A semantic approach for timeseries data fusion
Samourkasidis, Argyrios ; Athanasiadis, Ioannis N. - \ 2020
Computers and Electronics in Agriculture 169 (2020). - ISSN 0168-1699
AgMIP - APSIM - Data reuse - DSSAT - Environmental timeseries - FAIR data - Internet of Things - Interoperability - Legacy data - Reasoning - Semantic heterogeneity - Templates - WOFOST
The data deluge following the rise of Internet of Things contributes towards the creation of non-reusable data silos. Especially in the environmental sciences domain, syntactic and semantic heterogeneity hinders data re-usability as most times manual labour and domain expertise is required. Both the different syntaxes under which environmental timeseries are formatted and the implicit semantics which are used to describe them contribute to this end. Usually, the real meaning of data is obscured in a combination of short data labels, titles and various value codes, that require domain or institutional knowledge to decipher. The FAIR data principles for scientific data sharing are stewardship offer a framework based on community-adopted metadata. In this work, we present the Environmental Data Acquisition Module (EDAM) which focuses on data interoperability and reuse, and deals with syntactic and semantic heterogeneity using a template approach. Data curators draft templates to describe in an abstract fashion the syntax of the timeseries datasets they want to acquire or disseminate. They complement each template with a metadata file, which is used to annotate observables and their properties (including physical quantities and units of measurement) with terms from an ontology. EDAM employs a reasoner to infer compatibility among syntactically and semantically heterogeneous datasets, and enables timeseries, format and units of measurement transformation on-the-fly. Our approach utilizes a local ontology to store metadata about datasets, which enables EDAM to acquire and transform datasets which were originally stored with different semantics and syntaxes. We demonstrate EDAM in a case study where we transform meteorological input files of four agricultural models. Our approach, allows to cut across environmental data silos and facilitate timeseries reusability, as it enables users to (a) discover datasets in other formats, (b) transform them and (c) reuse them in their scientific workflows. This directly contributes to the toolshed for FAIR data management in environmental sciences. EDAM implementation has been released under an open-source license.
Joint Assimilation of Leaf Area Index and Soil Moisture from Sentinel-1 and Sentinel-2 Data into the WOFOST Model for Winter Wheat Yield Estimation
Pan, Haizhu ; Chen, Zhongxin ; Wit, Allard de; Ren, Jianqiang - \ 2019
Sensors 19 (2019)14. - ISSN 1424-8220
data assimilation - EnKF - LAI - Sentinel-1 and Sentinel-2 - SM - winter wheat yield - WOFOST
It is well known that timely crop growth monitoring and accurate crop yield estimation at a fine scale is of vital importance for agricultural monitoring and crop management. Crop growth models have been widely used for crop growth process description and yield prediction. In particular, the accurate simulation of important state variables, such as leaf area index (LAI) and root zone soil moisture (SM), is of great importance for yield estimation. Data assimilation is a useful tool that combines a crop model and external observations (often derived from remote sensing data) to improve the simulated crop state variables and consequently model outputs like crop total biomass, water use and grain yield. In spite of its effectiveness, applying data assimilation for monitoring crop growth at the regional scale in China remains challenging, due to the lack of high spatiotemporal resolution satellite data that can match the small field sizes which are typical for agriculture in China. With the accessibility of freely available images acquired by Sentinel satellites, it becomes possible to acquire data at high spatiotemporal resolution (10-30 m, 5-6 days), which offers attractive opportunities to characterize crop growth. In this study, we assimilated remotely sensed LAI and SM into the Word Food Studies (WOFOST) model to estimate winter wheat yield using an ensemble Kalman filter (EnKF) algorithm. The LAI was calculated from Sentinel-2 using a lookup table method, and the SM was calculated from Sentinel-1 and Sentinel-2 based on a change detection approach. Through validation with field data, the inverse error was 10% and 35% for LAI and SM, respectively. The open-loop wheat yield estimation, independent assimilations of LAI and SM, and a joint assimilation of LAI + SM were tested and validated using field measurement observation in the city of Hengshui, China, during the 2016-2017 winter wheat growing season. The results indicated that the accuracy of wheat yield simulated by WOFOST was significantly improved after joint assimilation at the field scale. Compared to the open-loop estimation, the yield root mean square error (RMSE) with field observations was decreased by 69 kg/ha for the LAI assimilation, 39 kg/ha for the SM assimilation and 167 kg/ha for the joint LAI + SM assimilation. Yield coefficients of determination (R2) of 0.41, 0.65, 0.50, and 0.76 and mean relative errors (MRE) of 4.87%, 4.32%, 4.45% and 3.17% were obtained for open-loop, LAI assimilation alone, SM assimilation alone and joint LAI + SM assimilation, respectively. The results suggest that LAI was the first-choice variable for crop data assimilation over SM, and when both LAI and SM satellite data are available, the joint data assimilation has a better performance because LAI and SM have interacting effects. Hence, joint assimilation of LAI and SM from Sentinel-1 and Sentinel-2 at a 20 m resolution into the WOFOST provides a robust method to improve crop yield estimations. However, there is still bias between the key soil moisture in the root zone and the Sentinel-1 C band retrieved SM, especially when the vegetation cover is high. By active and passive microwave data fusion, it may be possible to offer a higher accuracy SM for crop yield prediction.
