A MODIS-Derived Primary Productivity Dataset for North America Based on Topographically-Aware Weather Data and Light-Use Efficiency

2018 ◽  
Author(s):  
Sergio Bernardes
Keyword(s):  
North America ◽  
Primary Productivity ◽  
Weather Data ◽  
Light Use Efficiency ◽  
Use Efficiency

scholarly journals Protective Netting Improves Leaf-level Photosynthetic Light Use Efficiency in ‘Honeycrisp’ Apple Under Heat Stress

HortScience ◽  
2018 ◽  
Vol 53 (10) ◽  
pp. 1416-1422 ◽  
Author(s):  
Giverson Mupambi ◽  
Stefano Musacchi ◽  
Sara Serra ◽  
Lee A. Kalcsits ◽  
Desmond R. Layne ◽  
...  
Keyword(s):  
Heat Stress ◽  
Solar Radiation ◽  
Leaf Gas Exchange ◽  
Growing Season ◽  
High Light ◽  
Weather Data ◽  
Light Use Efficiency ◽  
Ambient Temperatures ◽  
Leaf Level ◽  
Use Efficiency

Globally, apple production often occurs in semiarid climates characterized by high summer temperatures and solar radiation. Heat stress events occur regularly during the growing season in these regions. For example, in the semiarid eastern half of Washington State, historic weather data show that, on average, 33% of the days during the growing season exceed 30 °C. To mediate some of the effects of heat stress, protective netting (PN) can be used to reduce the occurrence of fruit sunburn. However, the impacts of reduced solar radiation in a high light environment on light-use efficiency and photosynthesis are poorly understood. We sought to understand the ecophysiological response of apple (Malus domestica Borkh. cv. Honeycrisp) under blue photoselective PN during days with low (26.6 °C), moderate (33.7 °C), or high (38.1 °C) ambient temperatures. Two treatments were evaluated; an uncovered control and blue photoselective PN. Maximum photochemical efficiency of PSII, or photosystem II (Fv/Fm) was significantly greater at all measurement times under blue photoselective PN compared with the control on days with high ambient temperatures. Fv/Fm dropped below 0.79, which is considered the threshold for stress, at 1000 hr in the control and at 1200 hr under blue photoselective PN on a day with high ambient temperature. On days with low or moderate ambient temperatures, Fv/Fm was significantly greater under blue photoselective PN at 1400 hr, which coincided with the peak in solar radiation. ‘Honeycrisp’ apple exhibited dynamic photoinhibition as shown by the diurnal decline in Fv/Fm. Quantum photosynthetic yield of PSII (ΦPSII) was also generally greater under blue photoselective PN compared with the control for days with moderate or high ambient temperatures. Photochemical reflectance index (ΔPRI), the difference in reflectance between a stress-responsive and nonstress-responsive wavelength, was greater under PN compared with the control on the day with high ambient temperatures, with no differences observed under low or moderate ambient temperatures. Leaf gas exchange did not show noticeable improvement under blue photoselective netting when compared with the control despite the improvement in leaf-level photosynthetic light use efficiency. In conclusion, PN reduced incoming solar radiation, improved leaf-level photosynthetic light use efficiency, and reduced the symptoms of photoinhibition in a high-light, arid environment.

scholarly journals Simulating Spatiotemporal Dynamics of Sichuan Grassland Net Primary Productivity Using the CASA Model and In Situ Observations

2014 ◽  
Vol 2014 ◽  
pp. 1-12 ◽  
Author(s):  
Chuanjiang Tang ◽  
Xinyu Fu ◽  
Dong Jiang ◽  
Jingying Fu ◽  
Xinyue Zhang ◽  
...  
Keyword(s):  
Primary Productivity ◽  
Net Primary Productivity ◽  
Alpine Meadow ◽  
Growth Period ◽  
Temporal Variations ◽  
Light Use Efficiency ◽  
Important Indicator ◽  
Casa Model ◽  
Use Efficiency

Net primary productivity (NPP) is an important indicator for grassland resource management and sustainable development. In this paper, the NPP of Sichuan grasslands was estimated by the Carnegie-Ames-Stanford Approach (CASA) model. The results were validated with in situ data. The overall precision reached 70%; alpine meadow had the highest precision at greater than 75%, among the three types of grasslands validated. The spatial and temporal variations of Sichuan grasslands were analyzed. The absorbed photosynthetic active radiation (APAR), light use efficiency (ε), and NPP of Sichuan grasslands peaked in August, which was a vigorous growth period during 2011. High values of APAR existed in the southwest regions in altitudes from 2000 m to 4000 m. Light use efficiency (ε) varied in the different types of grasslands. The Sichuan grassland NPP was mainly distributed in the region of 3000–5000 m altitude. The NPP of alpine meadow accounted for 50% of the total NPP of Sichuan grasslands.

