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Soil organic carbon (SOC) is central to the functioning of terrestrial ecosystems, has climate mitigation potential and provides several benefits for soil health. Understanding the spatial distribution of SOC can help formulate sustainable soil management practices. Digital soil mapping (DSM) uses advanced statistical and geostatistical methods to estimate soil properties across large areas. DSM integrates climate data, topographic features, geology, legacy soil maps, land management and remote sensing data. Bare soil spectra may reflect the presence of particular soil components, making satellite derived spectra suitable predictors of SOC. Bare soil spectra derived from Sentinel-2 were used to estimate SOC concentration (SOC%) and granulometric fractions in the plough layer (0–30 cm) of agricultural parcels in northern Belgium. Thereafter, the estimation performance of SOC% was compared for three DSM models: one with bare soil spectra, one with environmental covariates (topography, granulometry and vegetation), and a combined model with bare soil spectra and environmental covariates. The estimation performance of sand, silt and clay fractions using bare soil spectra from the spring seedbed (R2: 0.53–0.74; RPD: 1.49–2.05; RPIQ: 1.52–2.39) was higher than that of SOC% (R2: 0.16; RPD: 1.08; RPIQ: 1.32). The highest estimation performance of SOC% was obtained for a DSM model including all covariates (R2: 0.28; RPD: 1.18; RPIQ: 1.44), but the contribution of spring seedbed spectra to a model containing environmental covariates was small. The results provide valuable insights for refining soil property estimation using DSM with spectral and environmental covariates.

Plastic mulch is increasingly used in agriculture due to its short-term benefits, while long-term risks of plastic accumulation in soil is a growing concern. An analytical methodology is presented to quantify plastic mulch usage based on location, timing, and crop type, at parcel and regional scales. Multispectral Sentinel-2 data identified plastic mulch use between 2017 and 2021 with an F1 score of 0.7. Historical patterns of mulch use and crop rotation were explored through sequence analysis, and enabled predictions and likelihood of occurrence of plastic mulch for the year 2022. Silage maize followed by asparagus and carrots are the most prevalent crops utilizing plastic mulch. Successive cropping of silage maize on the same parcel was detected on 70 % of parcels, surpassing 40 % of successive cropping for four continuous years. Successive asparagus crops on the same plot, often alternating with spinach and carrots, also occurs. Crop forecasting reached an accuracy of 0.76 when up to 10 potential crop predictions for each historical sequence were permitted. The prediction of the likelihood of plastic mulch improved with fewer permitted predicted crops (

Abstract Background Spring frosts pose an important threat to orchard productivity in temperate zones and projections do not exclude damaging events in the future. However, there is no up-to-date and systematic comparison of the effectiveness and conditionality of the existing passive and active damage prevention strategies. This systematic review seeks to answer the questions “How do the performances of spring frost damage reduction strategies in temperate fruit orchards compare?” and “How do environmental conditions affect the effectiveness of frost damage reduction strategies in temperate fruit orchards?”. Methods This review covers a large range of on-field strategies for the protection of flowering orchards against damage inflicted by late spring frost. All major temperate fruit tree crops and grapevines were included, provided that the performance of frost damage reduction was compared against a control in terms of bud and flower survival, yield and delays in flowering time, or ambient temperature change. Articles and reports were collected between June and October 2021 from the Web of Science Core Collection and regional indexes and from the databases Scopus, FAO AGRIS, USDA Agricola, CAB Abstracts and the Groenekennis database of the University of Wageningen, the Netherlands, as well as from relevant institutional websites and the Chinese scholarly search engine ‘Baidu’. Biases resulting from inadequate randomisation, incomplete reporting or deficient study designs were reported. Temporal and spatial research trends and gaps were mapped based on 104 selected studies (from 8970 identified studies). Data was extracted for every experiment that an article reported on, leading to 971 data points. Groups of frost protection methods were compared in terms of effectiveness whereby environmental factors were examined to explain the variation of the effectiveness by means of mixed linear models. Review findings Most included studies originate from the United States and Europe more than from the temperate fruit production regions in Asia. An increase over time in the research on foliar applications, including growth regulation hormones was observed. Apple, peach and more recently grapevine were the most researched fruit types, followed by cherry and pear. The validity of the selected studies was generally low as measures of variability were reported only occasionally. Therefore, only descriptive comparisons of effectiveness were undertaken between intervention classes by fruit types. Sprinkler systems were found to perform best for most studied outcomes, while the emerging biochemical solutions revealed mixed results. The performances of resource-intensive heating systems did not outperform low-resource techniques such as tunnels or coverings of individual buds. The lack of reporting standards did not allow extensive correlations with ambient factors and reduced the transferability of the review’s findings. A need for standard protocols for experiments and reporting is therefore apparent. Conclusions In this field, strong shortcomings in the documentation of experimental setups and reporting standards were exposed. Implications for policy making are limited while for research recommendations to reduce bias and increase comparability are put forward.

