Estimating crop chlorophyll content with hyperspectral vegetation indices and the hybrid inversion method

Liang, Liang; Qin, Zhihao; Zhao, Shuang; Di, Liping; Zhang, Chao; Deng, Minghua; Lin, Hui; Zhang, Lianpeng; Wang, Lijuan; Liu, Zhixiao

doi:10.1080/01431161.2016.1186850

Cited by 100 publications

(69 citation statements)

References 80 publications

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“…Hence, correction for illumination conditions is ex-540 S. C. Truckenbrodt and C. C. Schmullius: A database for the set-up and validation of EO-LDAS pendable in this example. Figure 5f depicts the MTCI that is expected to increase linearly with increasing Chl a + b (Liang et al, 2016). However, the MTCI for winter rape (3.0 ± 0.3) is lower than for winter wheat (7.4 ± 0.3) and potato (3.5 ± 0.01), which is in contrast to the Chl a + b values.…”

Section: Influence Of Vegetation Parameters On Hyperspectral Reflectacontrasting

confidence: 42%

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Seasonal evolution of soil and plant parameters on the agricultural Gebesee test site: a database for the set-up and validation of EO-LDAS and satellite-aided retrieval models

Truckenbrodt

Schmullius

2018

Earth Syst. Sci. Data

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Abstract. Ground reference data are a prerequisite for the calibration, update, and validation of retrieval models facilitating the monitoring of land parameters based on Earth Observation data. Here, we describe the acquisition of a comprehensive ground reference database which was created to test and validate the recently developed Earth Observation Land Data Assimilation System (EO-LDAS) and products derived from remote sensing observations in the visible and infrared range. In situ data were collected for seven crop types (winter barley, winter wheat, spring wheat, durum, winter rape, potato, and sugar beet) cultivated on the agricultural Gebesee test site, central Germany, in 2013 and 2014. The database contains information on hyperspectral surface reflectance factors, the evolution of biophysical and biochemical plant parameters, phenology, surface conditions, atmospheric states, and a set of ground control points. Ground reference data were gathered at an approximately weekly resolution and on different spatial scales to investigate variations within and between acreages. In situ data collected less than 1 day apart from satellite acquisitions (RapidEye, SPOT 5, Landsat-7 and -8) with a cloud coverage ≤ 25 % are available for 10 and 15 days in 2013 and 2014, respectively. The measurements show that the investigated growing seasons were characterized by distinct meteorological conditions causing interannual variations in the parameter evolution. Here, the experimental design of the field campaigns, and methods employed in the determination of all parameters, are described in detail. Insights into the database are provided and potential fields of application are discussed. The data will contribute to a further development of crop monitoring methods based on remote sensing techniques. The database is freely available at PANGAEA (https://doi.org/10.1594/PANGAEA.874251).

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Section: Influence Of Vegetation Parameters On Hyperspectral Reflectacontrasting

confidence: 42%

“…5e). While the MTCI has proven to be valuable for the determination of the Chl a + b from hyperspectral canopy reflectance factors under diverse phenological conditions (Liang et al, 2016), the index is compromised by the occurrence of yellow petals (Fig. 5d, f).…”

Section: Influence Of Vegetation Parameters On Hyperspectral Reflectamentioning

confidence: 99%

Seasonal evolution of soil and plant parameters on the agricultural Gebesee test site: a database for the set-up and validation of EO-LDAS and satellite-aided retrieval models

Truckenbrodt

Schmullius

2018

Earth Syst. Sci. Data

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show abstract

“…Chl a+b (Liang et al, 2016). However, the MTCI for winter rape (3.0 ± 0.3) is lower than for winter wheat (7.4 ± 0.3) and potato (3.5 ± 0.01) which is in contrast to the Chl a+b values.…”

contrasting

confidence: 49%

“…5e). While the MTCI has been proven valuable for the determination of the Chl a+b from hyperspectral canopy reflectance under diverse phenological conditions (Liang et al, 2016), the index is compromised by the occurrence of yellow petals (Figs. 5d, f).…”

mentioning

confidence: 99%

Evolution of soil and plant parameters on the agricultural Gebesee test site: a database for the set-up and validation of EO-LDAS and other satellite-aided retrieval models

