scholarly journals Estimating Gross Primary Productivity (GPP) over Rice–Wheat-Rotation Croplands by Using the Random Forest Model and Eddy Covariance Measurements: Upscaling and Comparison with the MODIS Product

2021 ◽  
Vol 13 (21) ◽  
pp. 4229
Author(s):  
Zexia Duan ◽  
Yuanjian Yang ◽  
Shaohui Zhou ◽  
Zhiqiu Gao ◽  
Lian Zong ◽  
...  
Keyword(s):  
Random Forest ◽  
Eddy Covariance ◽  
Primary Productivity ◽  
Eastern China ◽  
Gross Primary Productivity ◽  
Coefficient Of Determination ◽  
Active Growth ◽  
Carbon Cycles ◽  
Rotation Periods ◽  
Moderate Resolution Imaging Spectroradiometer

Despite advances in remote sensing–based gross primary productivity (GPP) modeling, the calibration of the Moderate Resolution Imaging Spectroradiometer (MODIS) GPP product (GPPMOD) is less well understood over rice–wheat-rotation cropland. To improve the performance of GPPMOD, a random forest (RF) machine learning model was constructed and employed over the rice–wheat double-cropping fields of eastern China. The RF-derived GPP (GPPRF) agreed well with the eddy covariance (EC)-derived GPP (GPPEC), with a coefficient of determination of 0.99 and a root-mean-square error of 0.42 g C m−2 d−1. Therefore, it was deemed reliable to upscale GPPEC to regional scales through the RF model. The upscaled cumulative seasonal GPPRF was higher for rice (924 g C m−2) than that for wheat (532 g C m−2). By comparing GPPMOD and GPPEC, we found that GPPMOD performed well during the crop rotation periods but underestimated GPP during the rice/wheat active growth seasons. Furthermore, GPPMOD was calibrated by GPPRF, and the error range of GPP MOD (GPPRF minus GPPMOD) was found to be 2.5–3.25 g C m−2 d−1 for rice and 0.75–1.25 g C m−2 d−1 for wheat. Our findings suggest that RF-based GPP products have the potential to be applied in accurately evaluating MODIS-based agroecosystem carbon cycles at regional or even global scales.

scholarly journals Evaluating the Performance of Sentinel-3A OLCI Land Products for Gross Primary Productivity Estimation Using AmeriFlux Data

2020 ◽  
Vol 12 (12) ◽  
pp. 1927 ◽  
Author(s):  
Zhijiang Zhang ◽  
Lin Zhao ◽  
Aiwen Lin
Keyword(s):  
Primary Productivity ◽  
Land Surface ◽  
Vegetation Index ◽  
Meteorological Data ◽  
Gross Primary Productivity ◽  
Carbon Cycles ◽  
Moderate Resolution ◽  
Resolution Imaging ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
The Relationship

Accurate and reliable estimation of gross primary productivity (GPP) is of great significance in monitoring global carbon cycles. The fraction of absorbed photosynthetically active radiation (FAPAR) and vegetation index products of the Moderate Resolution Imaging Spectroradiometer (MODIS) are currently the most widely used data in evaluating GPP. The launch of the Ocean and Land Colour Instrument (OLCI) onboard the Sentinel-3 satellite provides the FAPAR and the OLCI Terrestrial Chlorophyll Index (OTCI) products with higher temporal resolution and smoother spatial distribution than MODIS, having the potential to monitor terrain GPP. OTCI is one of the red-edge indices and is particularly sensitive to canopy chlorophyll content related to GPP. The purpose of the study is to evaluate the performance of OLCI FAPAR and OTCI for the estimation of GPP across seven biomes in 2017–2018. To this end, OLCI FAPAR and OTCI products in combination with insitu meteorological data were first integrated into the MODIS GPP algorithm and in three OTCI-driven models to simulate GPP. The modeled GPP (GPPOLCI-FAPAR and GPPOTCI) were then compared with flux tower GPP (GPPEC) for each site. Furthermore, the GPPOLCI-FAPAR and GPP derived from the MODIS FAPAR (GPPMODIS-FAPAR) were compared. Results showed that the performance of GPPOLCI-FAPAR was varied in different sites, with the highest R2 of 0.76 and lowest R2 of 0.45. The OTCI-driven models that include APAR data exhibited a significant relationship with GPPEC for all sites, and models using only OTCI provided the most varied performance, with the relationship between GPPOTCI and GPPEC from strong to nonsignificant. Moreover, GPPOLCI-FAPAR (R2 = 0.55) performed better than GPPMODIS-FAPAR (R2 = 0.44) across all biomes. These results demonstrate the potential of OLCI FAPAR and OTCI products in GPP estimation, and they also provide the basis for their combination with the soon-to-launch Fluorescence Explorer satellite and their integration with the Sentinel-3 land surface temperature product into light use models for GPP monitoring at regional and global scales.

