scholarly journals Remotely-sensed detection of effects of extreme droughts on gross primary production

2016 ◽  
Vol 6 (1) ◽  
Author(s):  
Sara Vicca ◽  
Manuela Balzarolo ◽  
Iolanda Filella ◽  
André Granier ◽  
Mathias Herbst ◽  
...  
Keyword(s):  
Primary Production ◽  
Gross Primary Production ◽  
Remotely Sensed

scholarly journals A Comparison of OCO-2 SIF, MODIS GPP, and GOSIF Data from Gross Primary Production (GPP) Estimation and Seasonal Cycles in North America

2020 ◽  
Vol 12 (2) ◽  
pp. 258 ◽  
Author(s):  
Ruonan Qiu ◽  
Ge Han ◽  
Xin Ma ◽  
Hao Xu ◽  
Tianqi Shi ◽  
...  
Keyword(s):  
North America ◽  
Primary Production ◽  
Vegetation Type ◽  
Gross Primary Production ◽  
Remotely Sensed ◽  
Seasonal Cycles ◽  
Vegetation Types ◽  
Growth Trend ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Highly Correlated

Remotely sensed products are of great significance to estimating global gross primary production (GPP), which helps to provide insight into climate change and the carbon cycle. Nowadays, there are three types of emerging remotely sensed products that can be used to estimate GPP, namely, MODIS GPP (Moderate Resolution Imaging Spectroradiometer GPP, MYD17A2H), OCO-2 SIF, and GOSIF. In this study, we evaluated the performances of three products for estimating GPP and compared with GPP of eddy covariance(EC) from the perspectives of a single tower (23 flux towers) and vegetation types (evergreen needleleaf forests, deciduous broadleaf forests, open shrublands, grasslands, closed shrublands, mixed forests, permeland wetlands, and croplands) in North America. The results revealed that sun-induced chlorophyll fluorescence (SIF) data and MODIS GPP data were highly correlated with the GPP of flux towers (GPPEC). GOSIF and OCO-2 SIF products exhibit a higher accuracy in GPP estimation at the a single tower (GOSIF: R2 = 0.13–0.88, p < 0.001; OCO-2 SIF: R2 = 0.11–0.99, p < 0.001; MODIS GPP: R2 = 0.15–0.79, p < 0.001). MODIS GPP demonstrates a high correlation with GPPEC in terms of the vegetation type, but it underestimates the GPP by 1.157 to 3.884 gCm−2day−1 for eight vegetation types. The seasonal cycles of GOSIF and MODIS GPP are consistent with that of GPPEC for most vegetation types, in spite of an evident advanced seasonal cycle for grasslands and evergreen needleleaf forests. Moreover, the results show that the observation mode of OCO-2 has an evident impact on the accuracy of estimating GPP using OCO-2 SIF products. In general, compared with the other two datasets, the GOSIF dataset exhibits the best performance in estimating GPP, regardless of the extraction range. The long time period of MODIS GPP products can help in the monitoring of the growth trend of vegetation and the change trends of GPP.

scholarly journals Isoprene emissions track the seasonal cycle of canopy temperature, not primary production: evidence from remote sensing

2014 ◽  
Vol 11 (13) ◽  
pp. 3437-3451 ◽  
Author(s):  
P. N. Foster ◽  
I. C. Prentice ◽  
C. Morfopoulos ◽  
M. Siddall ◽  
M. van Weele
Keyword(s):  
Primary Production ◽  
Air Temperature ◽  
Seasonal Cycle ◽  
Gross Primary Production ◽  
Canopy Temperature ◽  
Remotely Sensed ◽  
Formaldehyde Concentration ◽  
Isoprene Emission ◽  
The Tropics ◽  
Better Than