Land Suitability Analysis (LSA), under the impact of climate change, is a fundamental approach to the design of appropriate land management strategies for sustainable crop production and food security. In this study, the FAO framework was used to assess the impact of climate change on land suitability for maize in Flanders, Belgium. The current LSA revealed the marginal suitability for maize cultivation, characterizing most of the agricultural land in Flanders and identifying precipitation as the most limiting factor for maize suitability. The LSA, under two climate change scenarios, was based on climate projections from several CMIP5 Global Circulation Models, transformed into future land suitability projections and assembled into a multi-model ensemble (MME) of projected suitability changes. The results indicate an average reduction in projected suitability of approximately 7% by 2099 under the high-emission scenario. The potential of the Soil-Improving Cropping System (SICS) to mitigate the impacts of climate change on land suitability was statistically significant under both low- and high-emission scenarios. This research provides valuable insights into the MME modeling of climate change impacts on land suitability and its associated uncertainty, with the application of SICS as a potential long-term mitigation measure to promote sustainable agricultural practices.

As a large proportion of land is managed by professional family farms, agent-based models are of interest for simulating agricultural land use. This requires a deep understanding of the farm characteristics that influence land use decisions. We developed a methodology to identify a data-driven farm typology by combining participatory methods, multivariate statistical modeling and spatiotemporal parcel-based land cover analysis between 2000 and 2020. A formal questionnaire provided data on the farm characteristics, which were subjected to principal component analysis and k-means clustering. The resulting data-driven typology complemented a production-based approach to understanding land use decisions. The main influencing factors were farm size, share of private land, dominant crops and participation in European schemes such as NATURA2000 and agri-environment-climate measures. Overall, family tradition and a high return on investment were the most important motivations for maintaining current land use practices, while a higher income, income support and diversification were the most important reasons for pursuing new land use options. Differences between the farm characteristics highlighted the importance of the motivations for land use decisions between the farm types. This methodology can be used to generate data-driven typologies suitable for implementing agent-based models to explore sustainable land management options in a changing environment.

As a result of climate change, climatic extremes are expected to increase. For high-value crops like vegetables, irrigation is a potentially economically viable adaptation measure in western Europe. To optimally schedule irrigation, decision support systems based on crop models like AquaCrop are increasingly used by farmers. High value vegetable crops like cauliflower or spinach are grown in two distinct growth cycles per year and, additionally, have a high turnover rate of new varieties. To successfully deploy the AquaCrop model in a decision support system, it requires a robust calibration. However, it is not known whether parameters can be conserved over both growth periods, nor whether a cultivar dependent model calibration is always required. Furthermore, when data are collected from farmers’ fields, there are constraints in data availability and uncertainty. We collected data from commercial cauliflower and spinach fields in Belgium in 2019, 2020 and 2021 during different growing periods and of different cultivars. With the use of a Bayesian calibration, we confirmed the need for a condition or cultivar specific calibration for cauliflower, while for spinach, splitting the data per cultivar or pooling the data together did not improve uncertainty on the model simulations. However, due to uncertainties arising from field specific soil and weather conditions, or measurement errors from calibration data, real time field specific adjustments are advised to simulations when using AquaCrop as decision support tool. Remotely sensed or in situ ground data may be invaluable information to reduce uncertainty on model simulations.

Abstract Background Spring frosts pose an important threat to orchard productivity in temperate zones and predictions of future occurrences do not exclude damaging events. However, there is no up-to-date and systematic comparison of the effectiveness and conditionality of the existing passive and active damage prevention strategies. This review seeks to answer the questions “How do the performances of spring frost damage reduction strategies in temperate fruit orchards compare?” and “How do environmental conditions affect frost damage reduction strategies in temperate fruit orchards?”. Methods and output The review covers on-site frost damage prevention mechanisms and interventions for the most important temperate pome, stone and citrus fruit trees as well as grapevine. Searches include the core collection and regional databases on the Web of Science platform, Scopus, as well as specialized libraries like Agris, Agricola, CAB Abstracts, Groenekennis and selected institutional websites. Included studies report on the effectiveness of at least one intervention in reducing spring frost damage or increasing temperatures in the field. The validity of the studies will be evaluated based on their risk of general research bias and on topic-specific, stakeholder co-designed validity criteria. Data will be extracted regarding the study setup (study design, location, characteristics of frosts) and the resulting temperature increase, crop development alteration and/or damage reductions. The effectiveness of interventions will be evaluated in terms of yields, damage to flowers, ambient temperature and/or crop development. An Evidence Atlas will identify general and geographic research gaps, as well as research trends. Meta-regression of effectiveness on environmental conditions will be attempted.

