A review on reflective remote sensing and data assimilation techniques for enhanced agroecosystem modeling

By Wouter A. Dorigo, Raúl Zurita-Milla, Allard de Wit, Jason Brazile, Ranvir Singh, Michael E. Schaepman. International Journal of Applied Earth Observation and Geoinformation, 2007

648 citations recorded by the source corpus.

Subfields: Ecology, Environmental Engineering, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/f36a0145dc55fb1bb33df1bae2c2d096d86682bd

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Citations in the corpus

Towards regional grain yield forecasting with 1km-resolution EO biophysical products: Strengths and limitations at pan-European level (2015, background): “In this specific context, the assimilation of remote sensing information into crop models at regional scale should be further investigated (Launay and Guerif, 2005; Dorigo et al., 2007) as a way to enhance the capabilities of crop models with remote observations of crop development and growth.”
Experimental Sentinel-2 LAI estimation using parametric, non-parametric and physical retrieval methods - A comparison (2015, background): “Optical Earth observing satellites, endowed with a high temporal resolution, enable the retrieval and hence monitoring of plant bio-geophysical variables (Moulin et al., 1998; Dorigo et al., 2007).”
A Bayesian object based approach for estimating vegetation biophysical and biochemical variables from APEX at sensor radiance data (2013, background): “Such statistical relationships require extensive field data, are specific to the vegetation type, development stage, study site and are only valid for a given sensor spectral configuration and acquisition geometry (Dorigo et al., 2007; Ustin et al., 2009).”
A physically-based model for retrieving foliar biochemistry and leaf orientation using close-range imaging spectroscopy (2016, neutral): “At the leaf level, these models range from simple plate models, to ray-tracing, radiosity and stochastic models that are computationally more dif ﬁ cult to invert (Dorigo et al., 2007).”
Earth system science related imaging spectroscopy — an assessment (2009, background): “Finally, there is a trend toward establishment of integrated systems solutions and supporting data assimilation (Dorigo et al., 2007; Fang et al., 2008) as well as data fusion based approaches (Gomez et al., 2001; Robinson et al., 2000; Zurita-Milla et al., 2008).”
Estimating leaf nitrogen concentration with three-band vegetation indices in rice and wheat (2012, background): “However, some factors such as biophysical properties of vegetation, vegetation canopy structure, soil background and atmospheric influence will affect the performance of spectral reflectance of plant canopy, implying that it is quite challenging to efficiently obtain the plant N status by using cano”
Assimilation of remote sensing into crop growth models: Current status and perspectives (2019, background): “Different from previous data assimilation reviews (Maas, 1988; Delécolle et al., 1992; Liang, 2004; Dorigo et al., 2007; Lewis et al., 2012; Kasampalis et al., 2018; Jin et al., 2018), this review focuses on the techniques in DA and explaining what approach should be used in which circumstances and ”
Bayesian object-based estimation of LAI and chlorophyll from a simulated Sentinel-2 top-of-atmosphere radiance image (2014, background): “statistical relationships, however, require extensive field data collection and are only valid for the specific conditions for which they were developed, including sensor, acquisition geometry, and vegetation type (Dorigo et al., 2007; Ustin et al., 2009).”
Integrating remotely sensed leaf area index and leaf nitrogen accumulation with RiceGrow model based on particle swarm optimization algorithm for rice grain yield assessment (2014, methodology): “In general, those crop growth indices that can be retrieved effectively by RS and are sensitive to the assimilated input parameters are selected as the assimilation parameter.(36) In this paper, LAI and LNA together were determined as the assimilation parameter through simple PS analysis.”
Assimilating a synthetic Kalman filter leaf area index series into the WOFOST model to improve regional winter wheat yield estimation (2016, methodology): “Several previous studies indicated that, because of such coarse spatial resolution, the MODIS LAI products that are commonly used for data assimilation cannot achieve satisfactory assimilation results for regions where wheat is cultivated (Duveiller et al., 2011; Xu et al., 2011; Ma et al., 2013a; H”