Tracking the seasonal and inter-annual variations of global gross primary production during last four decades using satellite near-infrared reflectance data

Wang, Songhan; Zhang, Yongguang; Ju, Weimin; Qiu, Bo; Zhang, Zhaoying

doi:10.1016/j.scitotenv.2020.142569

Cited by 179 publications

(111 citation statements)

References 39 publications

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“…Multivariate adap- The second step entailed building the model. In recent years, SIF and NIRv provide alternative approaches to estimate global GPP [45,70,73]. Meanwhile, precipitation and temperature are important for plant photosynthesis.…”

Section: Methodsologymentioning

confidence: 99%

“…NIRv can explain most of the variation in GPP on monthly and annual scales and has been successfully used to estimate global GPP [70,72]. Badgley [72] and Wang [73] also suggested that using satellite NIRv to estimate global GPP does not need additional information on environmental conditions. It can also be used to convert observed SIF recorded at a given angle to hemispherical SIF emissions, which are better correlated to GPP [71,74].…”

Section: Near-infrared Radiance Of Vegetation (Nirv)mentioning

confidence: 99%

“…Other variables used in this study, such as latitude, longitude, DEM, month, etc., reflect precipitation and temperature information and are important for plant photosynthesis. SIF and NIRv provide alternative approaches to estimate global GPP [45,70,73], but as shown in Figure 7, SIF was far more important than NIRv. The advantages and differences of SIF and NIR V estimation of GPP need to be further studied.…”

Section: Relative Importance Of Variablesmentioning

confidence: 99%

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Estimating Global Gross Primary Production from Sun-Induced Chlorophyll Fluorescence Data and Auxiliary Information Using Machine Learning Methods

2021

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The gross primary production (GPP) is important for regulating the global carbon cycle and climate change. Recent studies have shown that sun-induced chlorophyll fluorescence (SIF) is highly advantageous regarding GPP monitoring. However, using SIF to estimate GPP on a global scale is limited by the lack of a stable SIF-GPP relationship. Here, we estimated global monthly GPP at 0.05° spatial resolution for the period 2001–2017, using the global OCO-2-based SIF product (GOSIF) and other auxiliary data. Large amounts of flux tower data are not available to the public and the available data is not evenly distributed globally and has a smaller measured footprint than the GOSIF data. This makes it difficult to use the flux tower GPP directly as an input to the model. Our strategy is to scale in situ measurements using two moderate-resolution satellite GPP products (MODIS and GLASS). Specifically, these two satellite GPP products were calibrated and eventually integrated by in situ measurements (FLUXNET2015 dataset, 83 sites), which was then used to train a machine learning model (GBRT) that performed the best among five evaluated models. The GPP estimates from GOSIF were highly accurate coefficient of determination (R2) = 0.58, root mean square error (RMSE) = 2.74 g C·m−2, bias = –0.34 g C·m−2) as validated by in situ measurements, and exhibited reasonable spatial and seasonal variations on a global scale. Our method requires fewer input variables and has higher computational efficiency than other satellite GPP estimation methods. Satellite-based SIF data provide a unique opportunity for more accurate, near real-time GPP mapping in the future.

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Section: Methodsologymentioning

confidence: 99%

Section: Near-infrared Radiance Of Vegetation (Nirv)mentioning

confidence: 99%

Section: Relative Importance Of Variablesmentioning

confidence: 99%

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Estimating Global Gross Primary Production from Sun-Induced Chlorophyll Fluorescence Data and Auxiliary Information Using Machine Learning Methods

2021

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“…FCVI was demonstrated to capture the canopy structure and radiation components of the SIF signal, which tracked SIF and GPP, yet showed differences from NIRv due to exposed soil within the vegetated study areas. In previous studies, FCVI and NIRv were similar for dense green canopies where soils have less of an impact, but this has not yet been tested in the tropics (Wang et al, 2020;Badgley et al, 2019;Dechant et al, 2020). NIRvrad was proposed as a proxy for GPP on half-hourly and daily timescales, in contrast to NIRv and FCVI which track changes on longer timescales (Wu et al, 2020;Dechant et al, 2020;Baldocchi et al, 2020;Zeng et al, 2019).…”

