Monitoring the Sustainable Intensification of Arable Agriculture:the Potential Role of Earth Observation

Sustainable intensification (SI) has been proposed as a possible solution to the conflicting problems of meeting projected increases in food demand and preserving environmental quality. SI would provide necessary production increases while simultaneously reducing or eliminating environmental degradation, without taking land from competing demands. An important component of achieving these aims is the development of suitable methods for assessing the temporal variability of both the intensification and sustainability of agriculture. Current assessments rely on traditional data collection methods that produce data of limited spatial and temporal resolution. Earth Observation (EO) provides a readily accessible, long-term dataset with global coverage at various spatial and temporal resolutions. In this paper we demonstrate how EO could significantly contribute to SI assessments, providing opportunities to quantify agricultural intensity and environmental sustainability. We review an extensive body of research on EO-based methods to assess multiple indicators of both agricultural intensity and environmental sustainability. To date these techniques have not been combined to assess SI; here we identify the opportunities and initial steps required to achieve this. In this context, we propose the development of a set of essential sustainable intensification variables (ESIVs) that could be derived from EO data

LCM2021 – the UK Land Cover Map 2021

Land cover is a key environmental variable, underpinning widespread environmental research and decision making. The UK Centre for Ecology and Hydrology (UKCEH) has provided reliable land cover information since the early 1990s; this supports multiple scientific, government and commercial objectives. Recent advances in computation and satellite data availability have enabled annual UKCEH land cover maps since 2017. Here, we introduce the latest, annual UK Land Cover Map representing 2021 (LCM2021), and we describe its production and validation. LCM2021 methods replicate those of LCM2017 to LCM2020 with minor deviations in cloud-masking processes and training data sourcing to enhance accuracy. LCM2021 is based on the classification of satellite and spatial context data into 21 land cover or habitat classes, from which a product suite is derived. The production of LCM2021 involved three highly automated key stages: pre-processing of input data, image classification and production of the final data products. Google Earth Engine scripts were used to create an input data stack of satellite and context data. A set of training areas was created based on data harvested from historic UKCEH land cover maps. The training data were used to construct a random forest classifier, which yielded classified images. Compiled results were validated against 35 182 reference samples, with correspondence tables indicating variable class accuracy and an overall accuracy of 82.6 % for the 21-class data and 86.5 % at a 10-aggregated-classes level. The UK Land Cover Map product suite includes a set of raster products in various projections, thematic and spatial resolutions (10 m, 25 m and 1 km), and land–parcel or vector products. The data are provided in 21-class (all configurations) and aggregated 10-class (1 km raster products only) versions. All raster products are freely available for academic and non-commercial research. The data for Great Britain (GB) are provided in the British National Grid projection (EPSG: 27700) and the Northern Ireland (NI) data are in the TM75 Irish Grid (EPSG: 29903). Information on how to access the data is given in the “Data availability” section of the paper.</p

The influence of land cover data on farm-scale valuations of natural capital

The valuation of natural capital within individual farms could inform environmentally beneficial land use change and form the basis of agricultural subsidy schemes based on the provision of ecosystem services. Land cover extents can be used in a benefit transfer approach to produce monetary valuations of natural capital rapidly and at low cost. However, the methodology has not before been used within individual farms, and the impact of land cover data characteristics on the accuracy of valuations is uncertain. Here, we apply the approach to five UK farms of contrasting size, configuration and farming style, using three widely available land cover products. Results show that the land cover product used has a substantial impact on valuations, with differences of up to 58%, and the magnitude of this effect varies considerably according to the landscape structure of the farm. At most sites, valuation differences are driven by the extent of woodland recorded in the landscape, with higher resolution land cover products incorporating larger amounts of woodland through inclusion of smaller patches, leading to higher overall valuations. Integrating more accurate land cover data and accounting for the condition, configuration and location of natural capital has potential to improve the accuracy of valuations

Evaluating combinations of temporally aggregated Sentinel-1, Sentinel-2 and Landsat 8 For land cover mapping with Google Earth Engine