Global climate change has discernible impacts on the quality of the landscapes of Hungary. Only a dynamic and spatially differentiated land evaluation methodology can properly reflect these changes. The provision level, rate oftransformation and spatial distribution of ecosystem services (ESs) are fundamental properties of landscapes and have to be integral parts of an up-to-date land evaluation. For agricultural land capability assessment soil fertility is a major supporting ES, directly associated with climate change through greenhouse gas emissions and carbon sequestration as regulationg services. Since for Hungary aridification is the most severe consequence of climate change, water-related ESs, such as water retention and storage on and below the surface as well as control of floods, water pollution and soil erosion, are of increasing importance. The productivity of agricultural crops is enhanced by more atmospheric CO2 but restricted by higher drought susceptibility. The value of floodplain landscapes, i.e. their agroecological, nature conservation, tourism (aesthetic) and other potentials, however, will be increasingly controlled by their water supply, which is characterized by hydrometeorological parameters. Case studies are presented for the estimation of the value of two water-related regulating ESs (water retention and groundwater recharge capacities) in the floodplains of the Kapos and Drava rivers, Southwest Hungary. It is predictable that in the future land evaluation techniques based on the FAO framework will be more dynamic and integrated with the monetary valuation of ESs. The latter task, however, still involves numerous methodological problems to solve.

Adverse weather conditions greatly reduce crop yields, leading to economic losses and lower food availability. The characterization of adverse weather and the quantification of their potential impact on arable farming is necessary to advise farmers on feasible and effective adaptation strategies and to support decision making in the agriculture sector. This research aims to analyze the impact of adverse weather on the yield of winter wheat, grain maize and late potato using a yield gap approach. A time-series analysis was performed to identify the relationship between (agro-)meteorological indicators and crop yields and yield gaps in Flanders (northern Belgium) based on 10 years of field trial and weather data. Indicators were calculated for different crop growth stages and multiple soils. Indicators related to high temperature, water deficit and water excess were analyzed, as the occurrence frequency and intensity of these weather events will most likely increase by 2030–2050. The concept of “yield gap” was used to analyze the effects of adverse weather in relation to other yield-reducing factors such as suboptimal management practices. Winter wheat preferred higher temperatures during grain filling and was negatively affected by wet conditions throughout the growing season. Maize was especially vulnerable to drought throughout the growing season. Potato was more affected by heat and drought stress during tuber bulking and by waterlogging during the early growth stages. The impact of adverse weather on crop yield was influenced by soil type, and optimal management practices mitigated the impact of adverse weather.

Grasslands are an important biotope in Europe, not only because they are widespread, but also because they provide valuable ecosystem services. The ecological value of a grassland parcel is directly proportional to the number of uninterrupted years of grassland cover. However, the area of long-term grassland (i.e., grassland of 5 years or older) is decreasing, limiting its ability to provide ecosystem services. To prevent the further disappearance of long-term grasslands, Europe developed an agricultural policy instrument in 2003 to protect grasslands of 5 years or older. Nature policy instruments aim to protect grasslands that have existed for more than 10 years to support their high environmental value. However, there is currently no multi-annual information on the location and age of grasslands at a high spatial and temporal resolution, which makes it difficult to assess the effectiveness of the current grassland protection regulations. Multi-annual satellite-based land cover classification can provide a solution for grassland area and age monitoring, which we tested by producing a series of Landsat-based land cover classification maps from 2005 to 2019 for the region of Flanders, Belgium. Historical land cover classification maps proved useful for evaluating past and present planning and policy to ensure grassland conservation, linking spatial and temporal changes in the area of long-term grasslands with policy changes and landscape dynamics. We were able to locate grasslands that were grassland between 2005 and 2014 but were converted to arable land between 2015 and 2019, identify the year in which these grasslands were converted to arable land, and demonstrate regional differences in the conservation of long-term grassland aged 5–9 years and 10 years or more. Long-term grassland aged 10 years or more disappeared faster in urban than in rural areas in Flanders between 2014 and 2019. Our study shows that multi-annual high-resolution satellite imagery provides objective and quantitative information on long-term grassland to support climate, agricultural, environmental, and nature policies.