Abstract. Isoprene is important in atmospheric chemistry, but its seasonal emission pattern – especially in the tropics, where most isoprene is emitted – is incompletely understood. We set out to discover generalized relationships applicable across many biomes between large-scale isoprene emission and a series of potential predictor variables, including both observed and model-estimated variables related to gross primary production (GPP) and canopy temperature. We used remotely sensed atmospheric concentrations of formaldehyde, an intermediate oxidation product of isoprene, as a proxy for isoprene emission in 22 regions selected to span high to low latitudes, to sample major biomes, and to minimize interference from pyrogenic sources of volatile organic compounds that could interfere with the isoprene signal. Formaldehyde concentrations showed the highest average seasonal correlations with remotely sensed (r = 0.85) and model-estimated (r = 0.80) canopy temperatures. Both variables predicted formaldehyde concentrations better than air temperature (r= 0.56) and a "reference" isoprene model that combines GPP and an exponential function of temperature (r = 0.49), and far better than either remotely sensed green vegetation cover, fPAR (r = 0.25) or model-estimated GPP (r = 0.14). Gross primary production in tropical regions was anti-correlated with formaldehyde concentration (r = −0.30), which peaks during the dry season. Our results were most reliable in the tropics, where formaldehyde observational errors were the least. The tropics are of particular interest because they are the greatest source of isoprene emission as well as the region where previous modelling attempts have been least successful. We conjecture that positive correlations of isoprene emission with GPP and air temperature (as found in temperate forests) may arise simply because both covary with canopy temperature, peaking during the relatively short growing season. The lack of a general correlation between GPP and formaldehyde concentration in the seasonal cycle is consistent with experimental evidence that isoprene emission rates are largely decoupled from photosynthetic rates, and with the likely adaptive significance of isoprene emission in protecting leaves against heat damage and oxidative stress.

scholarly journals Relationships between primary production and crop yields in semi-arid and arid irrigated agro-ecosystems

2015 ◽  
Vol XL-7/W3 ◽  
pp. 27-30 ◽  
Author(s):  
H. H. Jaafar ◽  
F. A. Ahmad
Keyword(s):  
Primary Production ◽  
Crop Yield ◽  
Vegetation Index ◽  
Crop Yields ◽  
Gross Primary Production ◽  
Remotely Sensed ◽  
Early Assessment ◽  
Food Shortages ◽  
Semi Arid ◽  
Summer Crop

In semi-arid areas within the MENA region, food security problems are the main problematic imposed. Remote sensing can be a promising too early diagnose food shortages and further prevent the population from famine risks. This study is aimed at examining the possibility of forecasting yield before harvest from remotely sensed MODIS-derived Enhanced Vegetation Index (EVI), Net photosynthesis (net PSN), and Gross Primary Production (GPP) in semi-arid and arid irrigated agro-ecosystems within the conflict affected country of Syria. Relationships between summer yield and remotely sensed indices were derived and analyzed. Simple regression spatially-based models were developed to predict summer crop production. The validation of these models was tested during conflict years. A significant correlation (p<0.05) was found between summer crop yield and EVI, GPP and net PSN. Results indicate the efficiency of remotely sensed-based models in predicting summer yield, mostly for cotton yields and vegetables. Cumulative summer EVI-based model can predict summer crop yield during crisis period, with deviation less than 20% where vegetables are the major yield. This approach prompts to an early assessment of food shortages and lead to a real time management and decision making, especially in periods of crisis such as wars and drought.

Gross Primary Production Estimation in Crops Using Solely Remotely Sensed Data

2019 ◽  
Vol 111 (6) ◽  
pp. 2981-2990 ◽  
Author(s):  
Yi Peng ◽  
Oz Kira ◽  
Anthony Nguy‐Robertson ◽  
Andrew Suyker ◽  
Timothy Arkebauer ◽  
...  
Keyword(s):  
Primary Production ◽  
Gross Primary Production ◽  
Remotely Sensed ◽  
Remotely Sensed Data

scholarly journals Isoprene emissions track the seasonal cycle of canopy temperature, not primary production: evidence from remote sensing

2013 ◽  
Vol 10 (12) ◽  
pp. 19571-19601
Author(s):  
P. N. Foster ◽  
I. C. Prentice ◽  
C. Morfopoulos ◽  
M. Siddall ◽  
M. van Weele
Keyword(s):  
Primary Production ◽  
Air Temperature ◽  
Large Scale ◽  
Gross Primary Production ◽  
Canopy Temperature ◽  
Remotely Sensed ◽  
Formaldehyde Concentration ◽  
Tropical Regions ◽  
Isoprene Emission ◽  
Better Than

