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

2021 ◽  
Vol 755 ◽  
pp. 142569
Author(s):  
Songhan Wang ◽  
Yongguang Zhang ◽  
Weimin Ju ◽  
Bo Qiu ◽  
Zhaoying Zhang
Keyword(s):  
Primary Production ◽  
Near Infrared ◽  
Gross Primary Production ◽  
Infrared Reflectance ◽  
Near Infrared Reflectance ◽  
Annual Variations ◽  
Reflectance Data

scholarly journals Sun-induced fluorescence and near-infrared reflectance of vegetation track the seasonal dynamics of gross primary production over Africa

2021 ◽  
Vol 18 (9) ◽  
pp. 2843-2857
Author(s):  
Anteneh Getachew Mengistu ◽  
Gizaw Mengistu Tsidu ◽  
Gerbrand Koren ◽  
Maurits L. Kooreman ◽  
K. Folkert Boersma ◽  
...  
Keyword(s):  
Primary Production ◽  
Vegetation Index ◽  
Near Infrared ◽  
Numerical Models ◽  
Gross Primary Production ◽  
Infrared Reflectance ◽  
Terrestrial Vegetation ◽  
Max Planck ◽  
Near Infrared Reflectance ◽  
Validation Data

Abstract. The carbon cycle of tropical terrestrial vegetation plays a vital role in the storage and exchange of atmospheric CO2. But large uncertainties surround the impacts of land-use change emissions, climate warming, the frequency of droughts, and CO2 fertilization. This culminates in poorly quantified carbon stocks and carbon fluxes even for the major ecosystems of Africa (savannas and tropical evergreen forests). Contributors to this uncertainty are the sparsity of (micro-)meteorological observations across Africa's vast land area, a lack of sufficient ground-based observation networks and validation data for CO2, and incomplete representation of important processes in numerical models. In this study, we therefore turn to two remotely sensed vegetation products that have been shown to correlate highly with gross primary production (GPP): sun-induced fluorescence (SIF) and near-infrared reflectance of vegetation (NIRv). The former is available from an updated product that we recently published (Sun-Induced Fluorescence of Terrestrial Ecosystems Retrieval – SIFTER v2), which specifically improves retrievals in tropical environments. A comparison against flux tower observations of daytime-partitioned net ecosystem exchange from six major biomes in Africa shows that SIF and NIRv reproduce the seasonal patterns of GPP well, resulting in correlation coefficients of >0.9 (N=12 months, four sites) over savannas in the Northern and Southern hemispheres. These coefficients are slightly higher than for the widely used Max Planck Institute for Biogeochemistry (MPI-BGC) GPP products and enhanced vegetation index (EVI). Similarly to SIF signals in the neighboring Amazon, peak productivity occurs in the wet season coinciding with peak soil moisture and is followed by an initial decline during the early dry season, which reverses when light availability peaks. This suggests similar leaf dynamics are at play. Spatially, SIF and NIRv show a strong linear relation (R>0.9; N≥250 pixels) with multi-year MPI-BGC GPP even within single biomes. Both MPI-BGC GPP and the EVI show saturation relative to peak NIRv and SIF signals during high-productivity months, which suggests that GPP in the most productive regions of Africa might be larger than suggested.

scholarly journals Sun-induced Fluorescence and Near Infrared Reflectance of vegetation track the seasonal dynamics of gross primary production over Africa

2020 ◽  
Author(s):  
Anteneh Getachew Mengistu ◽  
Gizaw Mengistu Tsidu ◽  
Gerbrand Koren ◽  
Maurits L. Kooreman ◽  
K. Folkert Boersma ◽  
...  
Keyword(s):  
Primary Production ◽  
Vegetation Index ◽  
Near Infrared ◽  
Numerical Models ◽  
Gross Primary Production ◽  
Infrared Reflectance ◽  
Terrestrial Vegetation ◽  
Wet Season ◽  
Near Infrared Reflectance ◽  
Validation Data

Abstract. The carbon cycle of tropical terrestrial vegetation plays a vital role in the storage and exchange of atmospheric CO2. But large uncertainties surround the impacts of land-use change emissions, climate warming, the frequency of droughts, and CO2 fertilization. This culminates in poorly quantified carbon stocks and carbon fluxes even for the major ecosystems of Africa (savannas, and tropical evergreen forests). Contributors to this uncertainty are the sparsity of (micro-)meteorological observations across Africa's vast land area, a lack of sufficient ground-based observation networks and validation data for CO2, and incomplete representation of important processes in numerical models. In this study, we, therefore, turn to two remotely-sensed vegetation products that have shown to correlate highly with Gross Primary Production (GPP): Sun-Induced Fluorescence (SIF) and Near-Infrared Reflectance of vegetation (NIRv). The former is available from an updated product that we recently published (SIFTER v2), which specifically improves retrievals in tropical environments. A comparison against flux tower observations of daytime-partitioned Net Ecosystem Exchange from six major biomes in Africa shows that SIF and NIRv reproduce the seasonal patterns of GPP well, resulting in correlation coefficients of > 0.9 (N = 12 months, 4 sites) over savannas in the northern and southern hemispheres. These coefficients are slightly higher than for the widely used MPI-BGC GPP products and Enhanced Vegetation Index (EVI). Similar to SIF signals in the neighboring Amazon, peak productivity occurs in the wet season coinciding with peak soil moisture, and is followed by an initial decline during the early dry season, that reverses when light availability peaks. This suggests similar leaf dynamics are at play. Spatially, SIF and NIRv show a strong linear relation (R > 0.9, N = 250 + pixels) with multi-year MPI-BGC GPP even within single biomes. Both MPI-BGC GPP and EVI show saturation relative to peak NIRv and SIF signals during high productivity months, which suggests that GPP in the most productive regions of Africa might be larger than suggested.