Understanding crop phenology is crucial for predicting crop yields and identifying potential risks to food security. The objective was to investigate the effectiveness of satellite sensor data, compared to field observations and proximal sensing, in detecting crop phenological stages. Time series data from 122 winter wheat, 99 silage maize, and 77 late potato fields were analyzed during 2015–2017. The spectral signals derived from Digital Hemispherical Photographs (DHP), Disaster Monitoring Constellation (DMC), and Sentinel-2 (S2) were crop-specific and sensor-independent. Models fitted to sensor-derived fAPAR (fraction of absorbed photosynthetically active radiation) demonstrated a higher goodness of fit as compared to fCover (fraction of vegetation cover), with the best model fits obtained for maize, followed by wheat and potato. S2-derived fAPAR showed decreasing variability as the growing season progressed. The use of a double sigmoid model fit allowed defining inflection points corresponding to stem elongation (upward sigmoid) and senescence (downward sigmoid), while the upward endpoint corresponded to canopy closure and the maximum values to flowering and fruit development. Furthermore, increasing the frequency of sensor revisits is beneficial for detecting short-duration crop phenological stages. The results have implications for data assimilation to improve crop yield forecasting and agri-environmental modeling.

Agricultural land use planning is based on the capacity of the soil to support different types of crops and is a prerequisite for better use of cultivated land. Land Suitability Analysis (LSA) is used to measure the level of suitability of growing a specific crop in the area and can also be used to evaluate future scenarios as a means for sustainable agriculture. LSA was employed to calculate current land suitability, as well as four scenarios of Soil-Improving Cropping Systems (SICS): (a) Conservation Tillage (CT), (b) Cover Crop (CC), (c) Crop Residue Management (CRM), and (d) Manure Application (MA). The scenarios of SICS were derived by increasing soil organic matter and cation exchange capacity values depending on the SICS hypothetically applied for a period of 100 years in the future. LSA was evaluated for maize in three sites: (a) Flanders (BE), (b) Somogy (HU), and (c) Hengshui (CH). LSA was performed using the Agricultural Land Use Evaluation System (ALUES) considering soil and climatic and topographic parameters. Weighing factors of input parameters were assigned using the Analytical Hierarchy Process (AHP). The results show that in Flanders, the highly suitable (S2) class covered 3.3% of the total area, and the best scenario for improving current LS was CRM, in which S2 expanded to 9.1%. In Somogy, the S2 class covered 18.3% of the total area, and the best scenarios for improving current land suitability were CT and CC, in both of which the S2 class expanded to 70.5% of the total area. In Hengshui, the S2 class covered 64.7% of the total area, and all SICS scenarios performed extremely well, converting almost all moderately suitable (S3) areas to S2. The main limiting factor that was recognized from a limiting factor analysis in all cases was the climatic conditions. This work proves that LSA can evaluate scenarios of management practices and recognize limiting factors. The proposed methodology is a novel approach that can provide land suitability maps to efficiently evaluate SICS scenarios by projecting soil characteristics and LSA in the future, thus facilitating management decisions of regional policy makers.

Soil salinity has a major impact on agricultural production. In a changing climate with rising sea-levels, low-lying coastal areas are increasingly inundated whereby saltwater gradually contaminates the soil. Drought prone areas may suffer from salinity due to high evapotranspiration rates in combination with the use of saline irrigation water. Salinity is difficult to monitor because soil moisture affects the soil’s spectral signature. We conducted Fourier-transform infrared spectroscopy on alluvial and sandy soil samples in the coastal estuary of the Red River Delta. The soils are contaminated with NaCl, Na2CO3 and Na2SO4 salts. In an experiment of salt contamination, we established that three ranges of the spectrum were strongly influenced by both salt and moisture content in the soil, at wavenumbers 3200–3400 cm−1 (2.9–3.1 µm); 1600–1700 cm−1 (5.9–6.3 µm); 900–1100 cm−1 (9.1–11.1 µm). The Na2CO3 contaminated soil and the spectral value had a linear relationship between wavelengths 6.9 and 7.4 µm. At wavelength 6.99 µm, there was no relationship between absorbance and soil moisture, but the absorbance was proportional to the salt content (R2 = 0.85; RMSE = 0.68 g) and electrical conductivity (R2 = 0.50; RMSE = 3.8 dS/m). The relationship between soil moisture and spectral absorbance value was high at wavelengths below 6.7 µm, resulting in a quadratic relation between soil moisture and absorbance at wavelength 6.13 µm (R2 = 0.80; RMSE = 5.2%). The spectral signatures and equations might be useful for mapping salt-affected soils, particularly in difficult to access locations. Technological advances in thermal satellite sensors may offer possibilities for monitoring soil salinity.

