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

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.

Download Full-text

Estimation of gross primary production in wheat from in situ measurements

International Journal of Applied Earth Observation and Geoinformation ◽

10.1016/j.jag.2010.02.006 ◽

2010 ◽

Vol 12 (3) ◽

pp. 183-189 ◽

Cited By ~ 16

Author(s):

Chaoyang Wu ◽

Xiuzhen Han ◽

Jinsheng Ni ◽

Zheng Niu ◽

Wenjiang Huang

Keyword(s):

Primary Production ◽

Gross Primary Production ◽

In Situ Measurements

Download Full-text

Comparison of Machine Learning Methods to Up-Scale Gross Primary Production

Remote Sensing ◽

10.3390/rs13132448 ◽

2021 ◽

Vol 13 (13) ◽

pp. 2448

Author(s):

Tao Yu ◽

Qiang Zhang ◽

Rui Sun

Keyword(s):

Machine Learning ◽

Random Forest ◽

Primary Production ◽

Eddy Covariance ◽

Carbon Flux ◽

Gross Primary Production ◽

Regional Scale ◽

Learning Methods ◽

Flux Tower ◽

Machine Learning Methods

Eddy covariance observation is an applicable way to obtain accurate and continuous carbon flux at flux tower sites, while remote sensing technology could estimate carbon exchange and carbon storage at regional and global scales effectively. However, it is still challenging to up-scale the field-observed carbon flux to a regional scale, due to the heterogeneity and the unstable air conditions at the land surface. In this paper, gross primary production (GPP) from ground eddy covariance systems were up-scaled to a regional scale by using five machine learning methods (Cubist regression tree, random forest, support vector machine, artificial neural network, and deep belief network). Then, the up-scaled GPP were validated using GPP at flux tower sites, weighted GPP in the footprint, and MODIS GPP products. At last, the sensitivity of the input data (normalized difference vegetation index, fractional vegetation cover, shortwave radiation, relative humidity and air temperature) to the precision of up-scaled GPP was analyzed, and the uncertainty of the machine learning methods was discussed. The results of this paper indicated that machine learning methods had a great potential in up-scaling GPP at flux tower sites. The validation of up-scaled GPP, using five machine learning methods, demonstrated that up-scaled GPP using random forest obtained the highest accuracy.

Download Full-text

Upscaling Solar-Induced Chlorophyll Fluorescence from an Instantaneous to Daily Scale Gives an Improved Estimation of the Gross Primary Productivity

Remote Sensing ◽

10.3390/rs10101663 ◽

2018 ◽

Vol 10 (10) ◽

pp. 1663 ◽

Cited By ~ 10

Author(s):

Jiaochan Hu ◽

Liangyun Liu ◽

Jian Guo ◽

Shanshan Du ◽

Xinjie Liu

Keyword(s):

Chlorophyll Fluorescence ◽

Spatial Scales ◽

Gross Primary Production ◽

Weather Conditions ◽

Coefficient Of Determination ◽

Atmospheric Scattering ◽

Daily Scale ◽

Weather Changes ◽

Length Of The Day

Solar-induced chlorophyll fluorescence (SIF) is closely linked to the photosynthesis of plants and has the potential to estimate gross primary production (GPP) at different temporal and spatial scales. However, remotely sensed SIF at a ground or space level is usually instantaneous, which cannot represent the daily total SIF. The temporal mismatch between instantaneous SIF (SIFinst) and daily GPP (GPPdaily) impacts their correlation across space and time. Previous studies have upscaled SIFinst to the daily scale based on the diurnal cycle in the cosine of the solar zenith angle ( cos ( SZA ) ) to correct the effects of latitude and length of the day on the variations in the SIF-GPP correlation. However, the important effects of diurnal weather changes due to cloud and atmospheric scattering were not considered. In this study, we present a SIF upscaling method using photosynthetically active radiation (PAR) as a driving variable. First, a conversion factor (i.e., the ratio of the instantaneous PAR (PARinst) to daily PAR (PARdaily)) was used to upscale in-situ SIF measurements from the instantaneous to daily scale. Then, the performance of the SIF upscaling method was evaluated under changing weather conditions and different latitudes using continuous tower-based measurements at two sites. The results prove that our PAR-based method can reduce not only latitude-dependent but also the weather-dependent variations in the SIF-GPP model. Specifically, the PAR-based method gave a more accurate prediction of diurnal and daily SIF (SIFdaily) than the cos ( SZA ) -based method, with decreased relative root mean square error (RRMSE) values from 42.2% to 25.6% at half-hour intervals and from 25.4% to 13.3% at daily intervals. Moreover, the PAR-based upscaled SIFdaily had a stronger correlation with the daily absorbed PAR (APAR) than both the SIFinst and cos ( SZA ) -based upscaled SIFdaily, especially for cloudy days with a coefficient of determination (R2) that increased from approximately 0.5 to 0.8. Finally, the PAR-based SIFdaily was linked to GPPdaily and compared to the SIFinst or cos ( SZA ) -based SIFdaily. The results indicate that the SIF-GPP correlation can obviously be improved, with an increased R2 from approximately 0.65 to 0.75. Our study confirms the importance of upscaling SIF from the instantaneous to daily scale when linking SIF with GPP and emphasizes the need to take diurnal weather changes into account for SIF temporal upscaling.

