Identifying Urine Patches on Intensively Managed Grassland Using Aerial Imagery Captured From Remotely Piloted Aircraft Systems

The deposition of livestock urine and feces in grazed fields results in a sizable input of available nitrogen (N) in these soils; therefore significantly increasing potential nitrogen pollution from agricultural areas in the form of nitrous oxide (N2O), ammonia (NH3), and nitrate (NO3−). Livestock deposition events contributes to high spatial variability within the field and generate uncertainties when assessing the contribution that animal waste has on nitrogen pollution pathways. This study investigated an innovative technique for identifying the spatial coverage of urine deposition in grasslands without the need for manual soil measurements. A Remotely Piloted Aircraft System (RPAS) using a twin camera system was used to identify urine patches in a 5 ha field, which had been grazed by sheep 3 weeks previous to measurements. The imagery was processed using Agisoft Photoscan (Agisoft LLC) to produce true and false color orthomosaic imagery of the entire field. Imagery of five areas (225 m2) within the field were analyzed using a custom R script. For a total of 1,125 m2 of grassland, 12.2% of the area consisted of what was classified as urine patch. A simple up-scaling method was applied to these data to calculate N2O emissions for the entire field providing an estimate of 1.3–2.0 kg N2O-N ha−1 emissions from urine and fertilizer inputs

Segregation of ‘Hayward’ kiwifruit for storage potential using Vis-NIR spectroscopy

Kiwifruit are often harvested unripe and kept in local coolstores for extended periods of time before being marketed. Many pre-harvest factors contribute to variation in fruit quality at harvest and during coolstorage, resulting in the difficulty in segregating fruit for their storage potential. The ability to forecast storage potential, both within and between populations of fruit, could enable segregation systems to be implemented at harvest to assist with inventory decision making and improve profitability. Visible-near infrared (Vis-NIR) spectroscopy is one of the most commonly used non-destructive techniques for estimation of internal quality of kiwifruit. Whilst many previous attempts focused on instantaneous quantification of quality attributes, the objective of this work was to investigate the use of Vis-NIR spectroscopy utilised at harvest to qualitatively forecast storage potential of individual or batches of kiwifruit. Commercially sourced ‘Hayward’ kiwifruit capturing large variability of storability were measured non-destructively at harvest using Vis-NIR spectrometer, and then assessed at 75, 100, 125 and 150 days after coolstorage at 0 °C. Machine learning classification models were developed using at-harvest Vis-NIR spectral data, to segregate storability of kiwifruit into two groups based on the export FF criterion of 9.8 N. The best prediction was obtained for fruit stored at 0 °C for 125 days: approximately 54% of the soft fruit (short storability) and 79% of the good fruit (long storability) could be predicted. Further novelty of this work lies within an independent external validation using data collected from a new season. Kiwifruit were repacked at harvest based on their potential storability predicted by the developed model, with the actual post-storage performance of the same fruit assessed to evaluate model robustness. Segregation between grower lines at harvest achieved 30% reduction in soft fruit after storage. Should the model be applied in the industry to enable sequential marketing, significant costs could be saved because of reduced fruit loss, repacking and condition checking costs.fals

Remote Sensing of Pasture Quality

Worldwide, farming systems are undergoing significant changes due to economic, environmental and social drivers. Agribusinesses must increasingly deliver products specified in terms of safety, health and quality. Increasing constraints are being placed on them by the market, the community and by government to achieve a financial benefit within social and environmental limits (Dynes et al. 2003). In order to meet these goals, producers must know the quantity and quality of the inputs into their feeding systems, be able to reliably predict the products and by-products being generated, and have the skills to be able to manage their business accordingly. Easy access to accurate and objective evaluation of forage is the first key component to meeting these objectives in livestock systems (Dynes et al. 2003) and remote sensing has considerable potential to be informative and cost-effective (Pullanagari et al. 2012b)

Mapping a Cloud-Free Rice Growth Stages Using the Integration of PROBA-V and Sentinel-1 and Its Temporal Correlation with Sub-District Statistics

