Application of Sentinel-2A data for pasture biomass monitoring using a physically based radiative transfer model

A large proportion of the global land surface is covered by pasture. The advent of the Sentinel satellites program provides free datasets with good spatiotemporal resolution that can be a valuable source of information for monitoring pasture resources. We combined optical remote sensing data (proximal hyperspectral and Sentinel 2A) with a radiative transfer model (PROSAIL) to estimate leaf area index (LAI), and biomass, in a dairy farming context. Three sites in Southern England were used: two pasture farms that differed in pasture type and management, and a set of small agronomy trial plots with different mixtures of grasses, legumes and herbs, as well as pure perennial ryegrass. The proximal and satellite spectral data were used to retrieve LAI via PROSAIL model inversion, which were compared against field observations of LAI. The potential of bands of Sentinel 2A that corresponded with a 10 m resolution was studied by convolving narrow spectral bands (from a handheld hyperspectral sensor) into Sentinel 2A bands (10 m). Retrieved LAI, using these spectrally resampled S2A data, compared well with measured LAI, for all sites, even for those with mixed species cover (although retrieved LAI was somewhat overestimated for pasture mixtures with high LAI). This proved the suitability of 10 m Sentinel 2A spectral bands for capturing LAI dynamics for different types of pastures. We also found that inclusion of 20 m bands in the inversion scheme did not lead to any further improvement in retrieved LAI. Sentinel 2A image based retrieval yielded good agreement with LAI measurements obtained for a typical perennial ryegrass based pasture farm. LAI retrieved in this way was used to create biomass maps (that correspond to indirect biomass measurements by Rising Plate Meter (RPM)), for mixed-species paddocks for a farm for which limited field data were available. These maps compared moderately well with farmer-collected RPM measurements for this farm. We propose that estimates of paddock-averaged and within-paddock variability of biomass are more reliably obtained from a combined Sentinel 2A-PROSAIL approach, rather than by manual RPM measurements. The physically based radiative transfer model inversion approach outperformed the Normalised Difference Vegetation Index based retrieval method, and does not require site specific calibrations of the inversion scheme

Comparison of Methods for Modeling Fractional Cover Using Simulated Satellite Hyperspectral Imager Spectra

Remotely sensed data can be used to model the fractional cover of green vegetation (GV), non-photosynthetic vegetation (NPV), and soil in natural and agricultural ecosystems. NPV and soil cover are difficult to estimate accurately since absorption by lignin, cellulose, and other organic molecules cannot be resolved by broadband multispectral data. A new generation of satellite hyperspectral imagers will provide contiguous narrowband coverage, enabling new, more accurate, and potentially global fractional cover products. We used six field spectroscopy datasets collected in prior experiments from sites with partial crop, grass, shrub, and low-stature resprouting tree cover to simulate satellite hyperspectral data, including sensor noise and atmospheric correction artifacts. The combined dataset was used to compare hyperspectral index-based and spectroscopic methods for estimating GV, NPV, and soil fractional cover. GV fractional cover was estimated most accurately. NPV and soil fractions were more difficult to estimate, with spectroscopic methods like partial least squares (PLS) regression, spectral feature analysis (SFA), and multiple endmember spectral mixture analysis (MESMA) typically outperforming hyperspectral indices. Using an independent validation dataset, the lowest root mean squared error (RMSE) values were 0.115 for GV using either normalized difference vegetation index (NDVI) or SFA, 0.164 for NPV using PLS, and 0.126 for soil using PLS. PLS also had the lowest RMSE averaged across all three cover types. This work highlights the need for more extensive and diverse fine spatial scale measurements of fractional cover, to improve methodologies for estimating cover in preparation for future hyperspectral global monitoring missions

Forage mass estimation in silvopastoral and full sun systems: evaluation through proximal remote sensing applied to the SAFER model.

Abstract: The operational slowness in the execution of direct methods for estimating forage mass, an important variable for defining the animal stocking rate, gave rise to the need for methods with faster responses and greater territorial coverage. In this context, the aim of this study was to evaluate a method to estimate the mass of Urochloa brizantha cv. BRS Piatã in shaded and full sun systems, through proximal sensing applied to the Simple Algorithm for Evapotranspiration Retrieving (SAFER) model, applied with the Monteith Radiation Use Efficiency (RUE) model. The study was carried out in the experimental area of Fazenda Canchim, a research center of Embrapa Pecuária Sudeste, São Carlos, SP, Brazil (21°57′S, 47°50′W, 860 m), with collections of forage mass and reflectance in the silvopastoral systems animal production and full sun. Reflectance data, as well as meteorological data obtained by a weather station installed in the study area, were used as input for the SAFER model and, later, for the radiation use efficiency model to calculate the fresh mass of forage. The forage collected in the field was sent to the laboratory, separated, weighed and dried, generating the variables of pasture total dry mass), total leaf dry mass, leaf and stalk dry mass and leaf area index. With the variables of pasture, in situ, and fresh mass, obtained from SAFER, the training regression model, in which 80% were used for training and 20% for testing the models. The SAFER was able to promisingly express the behavior of forage variables, with a significant correlation with all of them. The variables that obtained the best estimation performance model were the dry mass of leaves and stems and the dry mass of leaves in silvopastoral and full sun systems, respectively. It was concluded that the association of the SAFER model with the proximal sensor allowed us to obtain a fast, precise and accurate forage estimation method

