induction (e.g. EM38) are affected by key soil properties including texture, moisture content and compaction, so offer a possible rapid alternative for compaction detection. Therefore, the aim of this work is the detection of the within-field variability of soil compaction using soil electrical conductivity for improved soil management. A methodology for identification of within-field variability and for comparison of the data collected by contact and contact-less EC sensors, soil compaction sensor (which contains of eight instrumented wedge faces attached to the leading edge of a tine) and cone penetrometer was developed and a randomised block design experiment was performed. The data was evaluated statistically, the maps of spatial variability were created and the areas for targeted soil loosening were determined. A key finding was the development of application maps for targeted soil loosening, based on soil electrical conductivity measurements. The practical utilisation of this method assumes the presence of two maps that would be compared. The initial map has to be created for the soil in desirable loosened conditions and will be used as a standard for further comparisons. Further map will characterize the conditions of the compacted soil. It is recommended that both maps would be obtained in similar conditions to minimize the effect of soil moisture. The application map can be then created either manually using visual comparison of both initial (loosened soil condition) and further map (compacted areas), or using an appropriate geostatistical tool, in this case a ratio of the two maps. It was found out that the EC readings collected by the Conductometer at depth range of 0-0.3 m are able to distinguish the soil areas with no compaction above 0.3 m and the soil compacted within whole profile. The EC readings obtained at depth range of 0-0.9 m can distinguish the soil zones with no compaction above 0.6 m from the rest of the field. Using the EC data obtained at both depth ranges it is possible to determine three different environments within the field: one with no compaction above 0.3 m, one with no compaction above 0.6 m and one containing the soil compacted within whole profile. The best results for determining the areas with different depths of soil compaction were obtained by soil compaction sensor and cone penetrometer. However the absolute values of soil compaction sensor were affected by small changes in soil texture. The slow data collection speed of point penetration resistance measurement technique practically limits the spatial resolution of final data set. The soil compaction sensor has to be attached on the frame of the subsoiler and although it provides precise readings, it has high energy requirement. The high labour and time requirements are the main disadvantages of penetration resistance measurements. Second most precise determination of the areas with different soil compaction presence was provided by the EC data collected by the Conductometer which is able to collect data on-the-go with much lower power requirements. EM38 operated in horizontal mode distinguished the areas with no compaction above 0.3 m and areas with soil compacted at whole profile with less precision. The same instrument operated in vertical mode was not sensitive enough to measure any differences in soil bulk density.
Open Access
Estimating field-scale soil moisture using SAR remote sensing and the COSMOS-UK network
(Cranfield University, 2021-03) Beale, John E. P.; Waine, Toby W.; Corstanje, Ronald
Soil moisture (SM) is one of the key parameters in the engineering, agronomic, geological, ecological, biological and hydrological functions of soil. Its needed to support decision making in agriculture for irrigation and trafficability, and is a key input to hydrological and meteorological models. Remote sensing has significant potential but there remains a challenge to improve the accuracy and usefulness of the data for end users. The objective of this thesis is to develop a field-scale soil moisture estimate from Sentinel-1 C-band synthetic aperture radar (SAR) data, that is sufficiently accurate to be useful to farmers and agronomists and the research community. SAR is attractive as an all-weather remote sensing solution with potential to estimate soil moisture over large scales and at high spatial resolution. But because SAR backscatter is strongly a ected by overlying vegetation and crops, and to some extent by soil surface roughness and soil texture, the process of SM retrieval from SAR is very complex. Among a number of potential solutions, one approach is to quantify the non-SM contributions by processing additional data alongside SAR data using models that are trained against in-situ SM observations. This is very resource intensive, with the results being limited in scope and accuracy by the ancillary and training data. The alternative change detection (CD) algorithm avoids the use of additional data or training sites by assuming, instead, that the e ects of soil surface roughness and vegetation are relatively static on the timescale of soil moisture variation. This can be highly e ective for estimating relative SM in many areas but not, crucially, in areas of farmland under arable cultivation where rapid changes in