Users’ Assessment of Orthoimage Photometric Quality for Visual Interpretation of Agricultural Fields

Land cover identification and area quantification are key aspects of implementing the European Common Agriculture Policy. Rightfulness of support provided to farmers is monitored using the Land Parcel Identification System (LPIS), with land cover identification performed by photointerpretation. While the geometric orthoimage quality required for correct photointerpretation is well understood, little is known about the photometric quality needed for LPIS applications. This paper analyzes the orthoimage quality characteristics chosen by authors as being most suitable for visual identification of agricultural fields. We designed a survey to assess users’ preferred brightness and contrast ranges for orthoimages used for LPIS purposes. Survey questions also tested the influence of a background color on the preferred orthoimage brightness and contrast, the preferred orthoimage format and color composite, assessments of orthoimages with shadowed areas, appreciation of image enhancements and, finally, consistency of individuals’ preferred brightness and contrast settings across multiple sample images. We find that image appreciation is stable at the individual level, but preferences do vary across respondents. We therefore recommend that LPIS operators be enabled to personalize photometric settings, such as brightness and contrast values, and to choose the displayed band combination from at least four spectral bands.JRC.H.4-Monitoring Agricultural Resource

Producing consistent visually interpreted land cover reference data: learning from feedback

Reference data for large-scale land cover map are commonly acquired by visual interpretation of remotely sensed data. To assure consistency, multiple images are used, interpreters are trained, sites are interpreted by several individuals, or the procedure includes a review. But little is known about important factors influencing the quality of visually interpreted data. We assessed the effect of multiple variables on land cover class agreement between interpreters and reviewers. Our analyses concerned data collected for validation of a global land cover map within the Copernicus Global Land Service project. Four cycles of visual interpretation were conducted, each was followed by review and feedback. Each interpreted site element was labelled according to dominant land cover type. We assessed relationships between the number of interpretation updates following feedback and the variables grouped in personal, training, and environmental categories. Variable importance was assessed using random forest regression. Personal variable interpreter identifier and training variable timestamp were found the strongest predictors of update counts, while the environmental variables complexity and image availability had least impact. Feedback loops reduced updating and hence improved consistency of the interpretations. Implementing feedback loops into the visually interpreted data collection increases the consistency of acquired land cover reference data

Combining legacy data from heterogeneous crop trials to identify genotype by environment interactions using model-based recursive partitioning

Crop variety trials are important to generate insights on variety environmental adaptation, but this requires that varieties should be tested in a wide range of environments to consider the complexity of genotype by environment interactions. Given the substantial costs of collecting trial data, agricultural science needs to maximize the insights extracted from existing data. An alternative is to combine data from different trials performed in different environments using a data synthesis approach. Analyzing aggregated data from different trials could be challenging as datasets are often heterogeneous. Previous research has shown that ranking-based methods can deal with heterogeneous data from different trials to gain insights in average performance of genotypes, but not in responses to different environmental conditions. We show that such insights can be obtained from heterogeneous legacy field trial data by means of model-based recursive partitioning, using climatic covariates from open access databases. We applied this strategy to analyze the reaction of different banana cultivars to black leaf streak disease across several environments. This data-driven approach allowed to integrate heterogeneous datasets, which differ in measurements scales, experimental design, and testing environments. In our preliminary results, we found that cultivar reaction to black leaf streak disease is driven by both genotypic and climatic factors. The main agroclimatic variables identified by our model are the diurnal temperature range (DTR) and maximum length of consecutive days with rain >= 1 mm (MLWS). We show the potential of this method, which allows to gain cumulative insights in genotype by environment interactions as more trial data becomes available

Data synthesis for crop variety evaluation. A review

Crop varieties should fulfill multiple requirements, including agronomic performance and product quality. Variety evaluations depend on data generated from field trials and sensory analyses, performed with different levels of participation from farmers and consumers. Such multi-faceted variety evaluation is expensive and time-consuming; hence, any use of these data should be optimized. Data synthesis can help to take advantage of existing and new data, combining data from different sources and combining it with expert knowledge to produce new information and understanding that supports decision-making. Data synthesis for crop variety evaluation can partly build on extant experiences and methods, but it also requires methodological innovation. We review the elements required to achieve data synthesis for crop variety evaluation, including (1) data types required for crop variety evaluation, (2) main challenges in data management and integration, (3) main global initiatives aiming to solve those challenges, (4) current statistical approaches to combine data for crop variety evaluation and (5) existing data synthesis methods used in evaluation of varieties to combine different datasets from multiple data sources. We conclude that currently available methods have the potential to overcome existing barriers to data synthesis and could set in motion a virtuous cycle that will encourage researchers to share data and collaborate on data-driven research

Development of a Dynamic Web Mapping Service for Vegetation Productivity Using Earth Observation and in situ Sensors in a Sensor Web Based Approach

