Integration of MODIS LAI and vegetation index products with the CSM–CERES–Maize model for corn yield estimation

Fang, Hongliang; Liang, Shunlin; Hoogenboom, Gerrit

doi:10.1080/01431160903505310

Cited by 169 publications

(83 citation statements)

References 55 publications

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“…LAI estimation can be used to select the populations with the greatest leaf area as the most vigorous ones, as early vigor gives an advantage over weeds [53,54]. VI-based LAI estimation could also be potentially used in optimizing crop production and the development of best crop management practices, such as the timing of application of water, fertilizers, and pesticides [55][56][57].…”

Section: Discussionmentioning

confidence: 99%

Effects of Crop Leaf Angle on LAI-Sensitive Narrow-Band Vegetation Indices Derived from Imaging Spectroscopy

Zou

Haikarainen

et al. 2018

Applied Sciences

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Leaf area index (LAI) is an important biophysical variable for understanding the radiation use efficiency of field crops and their potential yield. On a large scale, LAI can be estimated with the help of imaging spectroscopy. However, recent studies have revealed that the leaf angle greatly affects the spectral reflectance of the canopy and hence imaging spectroscopy data. To investigate the effects of the leaf angle on LAI-sensitive narrowband vegetation indices, we used both empirical measurements from field crops and model-simulated data generated by the PROSAIL canopy reflectance model. We found the relationship between vegetation indices and LAI to be notably affected, especially when the leaf mean tilt angle (MTA) exceeded 70 degrees. Of the indices used in the study, the modified soil-adjusted vegetation index (MSAVI) was most strongly affected by leaf angles, while the blue normalized difference vegetation index (BNDVI), the green normalized difference vegetation index (GNDVI), the modified simple ratio using the wavelength of 705 nm (MSR 705 ), the normalized difference vegetation index (NDVI), and the soil-adjusted vegetation index (SAVI) were only affected for sparse canopies (LAI < 3) and MTA exceeding 60 • . Generally, the effect of MTA on the vegetation indices increased as a function of decreasing LAI. The leaf chlorophyll content did not affect the relationship between BNDVI, MSAVI, NDVI, and LAI, while the green atmospherically resistant index (GARI), GNDVI, and MSR 705 were the most strongly affected indices. While the relationship between SR and LAI was somewhat affected by both MTA and the leaf chlorophyll content, the simple ratio (SR) displayed only slight saturation with LAI, regardless of MTA and the chlorophyll content. The best index found in the study for LAI estimation was BNDVI, although it performed robustly only for LAI > 3 and showed considerable nonlinearity. Thus, none of the studied indices were well suited for across-species LAI estimation: information on the leaf angle would be required for remote LAI measurement, especially at low LAI values. Nevertheless, narrowband indices can be used to monitor the LAI of crops with a constant leaf angle distribution.

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Section: Discussionmentioning

confidence: 99%

Effects of Crop Leaf Angle on LAI-Sensitive Narrow-Band Vegetation Indices Derived from Imaging Spectroscopy

Zou

Haikarainen

et al. 2018

Applied Sciences

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“…Previous studies have typically used one agronomic variable (e.g., LAI) as a state variable for combining remote sensing and crop growth models and for yield estimation [8,13,14,16,17]. In such studies, the assimilated variable of the model had a reliable accuracy, but other agronomic state variables did not [18].…”

Section: Introductionmentioning

confidence: 99%

“…In a regional winter wheat and maize yield simulation, De Wit and Van Diepen [13] used the Ensemble Kalman filter (EnKF) to assimilate soil moisture into the World Food Study (WOFOST) model, where the results indicated assimilation of soil moisture improved the model's relationship with crop yield statistics for 66% and 56% of the regions for winter wheat and maize, respectively. Fang et al [7,14] integrated MODIS LAI, vegetation index, and the Crop System Model (CSM)-Crop Environment Resource Synthesis (CERES)-Maize model for corn yield estimation in Indiana, USA. Morel et al [15] coupled the sugarcane modelling software (MOSICAS) with a remotely sensed time series of the fraction of intercepted photosynthetically active radiation (fIPAR) to optimize the yield estimation by the partial forcing and complete forcing methods.…”

Section: Introductionmentioning

confidence: 99%

Assimilation of Two Variables Derived from Hyperspectral Data into the DSSAT-CERES Model for Grain Yield and Quality Estimation

Wang

et al. 2015

Remote Sensing

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Abstract:The combination of remote sensing and crop growth models has become an effective tool for yield estimation and a potential method for grain quality estimation. In this study, two assimilation variables (derived from a hyperspectral sensor), called leaf area index (LAI) and canopy nitrogen accumulation (CNA), were jointly used to calibrate the sensitive parameters and initial states of the DSSAT-CERES crop model, to improve This study provides a more robust method for estimating the grain yield and protein content of winter wheat based on the integration of the DSSAT-CERES crop model and remote sensing data.

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“…Through data assimilation, the MODIS LAI product and extracted vegetation indices of NDVI and EVI forecast crop yield, using only a partial year of data, with relative deviations from reference data less than 3.5% (Fang et al 2011). Passive MODIS, AVHRR, and Medium Resolution Imaging Spectrometer (MERIS), and active ASAR data, have been used to estimate wheat or maize yield with relative differences less than 11% (Moriondo et al 2007;Ren et al 2008;Yan et al 2009;Dente et al 2008).…”

Section: Provisioning Servicesmentioning

confidence: 99%

Remote sensing for biodiversity monitoring: a review of methods for biodiversity indicator extraction and assessment of progress towards international targets

2015

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Recognizing the imperative need for biodiversity protection, the Convention on Biological Diversity (CBD) has recently established new targets towards 2020, the so-called Aichi targets, and updated proposed sets of indicators to quantitatively monitor the progress towards these targets. Remote sensing has been increasingly contributing to timely, accurate, and cost-effective assessment of biodiversity-related characteristics and functions during the last years. However, most relevant studies constitute individual research efforts, rarely related with the extraction of widely adopted CBD biodiversity indicators. Furthermore, systematic operational use of remote sensing data by managing authorities has still been limited. In this study, the Aichi targets and the related CBD indicators whose monitoring can be facilitated by remote sensing are identified. For each headline indicator a number of recent remote sensing approaches able for the extraction of related properties are reviewed. Methods cover a wide range of fields, including: habitat extent and condition monitoring; species distribution; pressures from unsustainable management, pollution and climate change; ecosystem service monitoring; and conservation status assessment of protected areas. The advantages and limitations of different remote sensing data and algorithms are discussed. Sorting of the methods based on their reported accuracies is attempted, when possible. The extensive literature survey aims at reviewing highly performing methods that can be used for large-area, effective, and timely biodiversity assessment, to encourage the more systematic use of remote sensing solutions in monitoring progress towards the Aichi targets, and to decrease the gaps between the remote sensing and management communities.

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Integration of MODIS LAI and vegetation index products with the CSM–CERES–Maize model for corn yield estimation

Cited by 169 publications

References 55 publications

Effects of Crop Leaf Angle on LAI-Sensitive Narrow-Band Vegetation Indices Derived from Imaging Spectroscopy

Effects of Crop Leaf Angle on LAI-Sensitive Narrow-Band Vegetation Indices Derived from Imaging Spectroscopy

Assimilation of Two Variables Derived from Hyperspectral Data into the DSSAT-CERES Model for Grain Yield and Quality Estimation

Remote sensing for biodiversity monitoring: a review of methods for biodiversity indicator extraction and assessment of progress towards international targets

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