scholarly journals Evaluating the Performance of Satellite-Derived Vegetation Indices for Estimating Gross Primary Productivity Using FLUXNET Observations across the Globe

2019 ◽  
Vol 11 (15) ◽  
pp. 1823 ◽  
Author(s):  
Xiaojuan Huang ◽  
Jingfeng Xiao ◽  
Mingguo Ma
Keyword(s):  
Primary Productivity ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Vegetation Indices ◽  
Gross Primary Productivity ◽  
Surface Reflectance ◽  
Monthly Scale ◽  
Moderate Resolution ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Annual Scale

Satellite-derived vegetation indices (VIs) have been widely used to approximate or estimate gross primary productivity (GPP). However, it remains unclear how the VI-GPP relationship varies with indices, biomes, timescales, and the bidirectional reflectance distribution function (BRDF) effect. We examined the relationship between VIs and GPP for 121 FLUXNET sites across the globe and assessed how the VI-GPP relationship varied among a variety of biomes at both monthly and annual timescales. We used three widely-used VIs: normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), and 2-band EVI (EVI2) as well as a new VI - NIRV and used surface reflectance both with and without BRDF correction from the moderate resolution imaging spectroradiometer (MODIS) to calculate these indices. The resulting traditional (NDVI, EVI, EVI2, and NIRV) and BRDF-corrected (NDVIBRDF, EVIBRDF, EVI2BRDF, and NIRV, BRDF) VIs were used to examine the VI-GPP relationship. At the monthly scale, all VIs were moderate or strong predictors of GPP, and the BRDF correction improved their performance. EVI2BRDF and NIRV, BRDF had similar performance in capturing the variations in tower GPP as did the MODIS GPP product. The VIs explained lower variance in tower GPP at the annual scale than at the monthly scale. The BRDF-correction of surface reflectance did not improve the VI-GPP relationship at the annual scale. The VIs had similar capability in capturing the interannual variability in tower GPP as MODIS GPP. VIs were influenced by temperature and water stresses and were more sensitive to temperature stress than to water stress. VIs in combination with environmental factors could improve the prediction of GPP than VIs alone. Our findings can help us better understand how the VI-GPP relationship varies among indices, biomes, and timescales and how the BRDF effect influences the VI-GPP relationship.

scholarly journals Joint control of terrestrial gross primary productivity by plant phenology and physiology

2015 ◽  
Vol 112 (9) ◽  
pp. 2788-2793 ◽  
Author(s):  
Jianyang Xia ◽  
Shuli Niu ◽  
Philippe Ciais ◽  
Ivan A. Janssens ◽  
Jiquan Chen ◽  
...  
Keyword(s):  
Eddy Covariance ◽  
Primary Productivity ◽  
Abiotic Factors ◽  
Plant Phenology ◽  
Gross Primary Productivity ◽  
Fire Disturbance ◽  
Spatiotemporal Variability ◽  
Joint Control ◽  
Time And Space ◽  
Over Time

