Continuous observation of vegetation canopy dynamics using an integrated low-cost, near-surface remote sensing system

Kim, Jongmin; Ryu, Youngryel; Jiang, Chongya; Hwang, Y. S.

doi:10.1016/j.agrformet.2018.09.014

Cited by 60 publications

(27 citation statements)

References 71 publications

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“…As a result, structure and function co-vary with environmental variables, and the reflectance itself is able to capture long-term variability of GPP. In contrast, dayto-day and diurnal variations are strongly affected by high-frequency changes of PAR due to varying solar angle and sky conditions (Peng and Gitelson 2011), which does not cause much changes in bi-directional reflectance (Kim et al 2019). Therefore, NIR v,Rad containing the information of PAR in addition to biophysical and biochemical information contained in reflectance-based vegetation indices better captures short-term variability of GPP.…”

Section: Discussionmentioning

confidence: 99%

Radiance-based NIR_v as a proxy for GPP of corn and soybean

Guan

Jiang

et al. 2020

Environ. Res. Lett.

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Substantial uncertainty exists in daily and sub-daily gross primary production (GPP) estimation, which dampens accurate monitoring of the global carbon cycle. Here we find that near-infrared radiance of vegetation (NIR v,Rad ), defined as the product of observed NIR radiance and normalized difference vegetation index, can accurately estimate corn and soybean GPP at daily and half-hourly time scales, benchmarked with multi-year tower-based GPP at three sites with different environmental and irrigation conditions. Overall, NIR v,Rad explains 84% and 78% variations of half-hourly GPP for corn and soybean, respectively, outperforming NIR reflectance of vegetation (NIR v,Ref ), enhanced vegetation index (EVI), and far-red solar-induced fluorescence (SIF 760 ). The strong linear relationship between NIR v,Rad and absorbed photosynthetically active radiation by green leaves (APAR green ), and that between APAR green and GPP, explain the good NIR v,Rad -GPP relationship. The NIR v,Rad -GPP relationship is robust and consistent across sites. The scalability and simplicity of NIR v,Rad indicate a great potential to estimate daily or sub-daily GPP from high-resolution and/or long-term satellite remote sensing data.

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Section: Discussionmentioning

confidence: 99%

Radiance-based NIR_v as a proxy for GPP of corn and soybean

Guan

Jiang

et al. 2020

Environ. Res. Lett.

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“…Regular calibration was performed for both SIF and canopy reflectance systems using a calibration light source (HL-2000-Cal, Ocean Optics, Dunedin, FL, USA). fPAR was continuously monitored at three sampling locations using an automated, low-cost observation system based on LED sensors (Kim et al, 2019) but had to be gap-filled with PROSAIL (Jacquemoud et al, 2009; Jacquemoud and Baret, 1990) simulation using observed input data. For this, effects of diffuse radiation where taken into account by weighting the PROSAIL outputs corresponding to cloudy and sunny sky conditions with the measured fraction of diffuse and direct PAR.…”

Section: In-situ Data Setsmentioning

confidence: 99%

Canopy structure explains the relationship between photosynthesis and sun-induced chlorophyll fluorescence in crops

Dechant

Ryu

Badgley

et al. 2020

Remote Sensing of Environment

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Remote sensing of far-red sun-induced chlorophyll fluorescence (SIF) has emerged as an important tool for studying gross primary productivity (GPP) at the global scale. However, the relationship between SIF and GPP at the canopy scale lacks a clear mechanistic explanation. This is largely due to the poorly characterized role of the relative contributions from canopy structure and leaf physiology to the variability of the top-of-canopy, observed SIF signal. In particular, the effect of the canopy structure beyond light absorption is that only a fraction (f esc) of the SIF emitted from all leaves in the canopy can escape from the canopy due to the strong scattering of near-infrared radiation. We combined rice, wheat and corn canopy-level in-situ datasets to study how the physiological and structural components of SIF individually relate to measures of photosynthesis. At seasonal time scales, we found a considerably strong positive correlation (R 2 =0.4-0.6) of f esc to the seasonal dynamics of the photosynthetic light use efficiency (LUE P), while the estimated physiological SIF yield was almost entirely uncorrelated to LUE P both at seasonal and diurnal time scales, with the partial exception of wheat. Consistent with these findings, the canopy structure and radiation component of SIF, defined as the product of APAR and f esc , explained the relationship of observed SIF to GPP and even outperformed GPP estimation based on observed SIF at two of the three sites investigated. These results held for both half-hourly and daily mean values. In contrast, the total emitted SIF, obtained by normalizing observed SIF for f esc , improved only the relationship to APAR but considerably decreased the correlation to GPP for all three crops. Our findings demonstrate the dominant role of canopy structure in the SIF-GPP relationship and establish a strong, mechanistic link between the near-infrared reflectance of vegetation (NIR V) and the relevant canopy structure information contained in the SIF signal. These insights are expected to be useful in improving remote sensing based GPP estimates.