The resilience of quinoa to drought stress conditions makes the crop suitable for the Sahel region. It can support grain production during the dry season and be considered an alternative crop for alleviating food insecurity within the region. Given the importance of this crop outside the indigenous cultivation area, there is a requisite for the development of crop models to facilitate further expansion of quinoa along the Sahel region. Crop water models are of interest due to increasing pressure on water resources, and the portrayal of irrigation scheduling as the best option for water optimisation. The AquaCrop model was selected, as this model simulates crop development and derives both optimal frequencies and net applications of irrigation. Due to limited water resources in the region, different irrigation regimes (full irrigation (FI), progressive drought (PD), deficit irrigation (DI) and extreme deficit irrigation (EDI)) were proposed for analysing yield and biomass responses to water stress conditions. Results suggest that yields were stabilised at around 1.0 Mg ha-1 under PD, thereby prioritising maximum water productivity rather than maximum yields. Water optimisation was attained by watering less at a suggested 310 mm, but with more frequent irrigation events, 28 rather than 20.

Within-season forecasting of crop yields is of great economic, geo-strategic and humanitarian interest. Satellite Earth Observation now constitutes a valuable and innovative way to provide spatio-temporal information to assist such yield forecasts. This study explores different configurations of remote sensing time series to estimate of winter wheat yield using either spatially finer but temporally sparser time series (5daily at 100 m spatial resolution) or spatially coarser but denser (300 m and 1 km at daily frequency) time series. Furthermore, we hypothesised that better yield estimations could be made using thermal time, which is closer to the crop physiological development. Time series of NDVI from the PROBA-V instrument, which has delivered images at a spatial resolution of 100 m, 300 m and 1 km since 2013, were extracted for 39 fields for field and 56 fields for regional level analysis across Northern France during the growing season 2014-2015. An asymmetric double sigmoid model was fitted on the NDVI series of the central pixel of the field. The fitted model was subsequently integrated either over thermal time or over calendar time, using different baseline NDVI thresholds to mark the start and end of the cropping season. These integrated values were used as a predictor for yield using a simple linear regression and yield observations at field level. The dependency of this relationship on the spatial pixel purity was analysed for the 100 m, 300 m and 1 km spatial resolution. At field level, depending on the spatial resolution and the NDVI threshold, the adjusted R² ranged from 0.20 to 0.74; jackknifed – leave-one-field-out cross validation – RMSE ranged from 0.6 to 1.07 t/ha and MAE ranged between 0.46 and 0.90 t/ha for thermal time analysis. The best results for yield estimation (adjusted R² = 0.74, RMSE =0.6 t/ha and MAE =0.46 t/ha) were obtained from the integration over thermal time of 100 m pixel resolution using a baseline NDVI threshold of 0.2 and without any selection based on pixel purity. The field scale yield estimation was aggregated to the regional scale using 56 fields. At the regional level, there was a difference of 0.0012 t/ha between thermal and calendar time for average yield estimations. The standard error of mean results showed that the error was larger for a higher spatial resolution with no pixel purity and smaller when purity increased. These results suggest that, for winter wheat, a finer spatial resolution rather than a higher revisit frequency and an increasing pixel purity enable more accurate yield estimations when integrated over thermal time at the field scale and at the regional scale only if higher pixel purity levels are considered. This method can be extended to larger regions, other crops, and other regions in the world, although site and crop-specific adjustments will have to include other threshold temperatures to reflect the boundaries of phenological activity. In general, however, this methodological approach should be applicable to yield estimation at the parcel and regional scales across the world.

Soil surveys with line-scanning platforms appear to have great advantages over the traditional methods used to collect soil information for the development of field-scale soil mapping and applications. These carry VNIR (visible and near infrared) spectrometers and have been used in recent years extensively for the assessment of soil fertility at the field scale, and the delineation of site-specific management zones (MZ). A challenging feature of VNIR applications in precision agriculture (PA) is the massiveness of the derived datasets that contain point predictions of soil properties, and the interpolation techniques involved in incorporating these data into site-specific management plans. In this study, fixed-rank kriging (FRK) geostatistical interpolation, which is a flexible, non-stationary spatial interpolation method especially suited to handling huge datasets, was applied to massive VNIR soil scanner data for the production of useful, smooth interpolated maps, appropriate for the delineation of site-specific MZ maps. Moreover, auxiliary Sentinel-2 data-based biophysical parameters NDVI (normalized difference vegetation index) and fAPAR (fraction of photosynthetically active radiation absorbed by the canopy) were included as covariates to improve the filtering performance of the interpolator and the ability to generate uniform patterns of spatial variation from which it is easier to receive a meaningful interpretation in PA applications. Results from the VNIR prediction dataset obtained from a pivot-irrigated field in Albacete, southeastern Spain, during 2019, have shown that FRK variants outperform ordinary kriging in terms of filtering capacity, by doubling the noise removal metrics while keeping the computation cost reasonably low. Such features, along with the capacity to handle a large volume of spatial information, nominate the method as ideal for PA applications with massive proximal and remote sensing datasets.