Land cover mapping of large areas is challenging due to the significant volume of satellite data to acquire and process, as well as the lack of spatial continuity due to cloud cover. Temporal aggregation—the use of metrics (i.e., mean or median) derived from satellite data over a period of time—is an approach that benefits from recent increases in the frequency of free satellite data acquisition and cloud-computing power. This enables the efficient use of multi-temporal data and the exploitation of cloud-gap filling techniques for land cover mapping. Here, we provide the first formal comparison of the accuracy between land cover maps created with temporal aggregation of Sentinel-1 (S1), Sentinel-2 (S2), and Landsat-8 (L8) data from one-year and test whether this method matches the accuracy of traditional approaches. hirty-two datasets were created for Wales by applying automated cloud-masking and temporally aggregating data over different time intervals, using Google Earth Engine. Manually processed S2 data was used for comparison using a traditional two-date composite approach. Supervised classifications were created, and their accuracy was assessed using field-based data. Temporal aggregation only matched the accuracy of the traditional two-date composite approach (77.9%) when an optimal combination of optical and radar data was used (76.5%). Combined datasets (S1, S2 or S1, S2, and L8) outperformed single-sensor datasets, while datasets based on spectral indices obtained the lowest levels of accuracy. The analysis of cloud cover showed that to ensure at least one cloud-free pixel per time interval, a maximum of two intervals per year for temporal aggregation were possible with L8, while three or four intervals could be used for S2. This study demonstrates that temporal aggregation is a promising tool for integrating large amounts of data in an efficient way and that it can compensate for the lower quality of automatic image selection and cloud masking. It also shows that combining data from different sensors can improve classification accuracy. However, this study highlights the need for identifying optimal combinations of satellite data and aggregation parameters in order to match the accuracy of manually selected and processed image composites

High resolution wheat yield mapping using Sentinel-2

Accurate crop yield estimates are important for governments, farmers, scientists and agribusiness. This paper provides a novel demonstration of the use of freely available Sentinel-2 data to estimate within-field wheat yield variability in a single year. The impact of data resolution and availability on yield estimation is explored using different combinations of input data. This was achieved by combining Sentinel-2 with environmental data (e.g. meteorological, topographical, soil moisture) for different periods throughout the growing season. Yield was estimated using Random Forest (RF) regression models. They were trained and validated using a dataset containing over 8000 points collected by combine harvester yield monitors from 39 wheat fields in the UK. The results demonstrate that it is possible to produce accurate maps of within-field yield variation at 10 m resolution using Sentinel-2 data (RMSE 0.66 t/ha). When combined with environmental data further improvements in accuracy can be obtained (RMSE 0.61 t/ha). We demonstrate that with knowledge of crop-type distribution it is possible to use these models, trained with data from a few fields, to estimate within-field yield variability on a landscape scale. Applying this method gives us a range of crop yield across the landscape of 4.09 to 12.22 t/ha, with a total crop production of approx. 289,000 t

Regional-scale high spatial resolution mapping of aboveground net primary productivity (ANPP) from field survey and Landsat data: a case study for the country of Wales

This paper presents an alternative approach for high spatial resolution vegetation productivity mapping at a regional scale, using a combination of Normalised Difference Vegetation Index (NDVI) imagery and widely distributed ground-based Above-ground Net Primary Production (ANPP) estimates. Our method searches through all available single-date NDVI imagery to identify the images which give the best NDVI–ANPP relationship. The derived relationships are then used to predict ANPP values outside of field survey plots. This approach enables the use of the high spatial resolution (30 m) Landsat 8 sensor, despite its low revisit frequency that is further reduced by cloud cover. This is one of few studies to investigate the NDVI–ANPP relationship across a wide range of temperate habitats and strong relationships were observed (R2 = 0.706), which increased when only grasslands were considered (R2 = 0.833). The strongest NDVI–ANPP relationships occurred during the spring “green-up” period. A reserved subset of 20% of ground-based ANPP estimates was used for validation and results showed that our method was able to estimate ANPP with a RMSE of 15–21%. This work is important because we demonstrate a general methodological framework for mapping of ANPP from local to regional scales, with the potential to be applied to any temperate ecosystems with a pronounced green up period. Our approach allows spatial extrapolation outside of field survey plots to produce a continuous surface product, useful for capturing spatial patterns and representing small-scale heterogeneity, and well-suited for modelling applications. The data requirements for implementing this approach are also discussed