Truckenbrodt¹,

Schmullius²

2017

Preprint

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Abstract. Ground reference data are a prerequisite for the calibration, update and validation of retrieval models facilitating the monitoring of land parameters based on Earth Observation data. Here, we describe the acquisition of a comprehensive ground reference database which was elaborated to test and validate the recently developed Earth Observation Land Data Assimilation System (EO-LDAS). In situ data was collected for seven crop types (winter barley, winter wheat, spring wheat, durum, winter 10 rape, potato and sugar beet) cultivated on the agricultural Gebesee test site, central Germany, in 2013 and 2014. The database contains information on hyperspectral surface reflectance, the evolution of biophysical and biochemical plant parameters, phenology, surface conditions, atmospheric states, and a set of ground control points. Ground reference data was gathered with an approximately weekly resolution and on different spatial scales to investigate variations within and between acreages. In situ data collected less than 1 day apart from satellite acquisitions (RapidEye, SPOT5, Landsat-7 and -8) with a cloud coverage 15 ≤ 25 % is available for 10 and 16 days in 2013 and 2014, respectively. The measurements show that the investigated growing seasons were characterized by distinct meteorological conditions causing interannual variations in the parameter evolution. In the article, the experimental design of the field campaigns, and methods employed in the determination of all parameters are described in detail. Insights into the database are provided and potential fields of application are discussed. We hope these data will contribute to a further development of crop monitoring methods based on remote sensing techniques. The database is

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“…Indeed, VIs have been widely applied in hyperspectral data for several purposes [40,[112][113][114][115][116][117]. For example, Haboudane et al [112] carried out a study to evaluate VIs sensitivity to green LAI, and to modify some of them in order to enhance their responsivity to LAI variations.…”

Section: Vegetation Indicesmentioning

confidence: 99%

Hyperspectral Imaging: A Review on UAV-Based Sensors, Data Processing and Applications for Agriculture and Forestry

et al. 2017

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Traditional imagery-provided, for example, by RGB and/or NIR sensors-has proven to be useful in many agroforestry applications. However, it lacks the spectral range and precision to profile materials and organisms that only hyperspectral sensors can provide. This kind of high-resolution spectroscopy was firstly used in satellites and later in manned aircraft, which are significantly expensive platforms and extremely restrictive due to availability limitations and/or complex logistics. More recently, UAS have emerged as a very popular and cost-effective remote sensing technology, composed of aerial platforms capable of carrying small-sized and lightweight sensors. Meanwhile, hyperspectral technology developments have been consistently resulting in smaller and lighter sensors that can currently be integrated in UAS for either scientific or commercial purposes. The hyperspectral sensors' ability for measuring hundreds of bands raises complexity when considering the sheer quantity of acquired data, whose usefulness depends on both calibration and corrective tasks occurring in pre-and post-flight stages. Further steps regarding hyperspectral data processing must be performed towards the retrieval of relevant information, which provides the true benefits for assertive interventions in agricultural crops and forested areas. Considering the aforementioned topics and the goal of providing a global view focused on hyperspectral-based remote sensing supported by UAV platforms, a survey including hyperspectral sensors, inherent data processing and applications focusing both on agriculture and forestry-wherein the combination of UAV and hyperspectral sensors plays a center role-is presented in this paper. Firstly, the advantages of hyperspectral data over RGB imagery and multispectral data are highlighted. Then, hyperspectral acquisition devices are addressed, including sensor types, acquisition modes and UAV-compatible sensors that can be used for both research and commercial purposes. Pre-flight operations and post-flight pre-processing are pointed out as necessary to ensure the usefulness of hyperspectral data for further processing towards the retrieval of conclusive information. With the goal of simplifying hyperspectral data processing-by isolating the common user from the processes' mathematical complexity-several available toolboxes that allow a direct access to level-one hyperspectral data are presented. Moreover, research works focusing the symbiosis between UAV-hyperspectral for agriculture and forestry applications are reviewed, just before the paper's conclusions.

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Estimating crop chlorophyll content with hyperspectral vegetation indices and the hybrid inversion method

Cited by 100 publications

References 80 publications

Seasonal evolution of soil and plant parameters on the agricultural Gebesee test site: a database for the set-up and validation of EO-LDAS and satellite-aided retrieval models

Seasonal evolution of soil and plant parameters on the agricultural Gebesee test site: a database for the set-up and validation of EO-LDAS and satellite-aided retrieval models

Evolution of soil and plant parameters on the agricultural Gebesee test site: a database for the set-up and validation of EO-LDAS and other satellite-aided retrieval models

Hyperspectral Imaging: A Review on UAV-Based Sensors, Data Processing and Applications for Agriculture and Forestry

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