The data concept behind the data: From metadata models and labelling schemes towards a generic spectral library

Spectral libraries play a major role in imaging spectroscopy. They are commonly used to store end-member and spectrally pure material spectra, which are primarily used for mapping or unmixing purposes. However, the development of spectral libraries is time consuming and usually sensor and site dependent. Spectral libraries are therefore often developed, used and tailored only for a specific case study and only for one sensor. Multi-sensor and multi-site use of spectral libraries is difficult and requires technical effort for adaptation, transformation, and data harmonization steps. Especially the huge amount of urban material specifications and its spectral variations hamper the setup of a complete spectral library consisting of all available urban material spectra. By a combined use of different urban spectral libraries, besides the improvement of spectral inter- and intra-class variability, missing material spectra could be considered with respect to a multi-sensor/ -site use. Publicly available spectral libraries mostly lack the metadata information that is essential for describing spectra acquisition and sampling background, and can serve to some extent as a measure of quality and reliability of the spectra and the entire library itself. In the GenLib project, a concept for a generic, multi-site and multi-sensor usable spectral library for image spectra on the urban focus was developed. This presentation will introduce a 1) unified, easy-to-understand hierarchical labeling scheme combined with 2) a comprehensive metadata concept that is 3) implemented in the SPECCHIO spectral information system to promote the setup and usability of a generic urban spectral library (GUSL). The labelling scheme was developed to ensure the translation of individual spectral libraries with their own labelling schemes and their usually varying level of details into the GUSL framework. It is based on a modified version of the EAGLE classification concept by combining land use, land cover, land characteristics and spectral characteristics. The metadata concept consists of 59 mandatory and optional attributes that are intended to specify the spatial context, spectral library information, references, accessibility, calibration, preprocessing steps, and spectra specific information describing library spectra implemented in the GUSL. It was developed on the basis of existing metadata concepts and was subject of an expert survey. The metadata concept and the labelling scheme are implemented in the spectral information system SPECCHIO, which is used for sharing and holding GUSL spectra. It allows easy implementation of spectra as well as their specification with the proposed metadata information to extend the GUSL. Therefore, the proposed data model represents a first fundamental step towards a generic usable and continuously expandable spectral library for urban areas. The metadata concept and the labelling scheme also build the basis for the necessary adaptation and transformation steps of the GUSL in order to use it entirely or in excerpts for further multi-site and multi-sensor applications

Fire

Vegetation plays a crucial role in regulating environmental conditions, including weather and climate. The amount of water and carbon dioxide in the air and the albedo of our planet are all influenced by vegetation, which in turn influences all life on Earth. Soil properties are also strongly influenced by vegetation, through biogeochemical cycles and feedback loops (see Volume 1A—Section 4). Vegetated landscapes on Earth provide habitat and energy for a rich diversity of animal species, including humans. Vegetation is also a major component of the world economy, through the global production of food, fibre, fuel, medicine, and other plantbased resources for human consumptio

Remote Sensing for Precision Nitrogen Management

This book focuses on the fundamental and applied research of the non-destructive estimation and diagnosis of crop leaf and plant nitrogen status and in-season nitrogen management strategies based on leaf sensors, proximal canopy sensors, unmanned aerial vehicle remote sensing, manned aerial remote sensing and satellite remote sensing technologies. Statistical and machine learning methods are used to predict plant-nitrogen-related parameters with sensor data or sensor data together with soil, landscape, weather and/or management information. Different sensing technologies or different modelling approaches are compared and evaluated. Strategies are developed to use crop sensing data for in-season nitrogen recommendations to improve nitrogen use efficiency and protect the environment

Nitrous oxide emissions from grazed grasslands: novel approaches to assessing spatial heterogeneity