Agrohydrological analysis of groundwater recharge and land use changes in the Pampas of Argentina
Kroes, Joop ; Dam, Jos van; Supit, Iwan ; Abelleyra, Diego de; Verón, Santiago ; Wit, Allard de; Boogaard, Hendrik ; Angelini, Marcos ; Damiano, Francisco ; Groenendijk, Piet ; Wesseling, Jan ; Veldhuizen, Ab - \ 2019
Agricultural Water Management 213 (2019). - ISSN 0378-3774 - p. 843 - 857.
Argentina - Capillary rise - Groundwater recharge - Land use - Pampas - Soybean - SWAP - WOFOST
This paper studies the changes of groundwater, climate and land use in the Pampas of Argentina. These changes offer opportunities and threats. Lowering groundwater without irrigation causes drought and successive crop and yield damage. Rising groundwater may alleviate drought as capillary rise supports root water uptake and crop growth, thus narrowing the difference between potential and actual yields. However, rising groundwater may also limit soil water storage, cause flooding in metropolitan areas and have a negative impact on crop yields. Changing land use from continuous soy bean into crop rotations or natural vegetation may decrease groundwater recharge and thus decrease groundwater levels. In case of crop rotation however, leaching of nutrients like nitrate may increase. We quantified these impacts using integrated dynamic crop growth and soil hydrology modelling. The models were tested at field scale using a local dataset from Argentina. We applied distributed modelling at regional scale to evaluate the impacts on groundwater recharge and crop yields using long term weather data. The experiments showed that threats arise from continuous monotone land use. Opportunities are created when a proper balance is found between supply and demand of soil water using a larger differentiation of land use. Increasing the areas of land use types with higher evapotranspiration, like permanent grassland and trees, will contribute to a more stable hydrologic system with more water storage capacities in the soil system and lower groundwater levels. Modelling tools clearly support the evaluation of the impact of land use and climate change on groundwater levels and crop yields.
Improving WOFOST model to simulate winter wheat phenology in Europe : Evaluation and effects on yield
Ceglar, A. ; Wijngaart, R. van der; Wit, A. de; Lecerf, R. ; Boogaard, H. ; Seguini, L. ; Berg, M. van den; Toreti, A. ; Zampieri, M. ; Fumagalli, D. ; Baruth, B. - \ 2019
Agricultural Systems 168 (2019). - ISSN 0308-521X - p. 168 - 180.
Calibration - Crop yield forecasting - Europe - Phenology - Triticum aestivum - WOFOST
This study describes and evaluates improvements to the MARS crop yield forecasting system (MCYFS) for winter soft wheat (Triticum aestivum) in Europe, based on the WOFOST crop simulation model, by introducing autumn sowing dates, realistic soil moisture initialization, adding vernalization requirements and photoperiodicity, and phenology calibration. Dataset of phenological observations complemented with regional cropping calendars across Europe is used. The calibration of thermal requirements for anthesis and maturity is done by pooling all available observations within European agro-environmental zones and minimizing an objective function that combines the differences between observed and simulated anthesis, maturity and harvest dates. Calibrated phenology results in substantial improvement in simulated dates of anthesis with respect to the original MCYFS simulations. The combined improvements to the system result in a physically more plausible spatial distribution of crop model indicators across Europe. Crop yield indicators point to better agreement with recorded national winter wheat yields with respect to the original MCYFS simulations, most pronounced in central, eastern and southern Europe. However, model skill remains low in large parts of western Europe, which may possibly be attributed to the impacts of wet conditions.