The CORDEX.be project created the foundations for Belgian climate services by producing high-resolution Belgian climate information that (a) incorporates the expertise of the different Belgian climate modeling groups and that (b) is consistent with the outcomes of the international CORDEX (“COordinated Regional Climate Downscaling Experiment”) project. The key practical tasks for the project were the coordination of activities among different Belgian climate groups, fostering the links to specific international initiatives and the creation of a stakeholder dialogue. Scientifically, the CORDEX.be project contributed to the EURO-CORDEX project, created a small ensemble of High-Resolution (H-Res) future projections over Belgium at convection-permitting resolutions and coupled these to seven Local Impact Models. Several impact studies have been carried out. The project also addressed some aspects of climate change uncertainties. The interactions and feedback from the stakeholder dialogue led to different practical applications at the Belgian national level. Keywords: Regional downscaling, Climate impact modeling, Statistical downscaling, Dynamical downscaling, Local Impact Models, Climate change, EURO-CORDEX, Uncertainty estimation, Regional Climate Model, Climate Belgium, Water vapour observations

Soil contamination by heavy metals is of particular concern, due to the direct negative impact on crop yield, food quality and human health. Although the conventional approach to monitor heavy metals relies on field sampling and lab analysis, the proliferation in the use of portable spectrometers has reduced the cost and time of investigation. However, discrepancies in spectral data from different spectrometers increase the modeling time and undermine the model accuracy for spatial mapping. This study, therefore, took advantage of the readily accessible Landsat 7 data to predict and map the spatiotemporal distribution of ten heavy metals (i.e., Sb, Pb, Ni, Mn, Hg, Cu, Cr, Co, Cd and As) over a 640 km2 area in Belgium. The Land Use/Cover Area Frame Survey (LUCAS) database of a region in north-eastern Belgium was used to retrieve variation in heavy metals concentrations over time and space, using the Landsat 7 imagery for four single dates in 2009, 2013, 2016 and 2020. Three regression methods, namely, partial least squares regression (PLSR), random forest (RF) and support vector machine (SVM) were used to model and predict the heavy metal concentrations for 2009. By comparing these models unbiasedly, the best model was selected for predicting and mapping the heavy metal distributions for 2013, 2016 and 2020. RF turned out to be the optimal model for 2009 with a coefficient of determination of prediction (R2P) and residual prediction deviation of prediction (RPDP) ranging from 0.62 to 0.92, and 1.23 to 2.79, respectively. The measured heavy metal distributions along the river floodplains, at the highlands and in the lowlands, were generally high, compared to their RF spatiotemporal predictions, which decreased over time. Increasing moisture contents in the floodplains adjacent to the river channels and the lowlands were the primary contributors to the reduction in the satellite reflectance spectra. However, topsoil erosion from rainfall, snowmelt as well as wind into the lowlands could have influenced the reduction in heavy metal spatiotemporal predicted values over time in the highlands. The spatiotemporal prediction maps produced for the heavy metals for the four different years revealed a good spatial similarity and consistency with the measured maps for 2009, which indicates their stability over the years.

Medium resolution satellite data, such as Sentinel-2 of the Copernicus programme, offer great new opportunities for the agricultural sector, and provide insights on soil surface characteristics and their management. Soil monitoring requires a high-quality dataset of uncovered and plastic covered agricultural soil. We developed a methodology to identify uncovered soil pixels in agricultural parcels during seedbed preparation and considered the impacts of clouds and shadows, vegetation cover, and artificial covers, such as those of greenhouses and plastic mulch films. We preserved the spatial and temporal integrity of parcels in the process and analysed spectral anomalies and their sources. The approach is based on freely available tools, namely Google Earth Engine and R Programming packages. We tested the methodology on the northern region of Belgium, which is characterised by small, fragmented parcels. We selected a period between mid-April to end-May, when active agricultural management practices leave the soil bare in preparation for the main cropping season. The spectral angle mapper was used to identify soil covered by non-plastic greenhouses or temporary soil covers, such as plastic mulch films. The effect of underlying soil on temporary covers was considered. The retrogressive plastic greenhouse index was used for detecting plastic greenhouses. The result was a high quality dataset of potential bare uncovered agricultural soil that allows further soil surface characterisation. This offered an improved understanding of the use of artificial covers, their spatial distribution, and their corresponding crops during the considered period. Artificial covers occurred most frequently in maize parcels. The approach resulted in precision values exceeding 0.9 for the detection of temporary covers and non-plastic greenhouses and a sensitivity value exceeding 0.95 for non-plastic and plastic greenhouses.