Monitoring rice production is essential for securing food security against climate change threats, such as drought and flood events becoming more intense and frequent. The current practice to survey an area of rice production manually and in near real-time is expensive and involves a high workload for local statisticians. Remote sensing technology with satellite-based sensors has grown in popularity in recent decades as an alternative approach, reducing the cost and time required for spatial analysis over a wide area. However, cloud-free pixels of optical imagery are required to pro-duce accurate outputs for agriculture applications. Thus, in this study, we propose an integration of optical (PROBA-V) and radar (Sentinel-1) imagery for temporal mapping of rice growth stages, including bare land, vegetative, reproductive, and ripening stages. We have built classification models for both sensors and combined them into 12-day periodical rice growth-stage maps from January 2017 to September 2018 at the sub-district level over Java Island, the top rice production area in Indonesia. The accuracy measurement was based on the test dataset and the predicted cross-correlated with monthly local statistics. The overall accuracy of the rice growth-stage model of PROBA-V was 83.87%, and the Sentinel-1 model was 71.74% with the Support Vector Machine classifier. The temporal maps were comparable with local statistics, with an average correlation between the vegetative area (remote sensing) and harvested area (local statistics) is 0.50, and lag time 89.5 days (n = 91). This result was similar to local statistics data, which correlate planting and the harvested area at 0.61, and the lag time as 90.4 days, respectively. Moreover, the cross-correlation between the predicted rice growth stage was also consistent with rice development in the area (r > 0.52, p < 0.01). This novel method is straightforward, easy to replicate and apply to other areas, and can be scaled up to the national and regional level to be used by stakeholders to support improved agricultural policies for sustainable rice production.fals

A physically informed multi-scale deep neural network for estimating foliar nitrogen concentration in vegetation

This study introduces a Physically Informed Deep Neural Network (PINN) that leverages spectral data and Radiative Transfer Model insights to improve nitrogen concentration estimation in vegetation, addressing the complexities of physical processes. Utilizing a comprehensive spectroscopy dataset from various species across dry/ground (n = 2010), leaf (n = 1512), and canopy (n = 6007) scales, the study identifies 13 spectral bands key for chlorophyll and protein quantification. Key bands at 2276 nm, 755 nm, 1526 nm, 2243 nm, and 734 nm emerged vital for accurate N% prediction. The PINN outperforms partial least squares regression and standard deep neural networks, achieving an R2 of 0.71 and an RMSE of 0.42 (%N) on an independent validation set. Results indicate dry/ground data performed best (R2 = 0.9, RMSE = 0.24 %N), with leaf and canopy data showing lower efficacy (R2 = 0.67, RMSE = 0.45 %N; R2 = 0.65, RMSE = 0.46 %N, respectively). This multi-scale approach provides insights into spectral and N% relationships, enabling precise estimation across vegetation types and facilitating the development of transferable models. The PINN offers a new avenue for analyzing remote sensing data, demonstrating the significant potential for accurate, scale-spanning N% estimation in vegetation. Further enriching our analysis, the inclusion of seasonal data significantly enhanced our model's performance in field spectroscopy, with notable improvements observed across summer, spring, autumn, and winter. This adjustment underlines the model's increased accuracy and predictive capability at the field spectroscopy scale, emphasizing the vital role of integrating environmental factors, including climatic and physiological aspects, in future research.fals

Inferring arsenic anomalies indirectly using airborne hyperspectral imaging – Implication for gold prospecting along the Rise and Shine Shear Zone in New Zealand