LCM2021 – the UK Land Cover Map 2021

Land cover is a key environmental variable, underpinning widespread environmental research and decision-making. The UK Centre for Ecology and Hydrology (UKCEH) have provided reliable land cover information since the early 1990’s; this supports multiple scientific, government and commercial objectives. Recent advances in computation and satellite data availability have enabled annual UKCEH land cover maps since 2017. Here we introduce the latest, annual UK Land Cover Map, representing 2021 (LCM2021) and describe its production and validation. LCM2021 methods replicate those for LCM2017 to LCM2020 with minor deviations to enhance accuracy. LCM2021 is based on the classification of satellite and spatial context data into 21 land cover/habitat classes, from which a product suite is derived. The production of LCM2021 involved three highly automated key stages: pre-processing of input data, image classification and production of the final data products. Google Earth Engine scripts were used to create an input data stack of satellite and context data. A set of training areas was created, based on data harvested from historic UKCEH land cover maps. The training data were used to construct a Random Forest classifier, which yielded classified images. Compiled results were validated against 35,182 reference samples, with correspondence tables indicating variable class accuracy and an overall accuracy of 82.6 % for the 21-class data and 86.5 % at a 10 aggregated-class level. •The UK Land Cover Map product suite includes a set of raster products in various projections, thematic and spatial resolutions (10 m, 25 m and 1 km), and land–parcel or vector products. The data are provided in 21-class (all configurations) and aggregated 10-class (1 km raster products only) versions. All raster products are freely available for academic and non-commercial research. The data for Great Britain (GB) are provided in the British National Grid projection (EPSG: 27700) and the Northern Ireland (NI) data are in the TM75 Irish Grid (EPSG: 29903). Information on how to access the data is given in the “Data availability” section of the paper

Near-infrared photoluminescence enhancement in Ge/CdS and Ge/ZnS core/shell nanocrystals: Utilizing IV/II-VI semiconductor epitaxy

Ge nanocrystals have a large Bohr radius and a small, size-tunable band gap that may engender direct character via strain or doping. Colloidal Ge nanocrystals are particularly interesting in the development of near-infrared materials for applications in bioimaging, telecommunications and energy conversion. Epitaxial growth of a passivating shell is a common strategy employed in the synthesis of highly luminescent II-VI, III-V and IV-VI semiconductor quantum dots. Here, we use relatively unexplored IV/II-VI epitaxy as a way to enhance the photoluminescence and improve the optical stability of colloidal Ge nanocrystals. Selected on the basis of their relatively small lattice mismatch compared with crystalline Ge, we explore the growth of epitaxial CdS and ZnS shells using the successive ion layer adsorption and reaction method. Powder X-ray diffraction and electron microscopy techniques, including energy dispersive X-ray spectroscopy and selected area electron diffraction, clearly show the controllable growth of as many as 20 epitaxial monolayers of CdS atop Ge cores. In contrast, Ge etching and/or replacement by ZnS result in relatively small Ge/ZnS nanocrystals. The presence of an epitaxial II-VI shell greatly enhances the near-infrared photoluminescence and improves the photoluminescence stability of Ge. Ge/II-VI nanocrystals are reproducibly 1-3 orders of magnitude brighter than the brightest Ge cores. Ge/4.9CdS core/shells show the highest photoluminescence quantum yield and longest radiative recombination lifetime. Thiol ligand exchange easily results in near-infrared active, water-soluble Ge/II-VI nanocrystals. We expect this synthetic IV/II-VI epitaxial approach will lead to further studies into the optoelectronic behavior and practical applications of Si and Ge-based nanomaterials

A systematic review of intervention effects on potential mediators of children\u27s physical activity

Background : Many interventions aiming to increase children&rsquo;s physical activity have been developed and implemented in a variety of settings, and these interventions have previously been reviewed; however the focus of these reviews tends to be on the intervention effects on physical activity outcomes without consideration of the reasons and pathways leading to intervention success or otherwise. To systematically review the efficacy of physical activity interventions targeting 5-12 year old children on potential mediators and, where possible, to calculate the size of the intervention effect on the potential mediator. Methods : A systematic search identified intervention studies that reported outcomes on potential mediators of physical activity among 5-12 year old children. Original research articles published between 1985 and April 2012 were reviewed. Results : Eighteen potential mediators were identified from 31 studies. Positive effects on cognitive/psychological potential mediators were reported in 15 out of 31 studies. Positive effects on social environmental potential mediators were reported in three out of seven studies, and no effects on the physical environment were reported. Although no studies were identified that performed a mediating analysis, 33 positive intervention effects were found on targeted potential mediators (with effect sizes ranging from small to large) and 73% of the time a positive effect on the physical activity outcome was reported. Conclusions : Many studies have reported null intervention effects on potential mediators of children&rsquo;s physical activity; however, it is important that intervention studies statistically examine the mediating effects of interventions so the most effective strategies can be implemented in future programs