This paper describes the development of a sensor web based approach which combines earth observation and in situ sensor data to derive typical information offered by a dynamic web mapping service (WMS). A prototype has been developed which provides daily maps of vegetation productivity for the Netherlands with a spatial resolution of 250 m. Daily available MODIS surface reflectance products and meteorological parameters obtained through a Sensor Observation Service (SOS) were used as input for a vegetation productivity model. This paper presents the vegetation productivity model, the sensor data sources and the implementation of the automated processing facility. Finally, an evaluation is made of the opportunities and limitations of sensor web based approaches for the development of web services which combine both satellite and in situ sensor sources

Quantifying the effect of forest age in annual net forest carbon balance

Forests dominate carbon (C) exchanges between the terrestrial biosphere and the atmosphere on land. In the long term, the net carbon flux between forests and the atmosphere has been significantly impacted by changes in forest cover area and structure due to ecological disturbances and management activities. Current empirical approaches for estimating net ecosystem productivity (NEP) rarely consider forest age as a predictor, which represents variation in physiological processes that can respond differently to environmental drivers, and regrowth following disturbance. Here, we conduct an observational synthesis to empirically determine to what extent climate, soil properties, nitrogen deposition, forest age and management influence the spatial and interannual variability of forest NEP across 126 forest eddy-covariance flux sites worldwide. The empirical models explained up to 62% and 71% of spatio-temporal and across-site variability of annual NEP, respectively. An investigation of model structures revealed that forest age was a dominant factor of NEP spatio-temporal variability in both space and time at the global scale as compared to abiotic factors, such as nutrient availability, soil characteristics and climate. These findings emphasize the importance of forest age in quantifying spatio-temporal variation in NEP using empirical approaches

Rank-based data synthesis of common bean on-farm trials across four Central American countries

Location-specific information is required to support decision making in crop vari-ety management, especially under increasingly challenging climate conditions. Datasynthesis can aggregate data from individual trials to produce information that sup-ports decision making in plant breeding programs, extension services, and of farmers.Data from on-farm trials using the novel approach of triadic comparison of technolo-gies (tricot) are increasingly available, from which more insights could be gainedusing a data synthesis approach. The objective of our study was to present the appli-cability of a rank-based data synthesis approach to several datasets from tricot trialsAbbreviations:AIC, Akaike information criteria; AOA, area of applicability; DAP, daily accumulated precipitation; DI, dissimilarity index; DP, dailyprecipitation; DSRF, daily solar radiation flux; tricot, triadic comparison of technologies.This is an open access article under the terms of theCreative Commons AttributionLicense, which permits use, distribution and reproduction in any medium, provided the originalwork is properly cited.©2022 The Authors. Crop Science published by Wiley Periodicals LLC on behalf of Crop Science Society of America.2246wileyonlinelibrary.com/journal/csc2Crop Science.2022;62:2246–2266.BROWNET AL.2247Crop Scienceto generate location-specific information supporting decision making in crop varietymanagement. Our study focuses on tricot data from 14 trials of common bean (Phase-olus vulgarisL.) performed between 2015 and 2018 across four countries in CentralAmerica (Costa Rica, El Salvador, Honduras, and Nicaragua). The combined data of17 common bean genotypes were rank aggregated and analyzed with the Plackett–Luce model. Model-based recursive partitioning was used to assess the influenceof spatially explicit environmental covariates on the performance of common beangenotypes. Location-specific performance was predicted for the three main grow-ing seasons in Central America. We demonstrate how the rank-based data synthesismethodology allows integrating tricot trial data from heterogenous sources to providelocation-specific information to support decision making in crop variety manage-ment. Maps of genotype performance can support decision making in crop varietyevaluation such as variety recommendations to farmers and variety release processes

Data synthesis of multiple on-farm trials to generate regional variety recommendations: the case of common bean in Central America

Common bean (Phaseolus vulgaris L.) is a main food crop in Central America. Several improved varieties have been developed and released by different crop improvement programs in the region but many of these varieties are not used widely by farmers. One limitation is the lack of information about which are the best adapted varieties for each area within the region, even though on-farm testing of varieties is widely done by different organizations. Data synthesis of existing on-farm trial data can help to predict the suitability of varieties to areas within the region where trials were not conducted. Data synthesis is facilitated by a new participatory on-farm testing approach, triadic comparison of technologies (tricot). This approach involves the participation of farmers as citizen scientists at scale and ensures data are collected digitally, facilitating data synthesis. From 2015 to 2018, more than 2,000 tricot trial plots were established in Central America by different organizations, including agricultural research centers, universities, NGOs, and farmer’s associations. The trials tested landraces, experimental lines, and improved varieties created with both conventional and participatory breeding approaches. We applied an innovative data synthesis method to analyze the tricot trial data jointly, including seasonal climate and soil covariates to assess environmental adaptation. The results showed that the method was able to predict farmers’ overall appreciation of varieties in unsampled areas.Common bean (Phaseolus vulgaris L.) is a main food crop in Central America. Several improved varieties have been developed and released by different crop improvement programs in the region but many of these varieties are not used widely by farmers. One limitation is the lack of information about which are the best adapted varieties for each area within the region, even though on-farm testing of varieties is widely done by different organizations. Data synthesis of existing on-farm trial data can help to predict the suitability of varieties to areas within the region where trials were not conducted. Data synthesis is facilitated by a new participatory on-farm testing approach, triadic comparison of technologies (tricot). This approach involves the participation of farmers as citizen scientists at scale and ensures data are collected digitally, facilitating data synthesis. From 2015 to 2018, more than 2,000 tricot trial plots were established in Central America by different organizations, including agricultural research centers, universities, NGOs, and farmer’s associations. The trials tested landraces, experimental lines, and improved varieties created with both conventional and participatory breeding approaches. We applied an innovative data synthesis method to analyze the tricot trial data jointly, including seasonal climate and soil covariates to assess environmental adaptation. The results showed that the method was able to predict farmers’ overall appreciation of varieties in unsampled areas