Attractive landscapes are diverse and resilient landscapes that provide a multitude of essential ecosystem services. The development of landscape policy to protect and improve landscape attractiveness, thereby ensuring the provision of ecosystem services, is ideally adapted to region specific landscape characteristics. In addition, trends in landscape attractiveness may be linked to certain policies, or the absence of policies over time. A spatial and temporal evaluation of landscape attractiveness is thus desirable for landscape policy development. In this paper, landscape attractiveness was spatially evaluated for Flanders (Belgium) using landscape indicators derived from geospatial data as a case study. Large local differences in landscape quality in (i) rural versus urban areas and (ii) between the seven agricultural regions in Flanders were found. This observed spatial variability in landscape attractiveness demonstrated that a localized approach, considering the geophysical characteristics of each individual region, would be required in the development of landscape policy to improve landscape quality in Flanders. Some trends in landscape attractiveness were related to agriculture in Flanders, e.g., a slight decrease in total agricultural area, decrease in dominance of grassland, maize and cereals, a decrease in crop diversity, sharp increase in the adoption of agri-environmental agreements (AEA) and a decrease in bare soil conditions in winter. The observed trends and spatial variation in landscape attractiveness can be used as a tool to support policy analysis, assess the potential effects of future policy plans, identify policy gaps and evaluate past landscape policy.

Information on crop yield at scales ranging from the field to the global level is imperative for farmers and decision makers. The current data sources to monitor crop yield, such as regional agriculture statistics, are often lacking in spatial and temporal resolution. Remotely sensed vegetation indices (VIs) such as NDVI are able to assess crop yield using empirical modelling strategies. Empirical NDVI-based crop yield models were evaluated by comparing the model performance with similar models used in different regions. The integral NDVI and the peak NDVI were weak predictors of winter wheat yield in northern Belgium. Winter wheat (Triticum aestivum) yield variability was better predicted by monthly precipitation during tillering and anthesis than by NDVI-derived yield proxies in the period from 2016 to 2018 (R2 = 0.66). The NDVI series were not sensitive enough to yield affecting weather conditions during important phenological stages such as tillering and anthesis and were weak predictors in empirical crop yield models. In conclusion, winter wheat yield modelling using NDVI-derived yield proxies as predictor variables is dependent on the environment.

A one-dimensional simulation model that simulates daily mean soil temperature on a daily time-step basis, named AGRISOTES (AGRIcultural SOil TEmperature Simulation), is described. It considers ground coverage by biomass or a snow layer and accounts for the freeze/thaw effect of soil water. The model is designed for use on agricultural land with limited (and mostly easily available) input data, for estimating soil temperature spatial patterns, for single sites (as a stand-alone version), or in context with agrometeorological and agronomic models. The calibration and validation of the model are carried out on measured soil temperatures in experimental fields and other measurement sites with various climates, agricultural land uses and soil conditions in Europe. The model validation shows good results, but they are determined strongly by the quality and representativeness of the measured or estimated input parameters to which the model is most sensitive, particularly soil cover dynamics (biomass and snow cover), soil pore volume, soil texture and water content over the soil column.

A framework was developed to elucidate (1) the drivers of land degradation, (2) pressures, (3) local impacts and vulnerabilities and (4) adaptation strategies. The combination of participatory approaches, statistical data analysis, time series Landsat imagery and spatial data mining was tested in southeast Vietnam where the impacts of land degradation on the environment and economy are considerable. The major drivers of land degradation are climate, notably drought, and population density. The pressures include natural resource management and land use/cover change. A Landsat archive analysis showed an increase in agricultural land use from 31% to 50%, mostly at the expense of forests, from 1990 to 2019. Farmers adapted by investing in the irrigation of rice and dragon fruit, and by selecting their rainfed crops in line with the changing environment. The most vulnerable were the rural poor and farmers without access to land and water resources. The best protection against land degradation was prosperity, which is enhanced by the region’s location along Vietnam’s major national route, connecting major cities along a north–south axis. Our analysis shows that southeast Vietnam emerged as a region with an important human ecological resilience strengthened by increased prosperity. The current adaptation options and limitations warrant further research.