Abstract. Isoprene is important in atmospheric chemistry, but its seasonal emission pattern – especially in the tropics, where most isoprene is emitted – is incompletely understood. We set out to discover general, biome-independent relationships between large-scale isoprene emission and a series of potential predictor variables, including both observed and model-estimated variables related to gross primary production (GPP) and canopy temperature. To this end we used remotely sensed atmospheric concentrations of formaldehyde, an intermediate oxidation product of isoprene, as a proxy for isoprene emission in 22 regions selected to span high to low latitudes, to sample major biomes, and to minimize interference from pyrogenic sources of volatile organic compounds that could interfere with the isoprene signal. Formaldehyde concentrations showed the highest average seasonal correlations with remotely sensed (r = 0.85) and model-estimated (r = 0.80) canopy temperatures. Both variables predicted formaldehyde concentrations better than air temperature (r = 0.56) and a "reference" isoprene model that includes both temperature and GPP (r = 0.49), and far better than either remotely sensed green vegetation cover (r = 0.25) or model-estimated GPP (r = 0.14). GPP in tropical regions was anti-correlated with formaldehyde concentration (r = –0.30), which peaks during the dry season. We conjecture that the positive correlations of isoprene emission with primary production, and with air temperature, found in temperate forest regions arise simply because all three peak during the relatively short growing season. In most tropical regions, where the seasonal cycles of GPP and canopy temperature are very different, isoprene emission is revealed to depend on canopy temperature but not at all on GPP. The lack of a general correlation between GPP and formaldehyde concentration is consistent with experimental evidence that isoprene emission is decoupled from photosynthesis, and with the likely adaptive significance of isoprene emission in protecting leaves against heat damage and oxidative stress. In contrast, the high correlation between canopy temperature and formaldehyde concentration indicates the importance of including canopy temperature explicitly in large-scale models.

scholarly journals Estimating the Gross Primary Production and Evapotranspiration of Rice Paddy Fields in the Sub-Tropical Region of China Using a Remotely-Sensed Based Water-Carbon Coupled Model

2021 ◽  
Vol 13 (17) ◽  
pp. 3470
Author(s):  
Guojing Gan ◽  
Xiaosong Zhao ◽  
Xingwang Fan ◽  
Henwang Xie ◽  
Weirong Jin ◽  
...  
Keyword(s):  
Primary Production ◽  
Seasonal Variations ◽  
Gross Primary Production ◽  
Regional Scale ◽  
Coupled Model ◽  
Rice Paddy ◽  
Remotely Sensed ◽  
Paddy Fields ◽  
Tropical Region ◽  
Rice Paddy Fields

Rice serves as the staple food for over 50% of the global population. Remotely-sensed based estimation of the gross primary production (GPP) and evapotranspiration (ET) of rice paddy fields is essential to assess global food security. In this study, we tested the application of a recently proposed remotely-sensed based water-carbon coupled model (PML-V2) in the lower reaches of the Poyang Lake plain, which is one of the nine production bases for crops in China. Evaluation using the eddy covariance measurements showed that, after parameter localization, the model reproduced the seasonal variations of GPP and ET for both the early rice and the late rice. The model performed reasonably well in the validation period because the key parameters (e.g., the quantum efficiency and the stomatal conductance coefficient) exhibited predictable seasonal variations. At the regional scale, the spatial distribution in multi-year GPP of rice (1365 ± 326 gCm−2year−1) can be explained by the vegetation cover fraction (R2 > 0.9); in comparison, the multi-year ET (1003 ± 65 mm/year) exhibits smaller spatial variations due to the high evaporation rate of the saturated soil surface of paddy fields. The water use efficiency of rice in this region varies around 1.35 gC/kgH2O with a standard deviation of 0.30. Our study shows that GPP and ET of rice can be estimated by remote sensing models without detailed crop management information, which is usually unavailable at regional scales.

scholarly journals Estimation of crop gross primary production (GPP): fAPARchl versus MOD15A2 FPAR

2014 ◽  
Vol 153 ◽  
pp. 1-6 ◽  
Author(s):  
Qingyuan Zhang ◽  
Yen-Ben Cheng ◽  
Alexei I. Lyapustin ◽  
Yujie Wang ◽  
Feng Gao ◽  
...  
Keyword(s):  
Primary Production ◽  
Gross Primary Production
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