Enhanced biomass prediction by assimilating satellite data into a crop growth model

Machwitz, Miriam; Giustarini, Laura; Bossung, Christian; Frantz, David; Schlerf, Martin; Lilienthal, Holger; Wandera, Loise; Matgen, Patrick; Hoffmann, Lucien; Udelhoven, Thomas

doi:10.1016/j.envsoft.2014.08.010

Cited by 51 publications

(27 citation statements)

References 56 publications

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“…These ranged from spatial issues, from small areas of heterogeneity caused by the behaviour of grazing animals , to the need for multi-paddock simulations to capture farm performance when that performance is dependent on the interaction of several paddocks rather than just the aggregation of many single paddocks Holzworth et al, 2014;Moore et al, 2014), to several papers (Balbi et al, 2014;Elliott et al, 2014;Kang et al, 2014;Le et al, 2014;Machwitz et al, 2014;McNider et al, 2014;Porter et al, 2014; While ensemble modelling has been in common usage by climate modellers for some time, as a simulation method it is relatively new to agricultural production system modelling. However, it is quickly emerging as a useful way for modellers to interact with each other to address emerging issues, such as on climate change through the AgMIP Project Rosenzweig et al, 2013).…”

Section: Reflections From This Thematic Issuementioning

confidence: 98%

Agricultural production systems modelling and software: Current status and future prospects

Holzworth

Snow

Janssen

et al. 2015

Environmental Modelling & Software

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Section: Reflections From This Thematic Issuementioning

confidence: 98%

Agricultural production systems modelling and software: Current status and future prospects

Holzworth

Snow

Janssen

et al. 2015

Environmental Modelling & Software

191

105

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“…The explicit spatial validation of in-field patterns is one of the major achievements of this study compared to other recent approaches in the field of remote sensing supported yield modeling, e.g., [28,64,73]. While other studies (e.g., [63] and [21]) use simulated and observed soil-adjusted vegetation indices for the assimilation, in our approach the greenLAI is used as common variable between model and observation.…”

Section: Discussionmentioning

confidence: 99%

“…For example, all scenarios of the ensemble are considered with the same probability. Ensemble Kalman filtering, as e.g., proposed by [24], or particle filtering, as e.g., applied by [64], is not yet part of the assimilation procedure. Although the results achieved with this method are very accurate, they largely depend on the quality of the greenLAI retrieval.…”

Section: Discussionmentioning

confidence: 99%

“…PROMET ensembles can, for instance, also be generated by varying the seeding date, such as e.g. proposed by [63,64], or by manipulating the velocity of phenological progress. The general advantage of the ensemble approach, compared to externally forcing updates of certain variables, is that the model internally remains consistent.…”

Section: Parameter Retrieval From Earth Observation Datamentioning

confidence: 99%

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Using a Remote Sensing-Supported Hydro-Agroecological Model for Field-Scale Simulation of Heterogeneous Crop Growth and Yield: Application for Wheat in Central Europe

2015

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Abstract:The challenge of converting global agricultural food, fiber and energy crop cultivation into an ecologically and economically sustainable production process requires the most efficient agricultural management strategies. Development, control and maintenance of these strategies are highly dependent on temporally and spatially continuous information on crop status at the field scale. This paper introduces the application of a process-based, coupled hydro-agroecological model (PROMET) for the simulation of temporally and spatially dynamic crop growth on agriculturally managed fields. By assimilating optical remote sensing data into the model, the simulation of spatial crop dynamics is improved to a point where site-specific farming measures can be supported. Radiative transfer modeling (SLC) is used to provide maps of leaf area index from Earth Observation (EO). These maps are used in an assimilation scheme that selects closest matches between EO and PROMET ensemble runs. Validation is provided for winter wheat (years 2004, 2010 and 2011). Field samples validate the temporal dynamics of the simulations (avg. R² = 0.93) and > 700 ha of calibrated combine harvester data are used for accuracy assessment of the spatial yield simulations (avg. RMSE = 1.15 t·ha −1). The study shows that precise simulation of field-scale crop growth and yield is possible, if spatial remotely sensed information is combined with temporal dynamics provided by land surface process models. The presented methodology represents a technical solution to make the best

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“…In this regard, parameters like chlorophyll content, above ground biomass dry matter, nitrogen status, canopy water content and leaf area index (LAI) provide important information for describing current growth conditions, and thus can be converted into yield-driving state variables (e.g., dry mass increase), which were used for the re-parameterization of agricultural production models [6][7][8][9]. The LAI is one of the most important plant parameters and serves as an essential variable for assimilating remote sensing data into crop growth models [10,11]. It is defined as the ratio of the total one-sided leaf surface area per unit soil surface area [12].…”

Section: Introductionmentioning

confidence: 99%

The Potential of Pan-Sharpened EnMAP Data for the Assessment of Wheat LAI

et al. 2015

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Abstract:In modern agriculture, the spatially differentiated assessment of the leaf area index (LAI) is of utmost importance to allow an adapted field management. Current hyperspectral satellite systems provide information with a high spectral but only a medium spatial resolution. Due to the limited ground sampling distance (GSD), hyperspectral satellite images are often insufficient for precision agricultural applications. In the presented study, simulated hyperspectral data of the upcoming Environmental Mapping and Analysis Program (EnMAP) mission (30 m GSD) covering an agricultural region were pan-sharpened with higher resolution panchromatic aisaEAGLE (airborne imaging spectrometer for applications EAGLE) (3 m GSD) and simulated Sentinel-2 images (10 m GSD) using the spectral preserving Ehlers Fusion. As fusion evaluation criteria, the spectral angle (αspec) and the correlation coefficient (R) were calculated to determine the spectral preservation capability of the fusion results. Additionally, partial least squares regression (PLSR) models were built based on the EnMAP images, the fused datasets and the original aisaEAGLE hyperspectral data to spatially predict the LAI of two wheat fields. The aisaEAGLE model provided the best results (R 2 cv = 0.87) followed by the models built with the fused datasets (EnMAP-aisaEAGLE and EnMAP-Sentinel-2 fusion each with a R 2 cv of 0.75) and the simulated EnMAP data (R 2 cv = 0.68). The results showed the suitability of pan-sharpened EnMAP data for a reliable spatial prediction of LAI and underlined the OPEN ACCESSRemote Sens. 2015, 7 12738 potential of pan-sharpening to enhance spatial resolution as required for precision agriculture applications.

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Enhanced biomass prediction by assimilating satellite data into a crop growth model

Cited by 51 publications

References 56 publications

Agricultural production systems modelling and software: Current status and future prospects

Agricultural production systems modelling and software: Current status and future prospects

Using a Remote Sensing-Supported Hydro-Agroecological Model for Field-Scale Simulation of Heterogeneous Crop Growth and Yield: Application for Wheat in Central Europe

The Potential of Pan-Sharpened EnMAP Data for the Assessment of Wheat LAI

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