vegetation and soil surface roughness are common. This is mitigated in current published SM products by restricting their spatial resolution to around 1 km, averaging out the e ects of anthropogenic change over a diversity of land uses. A 1 km pixel area is an order of magnitude larger than the average field size in the UK, for example. Such products do not satisfy the needs of farmers in arable areas and do not exploit the high spatial resolution available from SAR sensors such as Sentinel-1 C-band (20 m). A key objective of this thesis is to achieve a better understanding of the capabilities of the change detection algorithm and enhance it to provide more accurate results for farming areas at field scale. Whilst achieving field-scale resolution and better accuracy from the CD approach would be significant, the results would still not very useful to farmers. The CD algorithm is not trained against ground measurements, so its output is a Soil Moisture Index (SMI) which is a relative estimate of SM within the boundaries of previous SAR measurements at each pixel. To obtain absolute values such as volumetric water content or soil moisture deficit (e.g. for irrigation management), the SMI is calibrated against the expected range of SM, often taken to be the di erence between the soil’s field capacity (FC) and permanent wilting point (PWP) predicted by the soil texture at each location. This study shows that a 10 to 20 vol.% mean absolute error may be introduced by uncertainties in selecting appropriate soil texture so the importance of using a reliable soil map is underlined. It is further shown that that the highly dynamic nature of SM in the shallow surface layer penetrated by C-band SAR (1 to 2 cm) means that the Van Genuchten model parameters,θS and θR should be used as wet and dry references to define the expected range of SM. The common practice of using FC and PWP is shown to contribute an additional 2 to 10 vol.% error. Taken together, these errors are large compared to user requirements of 4 to 5 vol.% and a typical seasonal range of 20 to 40 vol.% depending on soil texture. Validation is important for users to have confidence in the remote sensing of soil moisture from SAR. A further objective of this study was to address the issue of depth mismatch between the penetration of the SAR and the ground observations used for validation. The study concludes that C-band SAR SM estimates should not be validated directly against ground sensors at 5 to 10 cm depth. A novel validation method is proposed for validation of SAR SM estimates against simulated soil moisture profiles at 2 cm depth using a soil hydraulic model fitted to ground observations. In this thesis, the latter were obtained from the COSMOS-UK network of soil monitoring stations using Cosmic Ray Neutron Sensors with an approximately 200 m radius measurement zone and average measurement depth calculated to be around 10 cm. As a case study of using this improved method, the performance of the published Copernicus SSM soil moisture product across 13 COSMOS-UK test sites is shown to be in the range 8 to 20 vol.% mean error. It confirms, as expected, that the worst performance is in areas of arable agriculture, justifying the focus of the thesis in such areas. As a stage to achieve the key objective, an SM estimate, spatially aggregated to field boundaries, is demonstrated by the first known implementation of the CD algorithm in Google Earth Engine. It was found that the increased speckle noise at this scale is typically balanced by reductions in noise from excluded clutter sources and increased sensitivity to SM. Periodic noise due to satellite orbit geometries (ascending versus descending) remains evident but temporal smoothing was shown to be e ective against it. The performance, in terms of mean error, at field-scale varies from 8 vol.% in grass pasture to 20 vol.% in some arable fields during periods of rapid crop growth. To improve the accuracy in arable fields, a process has been developed to use multispectral remote sensing data to assign levels of confidence in the performance of the CD algorithm, to each field on every measurement date. An alternative method is proposed to achieve a more reliable soil moisture estimate by using two-dimensional interpolation using inverse distance and confidence weighting (IDCW) across a range of neighbouring fields within a local zone. By this method it is shown that, during the peak growing season, the mean absolute error in the soil moisture estimate for wheat fields is reduced from 20 vol.% to less than 5 vol.%, retaining field-scale resolution. This is the first time such levels of accuracy have been reported at field scale from Sentinel- 1 SAR without the training of a model. The method is applicable anywhere where the remote sensing data is available alongside a suitable soil texture map. The output meets the operational requirements of farmers for 5 vol.% SM accuracy and is very close to research requirements of 4 vol.%. It may be used to create a mapping product for use by farmers and agronomists using to inform decision making in near real-time. For scientists, engineers, infrastructure engineers and environmentalists the data will be valuable for research into flooding, soil erosion, ground movement and landslip.