Nitrous oxide (N2O) is a potent greenhouse gas mainly produced by microbial processes in the soil. Anthropogenic N2O is principally emitted from soils after nitrogen fertiliser and manure applications on agricultural land. This thesis focuses on emissions from grazing systems, which are known to be the largest source of uncertainty in global and national N2O emission inventories. Nitrogen-rich excreta deposits from grazing livestock are recognised as hotspots of N losses (N2O emissions in particular). The non-uniform distribution of these emissions hotspots within a typical field contributes significantly to the spatial heterogeneity of emissions often observed in addition to the natural variability of soil properties within the field such as pH, moisture and nutrient availability. However, it is extremely difficult to characterise the spatial and temporal pattern of these grazing inputs other than through the use of demanding and costly approaches such as manual observation or animal based-sensors. Two separate experiments were conducted during this study, in Scotland on sheep grazed grasslands and in Ireland on a dairy cow grazed grassland. Both sites were commercially used and were intensively managed with a nitrogen fertiliser application rate of 225 kg ha-1 yr-1 and 261 kg ha-1 yr-1, respectively. In Scotland, at Easter Bush fields the experiment was conducted during a 9 month campaign of gas, soil and grass sampling over the grazed field to study the spatial and temporal variability of the fluxes and soil properties to improve up-scaling of the fluxes from the plot scale to the field scale. In Ireland, at the Johnstown Castle farm, the experiment was conducted during an 11 month campaign on an experimental plot excluded from grazing. At the Scottish site, gas, soil and grass samples were collected regularly on soil which received different treatments within a randomised block design (e.g. urine deposition, fertiliser application, urine and fertiliser application or no N addition as a control). At both sites, Remotely Piloted Aerial System (RPAS) imagery was collected to study the spatial variability of the grass growth with the aim to map excreta depositions over the whole field. The Scottish site was used as a proof of concept of the method and the method was then used weekly on the Irish site over the entire grazing season. More generally, this thesis details the novel use of remote sensing techniques using high-resolution cameras linked to RPAS to improve our understanding of the spatial and temporal patterns of excreta deposition. This method proved to be repeatable for future studies as it can be automated, is easily deployable in the field, low-cost and the measurements are non-destructive (i.e. has no influence on the soil, vegetation or livestock). Excreta depositions contribute to very high emissions of N2O from relatively small areas of soil and can vary throughout the growing season in response to climatic conditions. Therefore, mapping of the excreta nitrogen inputs to the field facilitated a more accurate estimation of the annual field-scale N2O emission from grazing grasslands. Both experiments conducted in this study showed a high spatial and temporal N2O emissions variability due to the nature of N2O production within the soil and high variability of the soil properties (soil pH, soil moisture content, soil temperature) which influence the microbiological processes. Interaction on N2O emissions between fertiliser application and urine deposition was proved to be statistically significant and the magnitude of the interactions depended on the time of application within the year. The results showed a link between the variability of the emission factors of excreta deposition and fertiliser application and to the variation in weather conditions. This technique can be employed to up-scale emissions to a national level. This study plays a part in the on-going development of precision agricultural tools, based on image analysis of the grass sward to mitigate emissions from grazed grassland. Possible mitigation approaches, based on the methods presented in this thesis, include the use of RPAS technology to deliver nitrification inhibitors to newly deposited excreta within the field to reduce the potential nitrogen losses to the environment. This research indicates the future potential to better adjust fertiliser application using variable-rate fertiliser applications matching the vegetation nitrogen needs and limit nitrogen losses. This thesis identifies opportunities to develop innovative approaches to N2O mitigation by better evaluating emission estimations from agricultural practices, which could then be implemented in the national and global greenhouse gas inventories established by the Intergovernmental Panel on Climate Change

Outputs: Potassium Losses from Agricultural Systems

Potassium (K) outputs comprise removals in harvested crops and losses via a number of pathways. No specific environmental issues arise from K losses to the wider environment, and so they have received little attention. Nevertheless, K is very soluble and so can be leached to depth or to surface waters. Also, because K is bound to clays and organic materials, and adsorbed K is mostly associated with fine soil particles, it can be eroded with particulate material in runoff water and by strong winds. It can also be lost when crop residues are burned in the open. Losses represent a potential economic cost to farmers and reduce soil nutritional status for plant growth. The pathways of loss and their relative importance can be related to: (a) the general characteristics of the agricultural ecosystem (tropical or temperate regions, cropping or grazing, tillage management, interactions with other nutrients such as nitrogen); (b) the specific characteristics of the agricultural ecosystem such as soil mineralogy, texture, initial soil K status, sources of K applied (organic, inorganic), and rates and timing of fertilizer applications. This chapter provides an overview of the main factors affecting K removals in crops and losses through runoff, leaching, erosion, and open burning

Improving Potassium Recommendations for Agricultural Crops

This open access book highlights concepts discussed at two international conferences that brought together world-renowned scientists to advance the science of potassium (K) recommendations for crops. There was general agreement that the potassium recommendations currently in general use are oversimplified, outdated, and jeopardize soil, plant, and human health. Accordingly, this book puts forward a significantly expanded K cycle that more accurately depicts K inputs, losses and transformations in soils. This new cycle serves as both the conceptual basis for the scientific discussions in this book and a framework upon which to build future improvements. Previously used approaches are critically reviewed and assessed, not only for their relevance to future enhancements, but also for their use as metrics of sustainability. An initial effort is made to link K nutrition in crops and K nutrition in humans. The book offers an invaluable asset for graduate students, educators, industry scientists, data scientists, and advanced agronomists