Retrieval of leaf area index in different vegetation types using high resolution satellite data

By Roberto Colomboa, Dario Bellingerib, Dante Fasolinic, Carlo M. Marinob. 2003

401 citations recorded by the source corpus.

Subfields: Ecology, Global and Planetary Change, Plant Science

View Paper: https://www.semanticscholar.org/paper/8f920d9ca99ae2eff4041d97463138aaf2ac9722

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Citations in the corpus

Review of optical-based remote sensing for plant trait mapping (2013, background): “Consequently, predictive statistical relationships often suffer from lack of robustness and transferability as they are usually site, species and time specific (Colombo et al., 2003; Grossman et al., 1996).”
Estimation of LAI and fractional cover from small footprint airborne laser scanning data based on gap fraction (2006, methodology): “Many approaches were focused on the retrieval of these parameters from passive optical systems, often by the use of regression models (Cohen et al., 2003; Colombo et al., 2003), and in some cases by using radiative transfer modeling (Koetz et al.”
Practical guidelines for choosing GLCM textures to use in landscape classification tasks over a range of moderate spatial scales (2017, methodology): “Therefore, inclusion of a texture band often improves classification accuracy, no matter what approach or decision rule is used (Marceau et al. 1990; Ferro and Warner 2002; Colombo et al. 2003; Myint, Lam, and Tyler 2004).”
Global Data Sets of Vegetation Leaf Area Index (LAI)3g and Fraction of Photosynthetically Active Radiation (FPAR)3g Derived from Global Inventory Modeling and Mapping Studies (GIMMS) Normalized Difference Vegetation Index (NDVI3g) for the Period 1981 to 2011 (2013, result): “Site- and vegetation-specific empirical relationships between NDVI and LAI, for example, have been used in some studies [21,22].”
Extracting leaf area index using viewing geometry effects—A new perspective on high-resolution unmanned aerial system photography (2018, methodology): “Another ap58 proach to estimate LAI is based on passive multispectral reflectance measurements, processed with band 59 arithmetic calculations and regression models (e.g. Brown et al. 2000; Colombo et al. 2003; Haboudane 60 et al. 2004; Hunt et al. 2010; Gonsamo and Pellikka 2012; Reichenau et al. 2”
LAI assessment of wheat and potato crops by VENμS and Sentinel-2 bands (2011, background): “On the other hand Colombo et al. (2003) found no change in accuracy for pixel sizes of 12, 24, and 36 m.”
Large‐Area Maize Yield Forecasting Using Leaf Area Index Based Yield Model (2005, background): “While there have been studies on LAI in relation to crop monitoring and assessment (Pollock and Ka- Study Area nemasu, 1979; Colombo et al., 2003), there is a need to The study area is the state of Sinaloa, Mexico, which hasexplore further its use in large-area yield prediction. the country’s most…”
Review of Machine Learning Approaches for Biomass and Soil Moisture Retrievals from Remote Sensing Data (2015, background): “This limits the possibility to extend their use to different areas and different remote sensing systems, since they remain valid only under the conditions in which reference samples have been collected [22,26].”
LAI and chlorophyll estimation for a heterogeneous grassland using hyperspectral measurements (2008, background): “, 2000), non-grassed plants such as agricultural crops (Atzberger, 1995, 1997; Baret et al., 1987; Broge and Mortensen, 2002; Colombo et al., 2003; Danson et al., 2003; Gitelson et al., 2003; Jacquemoud et al., 2000; Walter-Shea et al., 1993; Weiss et al., 2001) or forests (Chen et al.”
Comparison of different vegetation indices for the remote assessment of green leaf area index of crops (2011, background): “Applications of vegetation indices have ranged from leaf to global levels, and in the case of Green LAI, some successes have been obtained for different crops (Boegh et al., 2002; Broge andMortensen, 2002; Clevers, 1989; Colombo et al., 2003; Curran, 1983a, b; Xiao et al., 2002).”