Past decade above-ground biomass change comparisons from four multi-temporal global maps

Above-ground biomass (AGB) is considered an essential climate variable that underpins our knowledge and information about the role of forests in mitigating climate change. The availability of satellite-based AGB and AGB change (Delta AGB) products has increased in recent years. Here we assessed the past decade net Delta AGB derived from four recent global multi-date AGB maps: ESA-CCI maps, WRI-Flux model, JPL time series, and SMOS-LVOD time series. Our assessments explore and use different reference data sources with biomass re-measurements within the past decade. The reference data comprise National Forest Inventory (NFI) plot data, local Delta AGB maps from airborne LiDAR, and selected Forest Resource Assessment country data from countries with well-developed monitoring capacities. Map to reference data comparisons were performed at levels ranging from 100 m to 25 km spatial scale. The comparisons revealed that LiDAR data compared most reasonably with the maps, while the comparisons using NFI only showed some agreements at aggregation levels <10 km. Regardless of the aggregation level, AGB losses and gains according to the map comparisons were consistently smaller than the reference data. Map-map comparisons at 25 km highlighted that the maps consistently captured AGB losses in known deforestation hotspots. The comparisons also identified several carbon sink regions consistently detected by all maps. However, disagreement between maps is still large in key forest regions such as the Amazon basin. The overall AAGB map cross-correlation between maps varied in the range 0.11-0.29 (r). Reported AAGB magnitudes were largest in the high-resolution datasets including the CCI map differencing (stock change) and Flux model (gain-loss) methods, while they were smallest according to the coarser-resolution LVOD and JPL time series products, especially for AGB gains. Our results suggest that AAGB assessed from current maps can be biased and any use of the estimates should take that into account. Currently, AAGB reference data are sparse especially in the tropics but that deficit can be alleviated by upcoming LiDAR data networks in the context of Supersites and GEO-Trees

A comprehensive framework for assessing the accuracy and uncertainty of global above-ground biomass maps

Over the past decade, several global maps of above-ground biomass (AGB) have been produced, but they exhibit significant differences that reduce their value for climate and carbon cycle modelling, and also for national estimates of forest carbon stocks and their changes. The number of such maps is anticipated to increase because of new satellite missions dedicated to measuring AGB. Objective and consistent methods to estimate the accuracy and uncertainty of AGB maps are therefore urgently needed. This paper develops and demonstrates a framework aimed at achieving this. The framework provides a means to compare AGB maps with AGB estimates from a global collection of National Forest Inventories and research plots that accounts for the uncertainty of plot AGB errors. This uncertainty depends strongly on plot size, and is dominated by the combined errors from tree measurements and allometric models (inter-quartile range of their standard deviation (SD) = 30–151 Mg ha−1). Estimates of sampling errors are also important, especially in the most common case where plots are smaller than map pixels (SD = 16–44 Mg ha−1). Plot uncertainty estimates are used to calculate the minimum-variance linear unbiased estimates of the mean forest AGB when averaged to 0.1∘. These are used to assess four AGB maps: Baccini (2000), GEOCARBON (2008), GlobBiomass (2010) and CCI Biomass (2017). Map bias, estimated using the differences between the plot and 0.1∘ map averages, is modelled using random forest regression driven by variables shown to affect the map estimates. The bias model is particularly sensitive to the map estimate of AGB and tree cover, and exhibits strong regional biases. Variograms indicate that AGB map errors have map-specific spatial correlation up to a range of 50–104 km, which increases the variance of spatially aggregated AGB map estimates compared to when pixel errors are independent. After bias adjustment, total pantropical AGB and its associated SD are derived for the four map epochs. This total becomes closer to the value estimated by the Forest Resources Assessment after every epoch and shows a similar decrease. The framework is applicable to both local and global-scale analysis, and is available at https://github.com/arnanaraza/PlotToMap. Our study therefore constitutes a major step towards improved AGB map validation and improvement