Section: Introductionmentioning

confidence: 96%

“…the number of fluorescence photons generated as a byproduct of photosynthesis) and structural component (e,g., arrangement of leaves, clumping, and canopy structure, which determine the light capture and SIF photon scattering regime of plants). The aforementioned complexity of measuring SIF and subsequent interpretation limit the number of studies using SIF in comparison to RIs (Hao et al, 2021;Zhang et al, 2020;Liangyun Liu, 2016;Van Wittenberghe et al, 2015;Baldocchi et al, 2020;Wang et al, 2020;Wu et al, 2020;Badgley et al, 2019). An additional barrier in tropical regions is that SIF data in tropical forests is even more scarce than in other biomes and, likely due to data scarcity and combined complexities of the SIF measurements and heterogeneity of tropical forest canopies, relationships to plant function in tropical forests are even more poorly understood than other biomes (Merrick et al, 2019;Wang et al, 2019;Yang et al, 2018a;Liu et al, 2017;Castro et al, 2020;Köhler et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

Unveiling spatial and temporal heterogeneity of a tropical forest canopy using high-resolution NIRv, FCVI, and NIRvrad from UAS observations

Merrick¹,

Pau²,

Detto³

et al. 2021

Preprint

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Abstract. Presented here for the first time are emerging vegetation indicators: near-infrared reflectance (NIRv) of vegetation, the fluorescence correction vegetation index (FCVI), and radiance (NIRvrad) of vegetation, for a tropical forest canopy calculated using UAS-based hyperspectral data. Fine-scale tropical forest heterogeneity represented by NIRv, FCVI, and NIRvrad, is investigated using unmanned aerial vehicle data and eddy covariance-based gross primary productivity estimates. By exploiting near-infrared signals, emerging vegetation indicators captured the greatest spatiotemporal variability, followed by the enhanced vegetation index (EVI), then the normalized difference vegetation index (NDVI), which saturates. Wavelet analyses showed the dominant spatial variability of all indicators is driven by tree clusters and larger-than-tree-crown size gaps (not individual tree crowns or leaf clumps), but emerging indices and EVI captured structural information at smaller spatial scales (~50 m) than NDVI (~90 m) and lidar (~70 m). As predicted in previous studies, we confirm that NIRv and FCVI are virtually identical for a dense green canopy despite the differences in how these indices were derived. Furthermore, we show that NIRvrad, which does not require separate irradiance measurements, correlated most strongly with gross primary productivity and photosynthetically active radiation. These emerging indicators, which are related to canopy structure and the radiation regime of vegetation canopies are promising tools to improve understanding of tropical forest canopy structure and function.

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Yield estimation of summer maize based on multi‐source remote‐sensing data

Wang

Liu

et al. 2022

Agronomy Journal

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Accurately estimating regional‐scale crop yields is substantial in determining current agricultural production performance and effective agricultural land management. The Yuncheng Basin is an important grain‐producing area in the Shanxi Province. This paper used Sentinel 2A with a spatial resolution of 10 m and MODIS with a temporal resolution of 1 d in 2020. The spatial and temporal nonlocal filter‐based fusion model (STNLFFM) was used to obtain fused data with a spatial resolution of 10 m and a temporal resolution of 1 d, combined with the Carnegie–Ames–Stanford Approach (CASA) and light‐use efficiency model to achieve summer maize (Zea mays L.) yield estimation. The results showed that the fused normalized difference vegetation index (NDVI) could inherit the spatial Sentinel‐2A NDVI details and express the spatial differences between smaller features more effectively. The STNLFFM NDVI curve was consistent with the actual summer maize growth condition, which accurately reflects the NDVI trend and local abrupt change information during the summer maize growth period. Moreover, the fused NDVI was influenced by topographic differences and artificial irrigation factors, whereas the summer maize yield in mountainous and plateau areas of the Yuncheng Basin was <5,000 kg ha−1 and those in the alluvial plain of the Sushui River reached 8,000 kg ha−1. The accuracy of the yield estimation model constructed based on STNLFFM NDVI (mean absolute percentage error [MAPE] = 5.47%, −13.74% ≤ relative error [RE] ≤0.12%) was significantly higher than that of the model based on MODIS NDVI (MAPE = 15.65%, −19.67% ≤ RE ≤ 20.88%), indicating that the use of spatio‐temporal fusion technology can effectively improve the summer maize yield estimation accuracy.

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Tracking the seasonal and inter-annual variations of global gross primary production during last four decades using satellite near-infrared reflectance data

Cited by 179 publications

References 39 publications

Estimating Global Gross Primary Production from Sun-Induced Chlorophyll Fluorescence Data and Auxiliary Information Using Machine Learning Methods

Estimating Global Gross Primary Production from Sun-Induced Chlorophyll Fluorescence Data and Auxiliary Information Using Machine Learning Methods

Unveiling spatial and temporal heterogeneity of a tropical forest canopy using high-resolution NIRv, FCVI, and NIRvrad from UAS observations

Yield estimation of summer maize based on multi‐source remote‐sensing data

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