scholarly journals Can vegetation index track the interannual variation in gross primary production of temperate deciduous forests?

2021 ◽  
Vol 10 (1) ◽  
Author(s):  
Fan Liu ◽  
Chuankuan Wang ◽  
Xingchang Wang
Keyword(s):  
Primary Production ◽  
Vegetation Index ◽  
Near Infrared ◽  
Deciduous Forest ◽  
Gross Primary Production ◽  
Growing Season ◽  
Temperate Deciduous Forest ◽  
Near Infrared Reflectance ◽  
Modis Ndvi ◽  
Temperate Deciduous

Abstract Background Vegetation indices (VIs) by remote sensing are widely used as simple proxies of the gross primary production (GPP) of vegetation, but their performances in capturing the inter-annual variation (IAV) in GPP remain uncertain. Methods We evaluated the performances of various VIs in tracking the IAV in GPP estimated by eddy covariance in a temperate deciduous forest of Northeast China. The VIs assessed included the normalized difference vegetation index (NDVI), the enhanced vegetation index (EVI), and the near-infrared reflectance of vegetation (NIRv) obtained from tower-radiometers (broadband) and the Moderate Resolution Imaging Spectroradiometer (MODIS), respectively. Results We found that 25%–35% amplitude of the broadband EVI tracked the start of growing season derived by GPP (R2: 0.56–0.60, bias < 4 d), while 45% (or 50%) amplitudes of broadband (or MODIS) NDVI represented the end of growing season estimated by GPP (R2: 0.58–0.67, bias < 3 d). However, all the VIs failed to characterize the summer peaks of GPP. The growing-season integrals but not averaged values of the broadband NDVI, MODIS NIRv and EVI were robust surrogates of the IAV in GPP (R2: 0.40–0.67). Conclusion These findings illustrate that specific VIs are effective only to capture the GPP phenology but not the GPP peak, while the integral VIs have the potential to mirror the IAV in GPP.

Determination of Particle Size in Powders by Scatter Correction in Diffuse Near-Infrared Reflectance

1988 ◽  
Vol 42 (5) ◽  
pp. 722-728 ◽  
Author(s):  
J. L. Ilari ◽  
H. Martens ◽  
T. Isaksson
Keyword(s):  
Particle Size ◽  
Near Infrared ◽  
Scatter Correction ◽  
Large Degree ◽  
Chemical Compositions ◽  
Infrared Reflectance ◽  
Near Infrared Reflectance ◽  
Analytical Technique ◽  
Scattering Effects ◽  
Reflectance Data

Diffuse near-infrared reflectance spectroscopy has traditionally been an analytical technique for determining chemical compositions in a sample. We will, in this paper, focus on light scattering effects and their ability to determine the mean particle sizes of powders. The reflectance data of NaCl, broken glass, and sorbitol powders are linearized and submitted to the Multiplicative Scatter Correction (MSC), and the ensuing parameters are used in subsequent multivariate calibrations. The results indicate that particle size can, to a large degree, be determined from NIR reflectance data for a given type of powder. Up to 99% of the partical size variance was explained by the regression.

A comparison of sample preparation and calibration techniques for the estimation of nitrogen, oil and glucosinolate content of rapeseed by near infrared spectroscopy

1985 ◽  
Vol 104 (2) ◽  
pp. 317-323 ◽  
Author(s):  
Carol Starr ◽  
Janet Suttle ◽  
A. G. Morgan ◽  
D. B. Smith
Keyword(s):  
Stepwise Regression ◽  
Near Infrared ◽  
Glucosinolate Content ◽  
Infrared Reflectance ◽  
Near Infrared Reflectance ◽  
Reflectance Analysis ◽  
Glucosinolate Concentration ◽  
Forward Stepwise ◽  
Calibration Techniques ◽  
Reflectance Data

SummaryPredictions of nitrogen, oil and glucosinolate concentration in rapeseed samples were made by near infrared reflectance analysis after various grinding treatments. Also examined were the effects of normalizing reflectance data and the possible advantage of using all combinations of two and three wavelengths in the calibration regression analysis over forward stepwise regression. The main conclusion was that drying the samples prior to a controlled grinding treatment gave the best results, although acceptable results for selection purposes could be obtained using whole seeds to predict nitrogen and oil. None of the treatments of the seed or reflectance data allowed acceptable prediction of glucosinolate content.

Sign in / Sign up

Export Citation Format

Share Document