Open Access
Evaluating management zone maps for variable rate fungicide application and selective harvest
(Elsevier, 2018-08-23) Whetton, Rebecca L.; Waine, Toby W.; Mouazen, Abdul M.
Currently the majority of crop protection approaches are based on homogeneous rate fungicide application (HRFA) over the entire field area. With the increasing pressures on fungicide applications, associated with increased environmental impact and cost, an alternative approach based on variable rate fungicide application (VRFA) and selective harvest (SH) is needed. This study was undertaken to evaluate the economic viability of adopting VRSA and SH in winter wheat and the environmental benefit in terms of chemical reduction is also discussed. High resolution data of crop canopy properties, yellow rust, fusarium head blight (FHB), soil properties and yield were subjected to k-means cluster analysis to develop management zone (MZ) maps for one field in Bedfordshire, UK. Virtual cost-benefit analysis for VRFA was performed on three fungicide application timings, namely, T1 and T2 focused on yellow rust, and T3 focused on FHB. Cost-benefit analysis was also applied to SH, which assumed different selling prices between healthy and grain downgraded due to mycotoxin infection. Results showed that in this study VRFA allowed for fungicide reductions of 22.24% at T1 and T2 and 25.93% at T3 when compared to HRFA. SH reduced the risk of market rejection due to low quality and high mycotoxin content. Gross profit of combining SH and VRFA was £83.35 per hectare per year, divided into SH £48.04 ha−1, and VRFA £8.8 ha−1 for T1 and T2 and £17.7 ha−1 for T3. Total profit when considering soil and crop scanning costs would be £66.85 ha−1 per year, which is roughly equivalent to €80 or $90 ha−1 per year. This study was restricted to a single field but demonstrates the potential of fungicide reductions and economic viability of this MZ concept.
Open Access
Evidence of collaborative opportunities to ensure long-term sustainability in African farming
(Elsevier, 2023-02-17) El Fartassi, Imane; Milne, Alice E.; El Alami, Rafiq; Rafiqi, Maryam; Hassall, Kirsty L.; Waine, Toby W.; Zawadzka, Joanna; Diarra, Alhousseine; Corstanje, Ron
Farmers face the challenge of increasing production to feed a growing population and support livelihoods, whilst also improving the sustainability and resilience of cropping systems. Understanding the key factors that influence farming management practices is crucial for determining farmers' adaptive capacity and willingness to engage in cooperative strategies. To that end, we investigated management practices that farmers adopt and the factors underlying farmers' decision-making. We also aimed to identify the constraints that impede the adoption of strategies perceived to increase farming resilience and to explore how the acceleration of technology adoption through cooperation could ensure the long-term sustainability of farming. Surveys were distributed to farming stakeholders and professionals who worked across the contrasting environments of Morocco. We used descriptive statistics and analysis by log-linear modelling to predict the importance of factors influencing farmers’ decision-making. The results show that influencing factors tended to cluster around environmental pressures, crop characteristics and water availability with social drivers playing a lesser role. Subsidies were also found to be an important factor in decision-making. Farming stakeholders generally believed that collaborative networks are likely to facilitate the adoption of sustainable agricultural practices. We conclude that farmers need both economic incentives and technical support to enhance their adaptive capacity as this can lessen the socioeconomic vulnerability inherent in arid and semi-arid regions.
Open Access
Field phenotyping for African crops: overview and perspectives
(Frontiers, 2023-10-04) Cudjoe, Daniel; Virlet, Nicolas; Castle, March; Riche, Andrew B.; Mhada, Manal; Waine, Toby W.; Mohareb, Fady; Hawkesford, Malcolm
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.
Open Access
Fusion of multi soil data for the delineation of management zones for variable rate irrigation
(Cranfield University, 2013-02) Alhwaimel, Saad Abdulaziz; Mouazen, A. M.; Waine, Toby W.