Download Full-text

Moisture availability mediates the relationship between terrestrial gross primary production and solar‐induced chlorophyll fluorescence: Insights from global‐scale variations

Global Change Biology ◽

10.1111/gcb.15373 ◽

2020 ◽

Author(s):

Anping Chen ◽

Jiafu Mao ◽

Daniel Ricciuto ◽

Jingfeng Xiao ◽

Christian Frankenberg ◽

...

Keyword(s):

Chlorophyll Fluorescence ◽

Primary Production ◽

Gross Primary Production ◽

Global Scale ◽

Moisture Availability ◽

The Relationship

Download Full-text

The spatial heterogeneity of the relationship between gross primary production and sun-induced chlorophyll fluorescence regulated by climate conditions during 2007-2018

Global Ecology and Conservation ◽

10.1016/j.gecco.2021.e01721 ◽

2021 ◽

pp. e01721

Author(s):

Yonglin Wang ◽

Lei Zhou ◽

Jie Zhuang ◽

Leigang Sun ◽

Yonggang Chi

Keyword(s):

Chlorophyll Fluorescence ◽

Primary Production ◽

Spatial Heterogeneity ◽

Gross Primary Production ◽

Climate Conditions ◽

The Relationship

Download Full-text

Multiscale observations of NH<sub>3</sub> around Toronto, Canada

Atmospheric Measurement Techniques ◽

10.5194/amt-14-905-2021 ◽

2021 ◽

Vol 14 (2) ◽

pp. 905-921

Author(s):

Shoma Yamanouchi ◽

Camille Viatte ◽

Kimberly Strong ◽

Erik Lutsch ◽

Dylan B. A. Jones ◽

...

Keyword(s):

Temporal Variability ◽

Grid Point ◽

Regional Scale ◽

Model Performance ◽

In Situ Measurements ◽

Coefficient Of Determination ◽

Spatial And Temporal Variability ◽

Predictive Capacity ◽

Total Column

Abstract. Ammonia (NH3) is a major source of nitrates in the atmosphere and a major source of fine particulate matter. As such, there have been increasing efforts to measure the atmospheric abundance of NH3 and its spatial and temporal variability. In this study, long-term measurements of NH3 derived from multiscale datasets are examined. These NH3 datasets include 16 years of total column measurements using Fourier transform infrared (FTIR) spectroscopy, 3 years of surface in situ measurements, and 10 years of total column measurements from the Infrared Atmospheric Sounding Interferometer (IASI). The datasets were used to quantify NH3 temporal variability over Toronto, Canada. The multiscale datasets were also compared to assess the representativeness of the FTIR measurements. All three time series showed positive trends in NH3 over Toronto: 3.34 ± 0.89 %/yr from 2002 to 2018 in the FTIR columns, 8.88 ± 5.08 %/yr from 2013 to 2017 in the surface in situ data, and 8.38 ± 1.54 %/yr from 2008 to 2018 in the IASI columns. To assess the representative scale of the FTIR NH3 columns, correlations between the datasets were examined. The best correlation between FTIR and IASI was obtained with coincidence criteria of ≤25 km and ≤20 min, with r=0.73 and a slope of 1.14 ± 0.06. Additionally, FTIR column and in situ measurements were standardized and correlated. Comparison of 24 d averages and monthly averages resulted in correlation coefficients of r=0.72 and r=0.75, respectively, although correlation without averaging to reduce high-frequency variability led to a poorer correlation, with r=0.39. The GEOS-Chem model, run at 2∘ × 2.5∘ resolution, was compared to FTIR and IASI to assess model performance and investigate the correlation of observational data and model output, both with local column measurements (FTIR) and measurements on a regional scale (IASI). Comparisons on a regional scale (a domain spanning 35 to 53∘ N and 93.75 to 63.75∘ W) resulted in r=0.57 and thus a coefficient of determination, which is indicative of the predictive capacity of the model, of r2=0.33, but comparing a single model grid point against the FTIR resulted in a poorer correlation, with r2=0.13, indicating that a finer spatial resolution is needed for modeling NH3.