Well-exposed mineral deposits are scarce at a global level and presently potential mineral-rich sites are underlying vegetation cover and topsoil, which are suboptimal for direct remote sensing based exploration techniques. This study aims to implement an indirect approach to arsenic (As) distribution mapping using the surface manifestations of the subsurface geology and link it to the known gold mineralisation in the study area. Rise and Shine Shear Zone (RSSZ) in New Zealand is broadly a part of the Otago schist hosting lower to upper green-schist facies rocks manifesting mesothermal gold mineralisation. The area has several surficial geological imprints separating mineralised and non-mineralised zones, but these are dominated by topographic ruggedness, soil moisture and vegetation (mainly grass/tussock) spectra in the hyperspectral data. Initially, a band selection using Recursive Feature Elimination (RFE) was executed. The bands generated were tallied with the field and geological understanding of the area. The resultant 85 bands were then further put through Orthogonal Total Variation Component Analysis (OTVCA) to concise the information in 10 bands. OTVCA output was then classified using Random Forest classifier to map three levels of As concentration (100 ppm). The potentially high As concentration zones are likely to be related to the gold mineralisation. The geology of the area correlates with soil exposure which is captured by the classification in some parts, this increases the accuracy but also makes the classification analysis challenging.fals

Ecophysiological variables retrieval and early stress detection: insights from a synthetic spatial scaling exercise

The ability to access physiologically driven signals, such as surface temperature, photochemical reflectance index (PRI), and sun-induced chlorophyll fluorescence (SIF), through remote sensing (RS) are exciting developments for vegetation studies. Accessing this ecophysiological information requires considering processes operating at scales from the top-of-the-canopy to the photosystems, adding complexity compared to reflectance index-based approaches. To investigate the maturity and knowledge of the growing RS community in this area, COST Action CA17134 SENSECO organized a Spatial Scaling Challenge (SSC). Challenge participants were asked to retrieve four key ecophysiological variables for a field each of maize and wheat from a simulated field campaign: leaf area index (LAI), leaf chlorophyll content (Cab), maximum carboxylation rate (Vcmax,25), and non-photochemical quenching (NPQ). The simulated campaign data included hyperspectral optical, thermal and SIF imagery, together with ground sampling of the four variables. Non-parametric methods that combined multiple spectral domains and field measurements were used most often, thereby indirectly performing the top-of-the-canopy to photosystem scaling. LAI and Cab were reliably retrieved in most cases, whereas Vcmax,25 and NPQ were less accurately estimated and demanded information ancillary to RS imagery. The factors considered least by participants were the biophysical and physiological canopy vertical profiles, the spatial mismatch between RS sensors, the temporal mismatch between field sampling and RS acquisition, and measurement uncertainty. Furthermore, few participants developed NPQ maps into stress maps or provided a deeper analysis of their parameter retrievals. The SSC shows that, despite advances in statistical and physically based models, the vegetation RS community should improve how field and RS data are integrated and scaled in space and time. We expect this work will guide newcomers and support robust advances in this research field

Quantifying Vegetation Biophysical Variables from Imaging Spectroscopy Data: A Review on Retrieval Methods

An unprecedented spectroscopic data stream will soon become available with forthcoming Earth-observing satellite missions equipped with imaging spectroradiometers. This data stream will open up a vast array of opportunities to quantify a diversity of biochemical and structural vegetation properties. The processing requirements for such large data streams require reliable retrieval techniques enabling the spatiotemporally explicit quantification of biophysical variables. With the aim of preparing for this new era of Earth observation, this review summarizes the state-of-the-art retrieval methods that have been applied in experimental imaging spectroscopy studies inferring all kinds of vegetation biophysical variables. Identified retrieval methods are categorized into: (1) parametric regression, including vegetation indices, shape indices and spectral transformations; (2) nonparametric regression, including linear and nonlinear machine learning regression algorithms; (3) physically based, including inversion of radiative transfer models (RTMs) using numerical optimization and look-up table approaches; and (4) hybrid regression methods, which combine RTM simulations with machine learning regression methods. For each of these categories, an overview of widely applied methods with application to mapping vegetation properties is given. In view of processing imaging spectroscopy data, a critical aspect involves the challenge of dealing with spectral multicollinearity. The ability to provide robust estimates, retrieval uncertainties and acceptable retrieval processing speed are other important aspects in view of operational processing. Recommendations towards new-generation spectroscopy-based processing chains for operational production of biophysical variables are given