Up until now, there have been no multi-sensor approaches used to estimate available water content (AWC) in order to determine variable rate irrigation. This has been a major problem for growers adopting precision farming technologies. The aim of this project is to implement an on-line multi-sensor platform and data fusion approach for the delineation of management zones for site specific irrigation in vegetable crop production systems. This is performed by simultaneous measurement of soil moisture content (MC), organic carbon (OC), clay content (CC), plasticity index (PI) and bulk density (BD) with an on-line visible and near infrared (vis-NIR) spectroscopy sensor and a load cell attached to a subsoiler and frame, which was linked to a three-point linkage of a tractor. The soil apparent Electrical Conductivity (ECa) was separately measured with an Electromagnetic Induction (EMI) device. Measurements were carried out in three fields in Lincolnshire and one in Cambridgeshire. Vis-NIR calibration models of soil properties were developed using partial least squares (PLS) regression. A multiple linear regression analysis (MLR) and an Artificial Neural Network (ANN) was used to derive zones of water holding capacity (WHC), based on correlation between on-line measured OC, CC, PI, BD and ECa with MC. The AWC was calculated with empirical equations, as a function of clay and sand fractions. Result showed that the on-line measurement accuracy for OC and MC were good to excellent (R2=0.71-0.83 and R2=0.75-0.85, RPD=2.00-2.57 and RPD=1.94-2.10 for OC and MC, respectively). For CC and PI, the measurement accuracy (R2=0.64-0.69 and RPD=0.55-0.66 for clay content and PI) was evaluated as moderate. It was observed in the study fields, that the ECa results had a minor response to MC distribution. Furthermore, the fusion of multi-soil data to derive a WHC index with MLR and ANN resulted in successful delineation of homogeneous zones. These were divided into four different normalisation categories of low (0 – 0.25), medium (0.25 – 0.5), high (0.5 – 0.75) and very high (0.75 – 1) of WHC. Spatial similarity between WHC maps with those of CC, IP and MC was documented, and found to be in line with the literature. AWC maps calculated as a function of soil texture classes, showed spatial similarity with WHC maps. Low values of AWC were observed at zones with low WHC index and vice versa. This supports the final conclusion of this work that multi-sensor and data fusion is a useful approach to guide positions of moisture sensor and optimise the amount of water used for irrigation.
Open Access
Harvest monitoring of Kenyan tea plantations with X-band SAR
(IEEE, 2018-02-23) Snapir, Boris; Waine, Toby W.; Corstanje, Ronald; Redfern, Sally P.; De Silva, Jacquie; Kirui, Charles
Tea is an important cash crop in Kenya, grown in a climatically restricted geographic area where climatic variability is starting to affect yield productivity levels. This paper assesses the feasibility of monitoring tea growth between, but also within fields, using X-band COSMO-SkyMed SAR images (five images at VV polarization and five images at HH polarization). We detect the harvested and nonharvested areas for each field, based on the loss of interferometric coherence between two images, with an accuracy of 52% at VV polarization and 74% at HH polarization. We then implement a normalization method to isolate the scattering component related to shoot growth and eliminate the effects of moisture and local incidence angle. After normalization, we analyze the difference in backscatter between harvested and nonharvested areas. At HH polarization, our backscatter normalization reveals a small decrease (∼0.1 dB) in HH backscatter after harvest. However, this decrease is too small for monitoring shoot growth. The decrease is not clear at VV polarization. This is attributed to the predominantly horizontal orientation of the harvested leaves.
Open Access
Hyperspectral measurements of yellow rust and fasarium head blight in cereal crops: Part 2: On-line field measurement
(Elsevier, 2018-02-08) Whetton, Rebecca L.; Waine, Toby W.; Mouazen, Abdul M.
