Evaluating Hyperspectral Vegetation Indices for Leaf Area Index Estimation of Oryza sativa L. at Diverse Phenological Stages

Din, Mairaj; Zheng, Wei; Rashid, Muhammad; Wang, Shanqin; Shi, Zhihua

doi:10.3389/fpls.2017.00820

Cited by 67 publications

(50 citation statements)

References 74 publications

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“…In addition, the visible band that is represented by the red band (670 nm) was greatly affected by rice panicles in the post-heading stage (Figure 3b). Therefore, during the reproductive growth stage, the performance and accuracy of LAI estimation for some VIs (GNDVI, SR, and MSR) were not as adequate as those in the vegetative growth stage, which is supported by previous studies [4,58].…”

Section: Influence Of Panicles On Lai Estimation Accuracysupporting

confidence: 61%

Estimating Leaf Area Index with a New Vegetation Index Considering the Influence of Rice Panicles

Zhang

et al. 2019

Remote Sensing

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The emergence of rice panicle substantially changes the spectral reflectance of rice canopy and, as a result, decreases the accuracy of leaf area index (LAI) that was derived from vegetation indices (VIs). From a four-year field experiment with using rice varieties, nitrogen (N) rates, and planting densities, the spectral reflectance characteristics of panicles and the changes in canopy reflectance after panicle removal were investigated. A rice “panicle line”—graphical relationship between red-edge and near-infrared bands was constructed by using the near-infrared and red-edge spectral reflectance of rice panicles. Subsequently, a panicle-adjusted renormalized difference vegetation index (PRDVI) that was based on the “panicle line” and the renormalized difference vegetation index (RDVI) was developed to reduce the effects of rice panicles and background. The results showed that the effects of rice panicles on canopy reflectance were concentrated in the visible region and the near-infrared region. The red band (670 nm) was the most affected by panicles, while the red-edge bands (720–740 nm) were less affected. In addition, a combination of near-infrared and red-edge bands was for the one that best predicted LAI, and the difference vegetation index (DI) (976, 733) performed the best, although it had relatively low estimation accuracy (R2 = 0.60, RMSE = 1.41 m2/m2). From these findings, correcting the near-infrared band in the RDVI by the panicle adjustment factor (θ) developed the PRDVI, which was obtained while using the “panicle line”, and the less-affected red-edge band replaced the red band. Verification data from an unmanned aerial vehicle (UAV) showed that the PRDVI could minimize the panicle and background influence and was more sensitive to LAI (R2 = 0.77; RMSE = 1.01 m2/m2) than other VIs during the post-heading stage. Moreover, of all the assessed VIs, the PRDVI yielded the highest R2 (0.71) over the entire growth period, with an RMSE of 1.31 (m2/m2). These results suggest that the PRDVI is an efficient and suitable LAI estimation index.

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Section: Influence Of Panicles On Lai Estimation Accuracysupporting

confidence: 61%

Estimating Leaf Area Index with a New Vegetation Index Considering the Influence of Rice Panicles

Zhang

et al. 2019

Remote Sensing

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“…In addition, modern methods often use remote sensing indicators to model biometric values. For example, regression models have been used to perform quantitative analyses between spectral VIs and LAI [24] or CCC (Canopy chlorophyll content) [25] measured under different phenological stages. In such analysis models, such as PROSPECT and SAIL radiative transfer models (PROSAIL) [26], multiple linear regression (SMLR; [27]), partial least square regression (PLSR; [28]) or multivariate adaptive regression splines (MARS; [29]) are often used.…”

Section: Introductionmentioning

confidence: 99%

In Situ Hyperspectral Remote Sensing for Monitoring of Alpine Trampled and Recultivated Species

Kycko

Zagajewski

Lavender³

et al. 2019

Remote Sensing

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Vegetation, through its condition, reflects the properties of the environment. Heterogeneous alpine ecosystems play a critical role in global monitoring systems, but due to low accessibility, cloudy conditions, and short vegetation periods, standard monitoring methods cannot be applied comprehensively. Hyperspectral tools offer a variety of methods based on narrow-band data, but before extrapolation to an airborne or satellite scale, they must be verified using plant biometrical variables. This study aims to assess the condition of alpine sward dominant species (Agrostis rupestris, Festuca picta, and Luzula alpino-pilosa) of the UNESCO Man&Biosphere Tatra National Park (TPN) where the high mountain grasslands are strongly influenced by tourists. Data were analyzed for trampled, reference, and recultivated polygons. The field-obtained hyperspectral properties were verified using ground measured photosynthetically active radiation, chlorophyll content, fluorescence, and evapotranspiration. Statistically significant changes in terms of cellular structures, chlorophyll, and water content in the canopy were detected. Lower values for the remote sensing indices were observed for trampled plants (about 10–15%). Species in recultivated areas were characterized by a similar, or sometimes improved, spectral properties than the reference polygons; confirmed by fluorescence measurements (Fv/Fm). Overall, the fluorescence analysis and remote sensing tools confirmed the suitability of such methods for monitoring species in remote mountain areas, and the general condition of these grasslands was determined as good.

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“…Regarding canopy chlorophyll content estimation, SR and SR 2 are at the top of their ranking. In a recent work on rice [117], MSAVI is among the VIs that presented the strong and significant relationships with the LAI estimation at different phenological stages. For more information in hyperspectral VIs, the fourteenth section of the fifth part of [116]-a book devoted to hyperspectral remote sensing of vegetation-is available for consultation.…”

Section: Vegetation Indicesmentioning

confidence: 99%

“…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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Evaluating Hyperspectral Vegetation Indices for Leaf Area Index Estimation of Oryza sativa L. at Diverse Phenological Stages

Cited by 67 publications

References 74 publications

Estimating Leaf Area Index with a New Vegetation Index Considering the Influence of Rice Panicles

Estimating Leaf Area Index with a New Vegetation Index Considering the Influence of Rice Panicles

In Situ Hyperspectral Remote Sensing for Monitoring of Alpine Trampled and Recultivated Species

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

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