Terrestrial gross primary productivity (GPP) varies greatly over time and space. A better understanding of this variability is necessary for more accurate predictions of the future climate–carbon cycle feedback. Recent studies have suggested that variability in GPP is driven by a broad range of biotic and abiotic factors operating mainly through changes in vegetation phenology and physiological processes. However, it is still unclear how plant phenology and physiology can be integrated to explain the spatiotemporal variability of terrestrial GPP. Based on analyses of eddy–covariance and satellite-derived data, we decomposed annual terrestrial GPP into the length of the CO2 uptake period (CUP) and the seasonal maximal capacity of CO2 uptake (GPPmax). The product of CUP and GPPmax explained >90% of the temporal GPP variability in most areas of North America during 2000–2010 and the spatial GPP variation among globally distributed eddy flux tower sites. It also explained GPP response to the European heatwave in 2003 (r2 = 0.90) and GPP recovery after a fire disturbance in South Dakota (r2 = 0.88). Additional analysis of the eddy–covariance flux data shows that the interbiome variation in annual GPP is better explained by that in GPPmax than CUP. These findings indicate that terrestrial GPP is jointly controlled by ecosystem-level plant phenology and photosynthetic capacity, and greater understanding of GPPmax and CUP responses to environmental and biological variations will, thus, improve predictions of GPP over time and space.

Evaluation of MODIS gross primary productivity for Africa using eddy covariance data

2013 ◽  
Vol 131 ◽  
pp. 275-286 ◽  
Author(s):  
M. Sjöström ◽  
M. Zhao ◽  
S. Archibald ◽  
A. Arneth ◽  
B. Cappelaere ◽  
...  
Keyword(s):  
Eddy Covariance ◽  
Primary Productivity ◽  
Gross Primary Productivity

Legacy effects of drought on gross primary productivity from eddy-covariance measurements

2021 ◽  
Author(s):  
Xin Yu ◽  
René Orth ◽  
Markus Reichstein ◽  
Ana Bastos
Keyword(s):  
Eddy Covariance ◽  
Primary Productivity ◽  
Carbon Balance ◽  
Climatic Conditions ◽  
Gross Primary Productivity ◽  
Legacy Effects ◽  
Ecosystem Carbon ◽  
Ecosystem Carbon Balance ◽  
Eddy Covariance Measurements ◽  
Effects Of Drought

<p>The frequency and severity of droughts are expected to increase in the wake of climate change. Drought events not only cause direct impacts on the ecosystem carbon balance but also result in legacy effects during the following years. These legacies result from, for example, drought damage to the xylem or the crown which causes impaired growth, or from higher vulnerability to pests and diseases. To understand how droughts might affect the carbon cycle in the future, it is important to consider both direct and legacy effects. Such effects likely affect interannual variability in C fluxes but are challenging to detect in observations, and poorly represented in models. Therefore, the patterns and mechanisms inducing the legacy effects of drought on ecosystem carbon balance are necessarily needed to improve.</p><p>In this study, we analyze gross primary productivity (GPP) from eddy-covariance measurements in Germany to detect legacy effects from recent droughts. We follow a data-driven modeling approach using a random forest model trained in different sets of drought and non-drought periods. This approach allows quantifying legacy effects as deviations of observed GPP from modeled GPP in legacy years, which indicates a change in the vegetation response to hydro-climatic conditions as compared with the training period.</p>

Evapotranspiration and gross primary productivity of secondary vegetation in Amazonia inferred by eddy covariance

2020 ◽  
Vol 294 ◽  
pp. 108141
Author(s):  
Rita de Cassia Silva von Randow ◽  
Javier Tomasella ◽  
Celso von Randow ◽  
Alessandro Carioca de Araújo ◽  
Antonio Ocimar Manzi ◽  
...  
Keyword(s):  
Eddy Covariance ◽  
Primary Productivity ◽  
Gross Primary Productivity ◽  
Secondary Vegetation
Sign in / Sign up

Export Citation Format

Share Document