Reducing the influence of solar illumination angle when using active optical sensor derived NDVIAOS to infer fAPAR for spring wheat (Triticum aestivum L.)

Rahman, Muhammad Moshiur; Lamb, David; Samborski, Stanisław

doi:10.1016/j.compag.2018.11.007

Cited by 8 publications

(5 citation statements)

References 60 publications

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“…During the senescence period, both green and senescent elements contribute to fAPAR at the canopy level (Asner et al, 1998;Di Bella et al, 2004;Huemmrich et al, 2005;Rahman et al, 2019). Since only the green components are used for photosynthesis and transpiration, the green fAPAR should be the quantity to be considered.…”

Section: Estimations Of Green Fapar and Fipar During The Senescence P...mentioning

confidence: 99%

“…Since only the green photosynthetically active elements contribute directly to key processes such as photosynthesis and transpiration, green vegetation elements should be isolated to estimate fAPAR (Huemmrich et al, 2005;Pinter, 1993;Weiss et al, 2007;Xiao, 2004;Zhang et al, 2005). The presence of senescent leaves during late crop growth stages have a significant impact on fAPAR, and the relationship between fAPAR and vegetation indices (Di Bella et al, 2004;Rahman et al, 2019;Viña and Gitelson, 2005). The ground measured canopy fAPAR can be partitioned into fAPAR of green components and non-green components.…”

Section: Introductionmentioning

confidence: 99%

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Critical analysis of methods to estimate the fraction of absorbed or intercepted photosynthetically active radiation from ground measurements: Application to rice crops

Fang

Wei

et al. 2021

Agricultural and Forest Meteorology

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Section: Estimations Of Green Fapar and Fipar During The Senescence P...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Critical analysis of methods to estimate the fraction of absorbed or intercepted photosynthetically active radiation from ground measurements: Application to rice crops

Fang

Wei

et al. 2021

Agricultural and Forest Meteorology

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“…The research on remote sensing of the vegetation health status based on the vegetation index has become a main research approach for scholars. However, for the study of the health status of vegetation at night, the vegetation index is greatly affected by the illumination [66][67][68]. As shown in Figure 6, the NDVI value decreases with the decrease of the environmental illumination.…”

Section: Discussionmentioning

confidence: 99%

Research on Polarized Multi-Spectral System and Fusion Algorithm for Remote Sensing of Vegetation Status at Night

Jiao

Wang

2021

Remote Sensing

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The monitoring of vegetation via remote sensing has been widely applied in various fields, such as crop diseases and pests, forest coverage and vegetation growth status, but such monitoring activities were mainly carried out in the daytime, resulting in limitations in sensing the status of vegetation at night. In this article, with the aim of monitoring the health status of outdoor plants at night by remote sensing, a polarized multispectral low-illumination-level imaging system (PMSIS) was established, and a fusion algorithm was proposed to detect vegetation by sensing the spectrum and polarization characteristics of the diffuse and specular reflection of vegetation. The normalized vegetation index (NDVI), degree of linear polarization (DoLP) and angle of polarization (AOP) are all calculated in the fusion algorithm to better detect the health status of plants in the night environment. Based on NDVI, DoLP and AOP fusion images (NDAI), a new index of night plant state detection (NPSDI) was proposed. A correlation analysis was made for the chlorophyll content (SPAD), nitrogen content (NC), NDVI and NPSDI to understand their capabilities to detect plants under stress. The scatter plot of NPSDI shows a good distinction between vegetation with different health levels, which can be seen from the high specificity and sensitivity values. It can be seen that NPSDI has a good correlation with NDVI (coefficient of determination R2 = 0.968), PSAD (R2 = 0.882) and NC (R2 = 0.916), which highlights the potential of NPSDI in the identification of plant health status. The results clearly show that the proposed fusion algorithm can enhance the contrast effect and the generated fusion image will carry richer vegetation information, thereby monitoring the health status of plants at night more effectively. This algorithm has a great potential in using remote sensing platform to monitor the health of vegetation and crops.

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“…In previous studies, Ishihara et al [28] suggests that the response of vegetation indices to the SZA was evident under a clear sky. Rahman [29] reported NDVIs from ground-based observations that decreased with decreasing SZA at a pasture site, but did not provide explanations to this finding. Their conclusions were directly based on the average assessment of ground-NDVIs, and hence, not remote sensing data.…”

Section: Influence Of the Time Of Day (Tod) And Solar Zenith Angle (S...mentioning

confidence: 93%

“…Flight parameters such as the flight altitude (FA) need to be considered because they determine the quality of the remote sensing data. For instance, the ground sampling distance (GSD = size of pixel × flight altitude/focal length) [27] and the spatial coverage of the survey [28,29] determine to which extent patterns and objects can be detected. On the other hand, collecting high-quality remote-sensing data can become challenging if the bidirectional reflectance distribution function (BRDF), resulting from a large solar zenith angle (SZA), affects the results [30].…”

Section: Introductionmentioning

confidence: 99%

Assessing the Operation Parameters of a Low-altitude UAV for the Collection of NDVI Values Over a Paddy Rice Field

Jiang

Wang

et al. 2020

Remote Sensing

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Unmanned aerial vehicle (UAV) remote sensing platforms allow for normalized difference vegetation index (NDVI) values to be mapped with a relatively high resolution, therefore enabling an unforeseeable ability to evaluate the influence of the operation parameters on the quality of the thus acquired data. In order to better understand the effects of these parameters, we made a comprehensive evaluation on the effects of the solar zenith angle (SZA), the time of day (TOD), the flight altitude (FA) and the growth level of paddy rice at a pixel-scale on UAV-acquired NDVI values. Our results show that: (1) there was an inverse relationship between the FA (≤100 m) and the mean NDVI values, (2) TOD and SZA had a greater impact on UAV–NDVIs than the FA and the growth level; (3) Better growth levels of rice—measured using the NDVI—could reduce the effects of the FA, TOD and SZA. We expect that our results could be used to better plan flight campaigns that aim to collect NDVI values over paddy rice fields.

show abstract

Reducing the influence of solar illumination angle when using active optical sensor derived NDVIAOS to infer fAPAR for spring wheat (Triticum aestivum L.)

Cited by 8 publications

References 60 publications

Critical analysis of methods to estimate the fraction of absorbed or intercepted photosynthetically active radiation from ground measurements: Application to rice crops

Critical analysis of methods to estimate the fraction of absorbed or intercepted photosynthetically active radiation from ground measurements: Application to rice crops

Research on Polarized Multi-Spectral System and Fusion Algorithm for Remote Sensing of Vegetation Status at Night

Assessing the Operation Parameters of a Low-altitude UAV for the Collection of NDVI Values Over a Paddy Rice Field

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