Download Full-text

Leveraging Very-High Spatial Resolution Hyperspectral and Thermal UAV Imageries for Characterizing Diurnal Indicators of Grapevine Physiology

Remote Sensing ◽

10.3390/rs12193216 ◽

2020 ◽

Vol 12 (19) ◽

pp. 3216 ◽

Cited By ~ 1

Author(s):

Matthew Maimaitiyiming ◽

Vasit Sagan ◽

Paheding Sidike ◽

Maitiniyazi Maimaitijiang ◽

Allison J. Miller ◽

...

Keyword(s):

Canopy Temperature ◽

Crop Productivity ◽

In Situ Measurements ◽

Sensor Technology ◽

Stress Index ◽

Coefficient Of Determination ◽

Neural Net ◽

Water Stress Index ◽

Very High

Efficient and accurate methods to monitor crop physiological responses help growers better understand crop physiology and improve crop productivity. In recent years, developments in unmanned aerial vehicles (UAV) and sensor technology have enabled image acquisition at very-high spectral, spatial, and temporal resolutions. However, potential applications and limitations of very-high-resolution (VHR) hyperspectral and thermal UAV imaging for characterization of plant diurnal physiology remain largely unknown, due to issues related to shadow and canopy heterogeneity. In this study, we propose a canopy zone-weighting (CZW) method to leverage the potential of VHR (≤9 cm) hyperspectral and thermal UAV imageries in estimating physiological indicators, such as stomatal conductance (Gs) and steady-state fluorescence (Fs). Diurnal flights and concurrent in-situ measurements were conducted during grapevine growing seasons in 2017 and 2018 in a vineyard in Missouri, USA. We used neural net classifier and the Canny edge detection method to extract pure vine canopy from the hyperspectral and thermal images, respectively. Then, the vine canopy was segmented into three canopy zones (sunlit, nadir, and shaded) using K-means clustering based on the canopy shadow fraction and canopy temperature. Common reflectance-based spectral indices, sun-induced chlorophyll fluorescence (SIF), and simplified canopy water stress index (siCWSI) were computed as image retrievals. Using the coefficient of determination (R2) established between the image retrievals from three canopy zones and the in-situ measurements as a weight factor, weighted image retrievals were calculated and their correlation with in-situ measurements was explored. The results showed that the most frequent and the highest correlations were found for Gs and Fs, with CZW-based Photochemical reflectance index (PRI), SIF, and siCWSI (PRICZW, SIFCZW, and siCWSICZW), respectively. When all flights combined for the given field campaign date, PRICZW, SIFCZW, and siCWSICZW significantly improved the relationship with Gs and Fs. The proposed approach takes full advantage of VHR hyperspectral and thermal UAV imageries, and suggests that the CZW method is simple yet effective in estimating Gs and Fs.

Download Full-text

The Simulated in situ Measurements of Oceanic Primary Production

Marine and Freshwater Research ◽

10.1071/mf9630139 ◽

1963 ◽

Vol 14 (2) ◽

pp. 139 ◽

Cited By ~ 23

Author(s):

HR Jitts

Keyword(s):

Primary Production ◽

In Situ Measurements ◽

Light Conditions ◽

Significant Relation ◽

Blue Glass ◽

Simultaneous Measurements ◽

Reflected Light

Simultaneous measurements with two types of incubators were made on replicate samples both in the incubators and in situ in the ocean. Both incubators used sunlight and blue glass filters to simulate light conditions at depths in the ocean. The first gave measurements of column production 1.58 times those in situ. This was due to the fact that at depths greater than 20 m the incubator gave much higher results with no significant relation to those measured in situ. In the second incubator the accuracy of reproduction of oceanic light conditions was improved by reducing reflected light and using a balance-by-depth twin photometer system for determining the depths of sampling. The measurements of column production in the second incubator were 1.03 times the in situ values.

Download Full-text