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 possible use of the growing stream of EO data in the context of sustainable land surface management.
Highlights
The challenge of sustainably providing food, fiber and energy from limited resources for an increasing world population requires a most efficient use of the biologically productive land surface
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
This paper aims at bridging this gap by extending an existing spatially explicit hydrological model (PROMET) [20] with a highly sophisticated representation of dynamic vegetation processes at the land surface and by using the coupled model for the assimilation of remote sensing observations
Summary
The challenge of sustainably providing food, fiber and energy from limited resources for an increasing world population requires a most efficient use of the biologically productive land surface. And economically efficient agricultural management thereby is highly dependent on detailed temporal and spatial knowledge of the processes affecting physiological crop development. Understanding natural heterogeneity patterns is the basis of all site-specific management decisions in the context of precision agriculture. Data on spatial heterogeneity of fields, though essential, can hardly be acquired with conventional in situ methods due to restrictions such as labor intensity, limited accessibility of large continuous acreages, crop damage through destructive sampling etc. Essential for process understanding, can only be provided for selected sampling points and largely fail to represent the natural heterogeneity of growth conditions that can be found within the boundaries of agricultural fields
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