Wheat yield variability will increase in the future due to the projected increase in extreme weather events and long-term climate change effects. Currently, regional agricultural statistics are used to monitor wheat yield. Remotely sensed vegetation indices have a higher spatio-temporal resolution and could give more insight into crop yield. In this paper, we (i) evaluate the possibility to use Normalized Difference Vegetation Index (NDVI) time series to estimate wheat yield in Latvia and (ii) determine which weather variables impact wheat yield changes using both ALARO-0 and REMO Regional Climate Models (RCM) output. The integral from NDVI series (aNDVI) for winter and spring wheat fields is used as a predictor to model regional wheat yield from 2014 to 2018. A correlation analysis between weather variables, wheat yield and aNDVI was used to elucidate which weather variables impact wheat yield changes in Latvia. Our results indicate that high temperatures in June for spring wheat and in July for winter wheat had a negative correlation with yield. A linear regression yield model explained 71% of the variability with a residual standard error of 0.55 Mg/ha. When RCM data were added as predictor variables to the wheat yield empirical model a random forest approach resulted in better results compared to a linear regression approach, the explained variance increased up to 97% and the residual standard error decreased to 0.17 Mg/ha. We conclude that NDVI time series and RCM output enabled regional crop yield and weather impact monitoring at higher spatio-temporal resolutions than regional statistics.

Based on observations for the beginning of the flowering stage of Malus domestica (apple) and Pyrus communis (pear) for the 1950–2018 period, phenological trends in north-eastern Belgium were investigated in function of temperatures during dormancy. Moreover, two different phenological models were adapted and evaluated. Median flowering dates of apple were on average 9.5 days earlier following warm dormancy periods, and 11.5 days for pear, but the relationship between bloom date and temperature was found not to be linear, suggesting delayed fulfilment of dormancy requirements due to increased temperatures during the chilling period. After warm chilling periods, an average delay of 5.0 and 10.6 days in the occurrence date of dormancy break was predicted by the phenological models while the PLSR reveals mixed signals regarding the beginning of flowering. Our results suggest overlapping chilling and forcing processes in a transition phase. Regarding the beginning of flowering, a dynamic chill model coupled to a growing degree days estimation yielded significantly lower prediction errors (on average 5.0 days) than a continuous chill-forcing model (6.0 days), at 99% confidence level. Model performance was sensitive to the applied parametrization method and limitations for the application of both models outside the past temperature ranges became apparent.

A timely inventory of agricultural areas and crop types is an essential requirement for ensuring global food security and allowing early crop monitoring practices. Satellite remote sensing has proven to be an increasingly more reliable tool to identify crop types. With the Copernicus program and its Sentinel satellites, a growing source of satellite remote sensing data is publicly available at no charge. Here, we used joint Sentinel-1 radar and Sentinel-2 optical imagery to create a crop map for Belgium. To ensure homogenous radar and optical inputs across the country, Sentinel-1 12-day backscatter mosaics were created after incidence angle normalization, and Sentinel-2 normalized difference vegetation index (NDVI) images were smoothed to yield 10-daily cloud-free mosaics. An optimized random forest classifier predicted the eight crop types with a maximum accuracy of 82% and a kappa coefficient of 0.77. We found that a combination of radar and optical imagery always outperformed a classification based on single-sensor inputs, and that classification performance increased throughout the season until July, when differences between crop types were largest. Furthermore, we showed that the concept of classification confidence derived from the random forest classifier provided insight into the reliability of the predicted class for each pixel, clearly showing that parcel borders have a lower classification confidence. We concluded that the synergistic use of radar and optical data for crop classification led to richer information increasing classification accuracies compared to optical-only classification. Further work should focus on object-level classification and crop monitoring to exploit the rich potential of combined radar and optical observations.

A method was developed for crop area mapping inspired by spectral matching techniques (SMTs) and based on phenological characteristics of different crop types applied using 100-m Proba-V NDVI data for the season 2014–2015. Ten-daily maximum value NDVI composites were created and smoothed in SPIRITS (spirits.jrc.ec.europa.eu). The study sites were globally spread agricultural areas located in Flanders (Belgium), Sria (Russia), Kyiv (Ukraine) and Sao Paulo (Brazil). For each pure pixel within the field, the NDVI profile of the crop type for its growing season was matched with the reference NDVI profile based on the training set extracted from the study site where the crop type originated. Three temporal windows were tested within the growing season: green-up to senescence, green-up to dormancy and minimum NDVI at the beginning of the growing season to minimum NDVI at the end of the growing season. Post classification rules were applied to the results to aggregate the crop type at the plot level. The overall accuracy (%) ranged between 65 and 86, and the kappa coefficient changed from 0.43–0.84 according to the site and the temporal window. In order of importance, the crop phenological development period, parcel size, shorter time window, number of ground-truth parcels and crop calendar similarity were the main reasons behind the differences between the results. The methodology described in this study demonstrated that 100-m Proba-V has the potential to be used in crop area mapping across different regions in the world.