Yellow rust and fusarium head blight cause significant losses in wheat and barley yields. Mapping the spatial distribution of these two fungal diseases at high sampling resolution is essential for variable rate fungicide application (in case of yellow rust) and selective harvest (in case of fusarium head blight). This study implemented a hyperspectral line imager (spectrograph) for on-line measurement of these diseases in wheat and barley in four fields in Bedfordshire, the UK. The % coverage was assessed based on two methods, namely, infield visual assessment (IVA) and photo interpretation assessment (PIA) based on 100-point grid overlaid RGB images. The spectral data and disease assessments were subjected to partial least squares regression (PLSR) analyses with leave-one-out cross-validation. Results showed that both diseases can be measured with similar accuracy, and that the performance is better in wheat, as compared to barley. For fusarium, it was found that PIA analysis was more accurate than IVA. The prediction accuracy obtained with PIA was classified as good to moderately accurate, since residual prediction deviation (RPD) values were 2.27 for wheat and 1.56 for barley, and R2 values were 0.82 and 0.61, respectively. Similar results were obtained for yellow rust but with IVA, where model performance was classified as moderately accurate in barley (RPD = 1.67, R2 = 0.72) and good in wheat (RPD = 2.19, R2 = 0.78). It is recommended to adopt the proposed approach to map yellow rust and fusarium head blight in wheat and barley.
Open Access
Hyperspectral measurements of yellow rust and fusarium head blight in cereal crops: Part 1: Laboratory study
(Elsevier, 2017-12-11) Whetton, Rebecca L.; Hassall, Kirsty L.; Waine, Toby W.; Mouazen, Abdul M.
This paper assesses the potential use of a hyperspectral camera for measurement of yellow rust and fusarium head blight in wheat and barley canopy under laboratory conditions. Scanning of crop canopy in trays occurred between anthesis growth stage 60, and hard dough growth stage 87. Visual assessment was made at four levels, namely, at the head, at the flag leaves, at 2nd and 3rd leaves, and at the lower canopy. Partial least squares regression (PLSR) analyses were implemented separately on data captured at four growing stages to establish separate calibration models to predict the percentage coverage of yellow rust and fusarium head blight infection. Results showed that the standard deviation between 500 and 650 nm and the squared difference between 650 and 700 nm wavelengths were found to be significantly different between healthy and infected canopy particularly for yellow rust in both crops, whereas the effect of water-stress was generally found to be unimportant. The PLSR yellow rust models were of good prediction capability for 6 out of 8 growing stages, a very good prediction at early milk stage in wheat and a moderate prediction at the late milk development stage in barley. For fusarium, predictions were very good for seven growing stages and of good performance for anthesis growing stage in wheat, with best performing for the milk development stages. However, the root mean square error of predictions for yellow rust were almost half of those for fusarium, suggesting higher prediction accuracies for yellow rust measurement under laboratory conditions.
Open Access
Image segmentation for improved consistency in image-interpretation of opium poppy
(Taylor and Francis, 2016-02-18) Simms, Daniel M.; Waine, Toby W.; Taylor, John C.; Brewer, Timothy R.
The image-interpretation of opium poppy crops from very high resolution satellite imagery forms part of the annual Afghanistan opium surveys conducted by the United Nations Office on Drugs and Crime and the United States Government. We tested the effect of generalization of field delineations on the final estimates of poppy cultivation using survey data from Helmand province in 2009 and an area frame sampling approach. The sample data was reinterpreted from pan-sharpened IKONOS scenes using two increasing levels of generalization consistent with observed practice. Samples were also generated from manual labelling of image segmentation and from a digital object classification. Generalization was found to bias the cultivation estimate between 6.6% and 13.9%, which is greater than the sample error for the highest level. Object classification of image-segmented samples increased the cultivation estimate by 30.2% because of systematic labelling error. Manual labelling of image-segmented samples gave a similar estimate to the original interpretation. The research demonstrates that small changes in poppy interpretation can result in systematic differences in final estimates that are not included within confidence intervals. Segmented parcels were similar to manually digitized fields and could provide increased consistency in field delineation at a reduced cost. The results are significant for Afghanistan’s opium monitoring programmes and other surveys where sample data are collected by remote sensing.
Open Access
Improved estimates of opium cultivation in Afghanistan using imagery-based stratification
(Taylor and Francis, 2017-03-30) Simms, Daniel M.; Waine, Toby W.; Taylor, John C.