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“…NDVI values measured from satellite and UAV data were compared to those obtained from ground-installed sensors and field spectrometers (or handheld spectrometers) [16,17]. Ground-based data are normally considered as "true reference sources" owing to the fact that the distance between the sensor and the measurement target is small.…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, the spatial characteristics of the data obtained from ground-based, airborne, and spaceborne sensors should be taken into account when making comparisons across data. In previous studies, the spatial characteristics of vegetation indices were only compared under specific spatial scales: comparisons between UAV and satellite data [15,20] or between ground-based data and satellite data [16] were carried out.…”

Section: Introductionmentioning

confidence: 99%

Inter-Comparison of Normalized Difference Vegetation Index Measured from Different Footprint Sizes in Cropland

Ryu

Cho

2020

Remote Sensing

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Remote sensing techniques using visible and near-infrared wavelengths are useful for monitoring terrestrial vegetation. The normalized difference vegetation index (NDVI) is a widely used proxy of vegetation conditions, and it has been measured at various footprint sizes using satellite, unmanned aerial vehicle (UAV), and ground-installed sensors. The goal of this study was to analyze the spatial characteristics of NDVI data by comparing the values obtained at different footprint sizes. In particular, the NDVI was evaluated in garlic and onion fields that featured ridges and furrows. The evaluation was performed using data from a leaf spectrometer, field spectrometers, ground-installed spectral reflectance sensors, a multispectral camera onboard a UAV, and Sentinel-2 satellites. The correlation coefficients between NDVIs evaluated from the various sensors (excluding the satellite-mounted sensors) ranged from 0.628 to 0.944. The UAV-based NDVI (NDVIUAV) exhibited the lowest root mean square error (RMSE = 0.088) when compared with field spectrometer data. On the other hand, the satellite-based NDVI data (NDVISentinel-2) were poorly correlated with those obtained from the other sensors as a result of the footprint mismatch. However, by upscaling the NDVIUAV data to the pixel size of Sentinel-2, the comparison was improved, and the following statistics were obtained: correlation coefficient: 0.504–0.785; absolute bias: 0.048–0.078; RMSE: 0.063–0.094. According to the aforementioned results, ground-based NDVI data can be used to validate NDVIUAV data without further processing and NDVIUAV data can be used to validate NDVISentinel-2 data after upscaling to the Sentinel-2 pixel size. Overall, the results presented in this study may be helpful to understand and integrate NDVI data at different spatial scales.

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Continuous observation of vegetation canopy dynamics using an integrated low-cost, near-surface remote sensing system

Cited by 60 publications

References 71 publications

Radiance-based NIR_v as a proxy for GPP of corn and soybean

Radiance-based NIR_v as a proxy for GPP of corn and soybean

Canopy structure explains the relationship between photosynthesis and sun-induced chlorophyll fluorescence in crops

Inter-Comparison of Normalized Difference Vegetation Index Measured from Different Footprint Sizes in Cropland

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Continuous observation of vegetation canopy dynamics using an integrated low-cost, near-surface remote sensing system

Cited by 60 publications

References 71 publications

Radiance-based NIRv as a proxy for GPP of corn and soybean

Radiance-based NIRv as a proxy for GPP of corn and soybean

Canopy structure explains the relationship between photosynthesis and sun-induced chlorophyll fluorescence in crops

Inter-Comparison of Normalized Difference Vegetation Index Measured from Different Footprint Sizes in Cropland

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Radiance-based NIR_v as a proxy for GPP of corn and soybean

Radiance-based NIR_v as a proxy for GPP of corn and soybean