Development of a New BRDF-Resistant Vegetation Index for Improving the Estimation of Leaf Area Index

Zhang, Su; Liu, Liangyun; Liu, Xinjie; Liu, Zefei

doi:10.3390/rs8110947

Cited by 11 publications

(2 citation statements)

References 39 publications

(12 reference statements)

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“…Another important issue is the bidirectional reflectance effect of vegetation canopy, which has a subtle impact on LAI estimation from satellite image 35 , 36 and ground-based measurement as well. Part of the bidirectional reflectance effect comes from the solar illumination angle.…”

Section: Discussionmentioning

confidence: 99%

Validation of leaf area index measurement system based on wireless sensor network

Yang

Liu

et al. 2022

Sci Rep

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Accurate measurement of leaf area index (LAI) is important for agricultural analysis such as the estimation of crop yield, which makes its measurement work important. There are mainly two ways to obtain LAI: ground station measurement and remote sensing satellite monitoring. Recently, reliable progress has been made in long-term automatic LAI observation using wireless sensor network (WSN) technology under certain conditions. We developed and designed an LAI measurement system (LAIS) based on a wireless sensor network to select and improve the appropriate algorithm according to the image collected by the sensor, to get a more realistic leaf area index. The corn LAI was continuously observed from May 30 to July 16, 2015. Research on hardware has been published, this paper focuses on improved system algorithm and data verification. By improving the finite length average algorithm, the data validation results are as follows: (1) The slope of the fitting line between LAIS measurement data and the real value is 0.944, and the root means square error (RMSE) is 0.264 (absolute error ~ 0–0.6), which has high consistency with the real value. (2) The measurement error of LAIS is less than LAI2000, although the result of our measurement method will be higher than the actual value, it is due to the influence of weeds on the ground. (3) LAIS data can be used to support the retrieval of remote sensing products. We find a suitable application situation of our LAIS system data, and get our application value as ground monitoring data by the verification with remote sensing product data, which supports its application and promotion in similar research in the future.

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Section: Discussionmentioning

confidence: 99%

Validation of leaf area index measurement system based on wireless sensor network

Yang

Liu

et al. 2022

Sci Rep

View full text Add to dashboard Cite

show abstract

“…Another important issue is the bidirectional reflectance effect of vegetation canopy, which has a subtle impact on LAI estimation from satellite image [32,33]and ground-based measurement as well. Part of the bidirectional reflectance effect comes from the solar illumination angle.…”

Section: Method2mentioning

confidence: 99%

Validation of Leaf Area Index Measurement System Based on Wireless Sensor Network

Yang

Liu

et al. 2021

Preprint

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Accurate measurement of leaf area index (LAI) is important for agricultural analysis such as the estimation of crop yield, which makes its measurement work important. There are mainly two ways to obtain LAI: ground station measurement and remote sensing satellite monitoring. Recently, reliable progress has been made in long-term automatic LAI observation using wireless sensor network (WSN) technology under certain conditions. We developed and designed an LAI measurement system (LAIS) based on a wireless sensor network to select and improve the appropriate algorithm according to the image collected by the sensor, to get a more realistic leaf area index. The corn LAI was continuously observed from May 30 to July 16, 2015. Research on hardware has been published, this paper focuses on improved system algorithm and data verification. By improving the finite length average algorithm, the data validation results are as follows: 1. The slope of the fitting line between LAIS measurement data and the real value is 0.944, and the root means square error (RMSE) is 0.264 (absolute error ~ 0-0.6), which has high consistency with the real value. 2. The measurement error of LAIS is less than LAI2000, although the result of our measurement method will be higher than the actual value, it is due to the influence of weeds on the ground. 3. LAIS data can be used to support the retrieval of remote sensing products. We find a suitable application situation of our LAIS system data, and get our application value as ground monitoring data by the verification with remote sensing product data, which supports its application and promotion in similar research in the future.

show abstract

A new spectral index for estimation of wheat canopy chlorophyll density: considering background interference and view zenith angle effect

et al. 2023

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Development of a New BRDF-Resistant Vegetation Index for Improving the Estimation of Leaf Area Index

Cited by 11 publications

References 39 publications

Validation of leaf area index measurement system based on wireless sensor network

Validation of leaf area index measurement system based on wireless sensor network

Validation of Leaf Area Index Measurement System Based on Wireless Sensor Network

A new spectral index for estimation of wheat canopy chlorophyll density: considering background interference and view zenith angle effect

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