The United Nations O ce on Drugs and Crime and the US Government make extensive use of remote sensing to quantify and monitor trends in Afghanistans illicit opium production. Cultivation gures from their independent annual surveys can vary because of systematic di erences in survey methodologies relating to spectral strati cation and the addition of a pixel bu er to the agricultural area. We investigated the e ect of strati cation and bu ering on area estimates of opium poppy using SPOT5 imagery covering the main opium cultivation area of Helmand province and sample data of poppy elds interpreted from very high resolution satellite imagery. The e ect of resolution was investigated by resampling the original 10 m pixels to 20, 30 and 60 m, representing the range of available imagery. The number of strata (1, 4, 8, 13, 23, 40) and sample fraction (0.2 to 2%) used in the estimate were also investigated. Strati cation reduced the con dence interval by improving the precision of estimates. Cultivation estimates of poppy using 40 spectral strata and a sample fraction of 1.1% had a similar precision to direct expansion estimates using a 2% sample fraction. Strati ed estimates were more robust to changes in sample size and distribution. The mapping of the agricultural area had a signi cant e ect on poppy cultivation estimates in Afghanistan, where the area of total agricultural production can vary signi cantly between years. The ndings of this research explain di erences in cultivation gures of the opium monitoring programmes in Afghanistan and recommendations can be applied to improve resource monitoring in other geographic areas.
Open Access
In-channel 3D models of riverine environments for hydromorphological characterization
(MDPI, 2018-06-25) Vandrol, Jan; Rivas Casado, Monica; Blackburn, Kim; Waine, Toby W.; Leinster, Paul; Wright, Ros
Recent legislative approaches to improve the quality of rivers have resulted in the design and implementation of extensive and intensive monitoring programmes that are costly and time consuming. An important component of assessing the ecological status of a water body as required by the Water Framework Directive is characterising the hydromorphology. Recent advances in autonomous operation and the spatial coverage of monitoring systems enables more rapid 3D models of the river environment to be produced. This study presents a Structure from Motion (SfM) semi-autonomous based framework for the estimation of key reach hydromorphological measures such as water surface area, wetted water width, bank height, bank slope and bank-full width, using in-channel stereo-imagery. The framework relies on a stereo-camera that could be positioned on an autonomous boat. The proposed approach is demonstrated along three 40 m long reaches with differing hydromorphological characteristics. Results indicated that optimal stereo-camera settings need to be selected based on the river appearance. Results also indicated that the characteristics of the reach have an impact on the estimation of the hydromorphological measures; densely vegetated banks, presence of debris and sinuosity along the reach increased the overall error in hydromorphological measure estimation. The results obtained highlight a potential way forward towards the autonomous monitoring of freshwater ecosystems
Open Access
An investigation into the maintenance of a third generation artificial rugby surface
(Cranfield University, 2013-01) Wellings, Greg; Waine, Toby W.
The endorsement of artificial turf for rugby by the sport’s governing bodies has seen a proliferation in facilities from grass roots to professional level. Maintenance of these facilities has become an important factor for discussion within the sports turf industry with organisations emphasising the need for well resourced, regular maintenance so that product quality and longevity may be maximised. Current knowledge and research has largely focussed on materials behaviour and the interaction of the carpet system with key playing parameters such as traction at the shoe/surface interface and surface hardness. There is comparatively little in the way of research, monitoring the longer term effects of maintenance on in-situ facilities. This research project aimed to monitor the surface quality and condition of a third generation artificial rugby pitch over an eighteen month period. A field survey was implemented to monitor key surface parameters defined in the governing body’s regulations. The results show significant differences in mean values obtained across areas of the pitch. The results assess the efficacy of maintenance operations and provide an insight into the effect of climatic conditions on surface performance and maintenance.
Open Access
Mapping agricultural land in Afghanistan’s opium provinces using a generalised deep learning model and medium resolution satellite imagery
(MDPI, 2023-09-26) Simms, Daniel M.; Hamer, Alex M.; Zeiler, Irmgard; Vita, Lorenzo; Waine, Toby W.