Remote sensing of canopy light use efficiency in temperate and boreal forests of North America using MODIS imagery

2012 ◽  
Vol 118 ◽  
pp. 60-72 ◽  
Author(s):  
Chaoyang Wu ◽  
Jing M. Chen ◽  
Ankur R. Desai ◽  
David Y. Hollinger ◽  
M. Altaf Arain ◽  
...  
Keyword(s):  
Remote Sensing ◽  
North America ◽  
Boreal Forests ◽  
Light Use Efficiency ◽  
Modis Imagery ◽  
Use Efficiency

scholarly journals Tree–grass phenology information improves light use efficiency modelling of gross primary productivity for an Australian tropical savanna

2017 ◽  
Vol 14 (1) ◽  
pp. 111-129 ◽  
Author(s):  
Caitlin E. Moore ◽  
Jason Beringer ◽  
Bradley Evans ◽  
Lindsay B. Hutley ◽  
Nigel J. Tapper
Keyword(s):  
Primary Productivity ◽  
Vegetation Index ◽  
Temporal Dynamics ◽  
Seasonal Pattern ◽  
Gross Primary Productivity ◽  
Perennial Grasses ◽  
Light Use Efficiency ◽  
Tropical Savanna ◽  
Flux Tower ◽  
Use Efficiency

Abstract. The coexistence of trees and grasses in savanna ecosystems results in marked phenological dynamics that vary spatially and temporally with climate. Australian savannas comprise a complex variety of life forms and phenologies, from evergreen trees to annual/perennial grasses, producing a boom–bust seasonal pattern of productivity that follows the wet–dry seasonal rainfall cycle. As the climate changes into the 21st century, modification to rainfall and temperature regimes in savannas is highly likely. There is a need to link phenology cycles of different species with productivity to understand how the tree–grass relationship may shift in response to climate change. This study investigated the relationship between productivity and phenology for trees and grasses in an Australian tropical savanna. Productivity, estimated from overstory (tree) and understory (grass) eddy covariance flux tower estimates of gross primary productivity (GPP), was compared against 2 years of repeat time-lapse digital photography (phenocams). We explored the phenology–productivity relationship at the ecosystem scale using Moderate Resolution Imaging Spectroradiometer (MODIS) vegetation indices and flux tower GPP. These data were obtained from the Howard Springs OzFlux/Fluxnet site (AU-How) in northern Australia. Two greenness indices were calculated from the phenocam images: the green chromatic coordinate (GCC) and excess green index (ExG). These indices captured the temporal dynamics of the understory (grass) and overstory (trees) phenology and were correlated well with tower GPP for understory (r2 =  0.65 to 0.72) but less so for the overstory (r2 =  0.14 to 0.23). The MODIS enhanced vegetation index (EVI) correlated well with GPP at the ecosystem scale (r2 =  0.70). Lastly, we used GCC and EVI to parameterise a light use efficiency (LUE) model and found it to improve the estimates of GPP for the overstory, understory and ecosystem. We conclude that phenology is an important parameter to consider in estimating GPP from LUE models in savannas and that phenocams can provide important insights into the phenological variability of trees and grasses.

scholarly journals Measured and modeled interactive effects of potassium deficiency and water deficit on gross primary productivity and light‐use efficiency in E ucalyptus grandis plantations

2015 ◽  
Vol 21 (5) ◽  
pp. 2022-2039 ◽  
Author(s):  
Mathias Christina ◽  
Guerric Le Maire ◽  
Patricia Battie‐Laclau ◽  
Yann Nouvellon ◽  
Jean‐Pierre Bouillet ◽  
...  
Keyword(s):  
Water Deficit ◽  
Primary Productivity ◽  
Potassium Deficiency ◽  
Gross Primary Productivity ◽  
Interactive Effects ◽  
Light Use Efficiency ◽  
Use Efficiency

scholarly journals LINKING PHOTOSYNTHETIC LIGHT USE EFFICIENCY AND OPTICAL VEGETATION ACTIVE INDICATORS: IMPLICATIONS FOR GROSS PRIMARY PRODUCTION ESTIMATION BY REMOTE SENSING

2020 ◽  
Vol V-3-2020 ◽  
pp. 571-578
Author(s):  
S. Wang ◽  
Z. Li ◽  
Y. Zhang ◽  
D. Yang ◽  
C. Ni
Keyword(s):  
Remote Sensing ◽  
Primary Productivity ◽  
Seasonal Changes ◽  
Gross Primary Production ◽  
Vegetation Indices ◽  
Canopy Structure ◽  
Remote Sensing Data ◽  
Growing Season ◽  
Light Use Efficiency ◽  
Use Efficiency

Abstract. Over the last 40 years, the light use efficiency (LUE) model has become a popular approach for gross primary productivity (GPP) estimation in the carbon and remote sensing communities. Despite the fact that the LUE model provides a simple but effective way to approximate GPP at ecosystem to global scales from remote sensing data, when implemented in real GPP modelling, however, the practical form of the model can vary. By reviewing different forms of LUE model and their performances at ecosystem to global scales, we conclude that the relationships between LUE and optical vegetation active indicators (OVAIs, including vegetation indices and sun-induced chlorophyll fluorescence-based products) across time and space are key for understanding and applying the LUE model. In this work, the relationships between LUE and OVAIs are investigated at flux-tower scale, using both remotely sensed and simulated datasets. We find that i) LUE-OVAI relationships during the season are highly site-dependent, which is complexed by seasonal changes of leaf pigment concentration, canopy structure, radiation and Vcmax; ii) LUE tends to converge during peak growing season, which enables applying pure OVAI-based LUE models without specifically parameterizing LUE and iii) Chlorophyll-sensitive OVAIs, especially machine-learning-based SIF-like signal, exhibits a potential to represent spatial variability of LUE during the peak growing season.We also show the power of time-series model simulations to improve the understanding of LUE-OVAI relationships at seasonal scale.

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