Understanding the relationship between land use and opium production is critical for monitoring the dynamics of poppy cultivation and developing an effective counter narcotics policy in Afghanistan. However, mapping agricultural land accurately and rapidly is challenging, as current methods require resource-intensive and time consuming manual image-interpretation. Deep convolutional neural nets have been shown to greatly reduce the manual effort in mapping agriculture from satellite imagery but require large amounts of densely labelled training data for model training. Here we develop a generalised model using past images and labels from different medium resolution satellite sensors for fully automatic agricultural land classification using the latest medium resolution satellite imagery. The model (FCN-8) is first trained on Disaster Monitoring Constellation (DMC) satellite images from 2007 to 2009. The effect of shape, texture and spectral features on model performance are investigated along with normalisation in order to standardise input medium resolution imagery from DMC, Landsat-5, Landsat-8, and Sentinel-2 for transfer learning between sensors and across years. Textural features make the highest contribution to overall accuracy (∼73%) while the effect of shape is minimal. The model accuracy on new images, with no additional training, is comparable to visual image interpretation (overall > 95%, user accuracy > 91%, producer accuracy > 85%, and frequency weighted intersection over union > 67%). The model is robust and was used to map agriculture from archive images (1990) and can be used in other areas with similar landscapes. The model can be updated by fine tuning using smaller, sparsely labelled datasets in the future. The generalised model was used to map the change in agricultural area in Helmand Province, showing the expansion of agricultural land into former desert areas. Training generalised deep learning models using data from both new and long-term EO programmes, with little or no requirement for fine tuning, is an exciting opportunity for automating image classification across datasets and through time that can improve our understanding of the environment.
Open Access
Mapping agricultural land in support of opium monitoring in Afghanistan with Convolutional Neural Networks (CNNs).
(Cranfield University, 2021-12) Hamer, Alex Matthew; Waine, Toby W.; Simms, Daniel M.
This work investigates the use of advanced image classification techniques for improving the accuracy and efficiency in determining agricultural areas from satellite images. The United Nations Office on Drugs and Crime (UNODC) need to accurately delineate the potential area under opium cultivation as part of their opium monitoring programme in Afghanistan. They currently use unsupervised image classification, but this is unable to separate some areas of agriculture from natural vegetation and requires time-consuming manual editing. This is a significant task as each image must be classified and interpreted separately. The aim of this research is to derive information about annual changes in land-use related to opium cultivation using convolutional neural networks with Earth observation data. Supervised machine learning techniques were investigated for agricultural land classification using training data from existing manual interpretations. Although pixel-based machine learning techniques achieved high overall classification accuracy (89%) they had difficulty separating between agriculture and natural vegetation at some locations. Convolutional Neural Networks (CNNs) have achieved ground-breaking performance in computer vision applications. They use localised image features and offer transfer learning to overcome the limitations of pixel-based methods. There are challenges related to training CNNs for land cover classification because of underlying radiometric and temporal variations in satellite image datasets. Optimisation of CNNs with a targeted sampling strategy focused on areas of known confusion (agricultural boundaries and natural vegetation). The results showed an improved overall classification accuracy of +6%. Localised differences in agricultural mapping were identified using a new tool called ‘localised intersection over union’. This provides greater insight than commonly used assessment techniques (overall accuracy and kappa statistic), that are not suitable for comparing smaller differences in mapping accuracy. A generalised fully convolutional model (FCN) was developed and evaluated using six years of data and transfer learning. Image datasets were standardised across image dates and different sensors (DMC, Landsat, and Sentinel-2), achieving high classification accuracy (up to 95%) with no additional training. Further fine-tuning with minimal training data and a targeted training strategy further increased model performance between years (up to +5%). The annual changes in agricultural area from 2010 to 2019 were mapped using the generalised FCN model in Helmand Province, Afghanistan. This provided new insight into the expansion of agriculture into marginal areas in response to counter-narcotic and alternative livelihoods policy. New areas of cultivation were found to contribute to the expansion of opium cultivation in Helmand Province. The approach demonstrates the use of FCNs for fully automated land cover classification. They are fast and efficient, can be used to classify satellite imagery from different sensors and can be continually refined using transfer learning. The proposed method overcomes the manual effort associated with mapping agricultural areas within the opium survey while improving accuracy. These findings have wider implications for improving land cover classification using legacy data on scalable cloud-based platforms.