Spatio-temporal variations of photosynthesis: the potential of optical remote sensing to better understand and scale light use efficiency and stresses of plant ecosystems

2008 ◽  
Vol 9 (6) ◽  
pp. 355-366 ◽  
Author(s):  
Uwe Rascher ◽  
Roland Pieruschka
Keyword(s):  
Remote Sensing ◽  
Temporal Variations ◽  
Optical Remote Sensing ◽  
Light Use Efficiency ◽  
Use Efficiency ◽  
Spatio Temporal

Developing a common globally applicable method for optical remote sensing of ecosystem light use efficiency

2019 ◽  
Vol 230 ◽  
pp. 111190 ◽  
Author(s):  
Karl F. Huemmrich ◽  
Petya Campbell ◽  
David Landis ◽  
Elizabeth Middleton
Keyword(s):  
Remote Sensing ◽  
Optical Remote Sensing ◽  
Light Use Efficiency ◽  
Use Efficiency

scholarly journals Temporal Variation of Chlorophyll-a Concentrations in Highly Dynamic Waters from Unattended Sensors and Remote Sensing Observations

Sensors ◽  
2018 ◽  
Vol 18 (8) ◽  
pp. 2699 ◽  
Author(s):  
Jian Li ◽  
Liqiao Tian ◽  
Qingjun Song ◽  
Zhaohua Sun ◽  
Hongjing Yu ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Water Quality ◽  
Chlorophyll Fluorescence ◽  
Chlorophyll A ◽  
Temporal Variations ◽  
Lake Area ◽  
Short Term ◽  
Chl A ◽  
Spatio Temporal

Monitoring of water quality changes in highly dynamic inland lakes is frequently impeded by insufficient spatial and temporal coverage, for both field surveys and remote sensing methods. To track short-term variations of chlorophyll fluorescence and chlorophyll-a concentrations in Poyang Lake, the largest freshwater lake in China, high-frequency, in-situ, measurements were collected from two fixed stations. The K-mean clustering method was also applied to identify clusters with similar spatio-temporal variations, using remote sensing Chl-a data products from the MERIS satellite, taken from 2003 to 2012. Four lake area classes were obtained with distinct spatio-temporal patterns, two of which were selected for in situ measurement. Distinct daily periodic variations were observed, with peaks at approximately 3:00 PM and troughs at night or early morning. Short-term variations of chlorophyll fluorescence and Chl-a levels were revealed, with a maximum intra-diurnal ratio of 5.1 and inter-diurnal ratio of 7.4, respectively. Using geostatistical analysis, the temporal range of chlorophyll fluorescence and corresponding Chl-a variations was determined to be 9.6 h, which indicates that there is a temporal discrepancy between Chl-a variations and the sampling frequency of current satellite missions. An analysis of the optimal sampling strategies demonstrated that the influence of the sampling time on the mean Chl-a concentrations observed was higher than 25%, and the uncertainty of any single Terra/MODIS or Aqua/MODIS observation was approximately 15%. Therefore, sampling twice a day is essential to resolve Chl-a variations with a bias level of 10% or less. The results highlight short-term variations of critical water quality parameters in freshwater, and they help identify specific design requirements for geostationary earth observation missions, so that they can better address the challenges of monitoring complex coastal and inland environments around the world.

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

Remote sensing of tundra gross ecosystem productivity and light use efficiency under varying temperature and moisture conditions

2010 ◽  
Vol 114 (3) ◽  
pp. 481-489 ◽  
Author(s):  
K.F Huemmrich ◽  
J.A. Gamon ◽  
C.E. Tweedie ◽  
S.F. Oberbauer ◽  
G. Kinoshita ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Light Use Efficiency ◽  
Ecosystem Productivity ◽  
Gross Ecosystem Productivity ◽  
Use Efficiency ◽  
Moisture Conditions

Remote sensing of photosynthetic-light-use efficiency of a Siberian boreal forest

Tellus B ◽  
2002 ◽  
Vol 54 (5) ◽  
pp. 677-687 ◽  
Author(s):  
CAROLINE J. NICHOL ◽  
JON LLOYD ◽  
OLGA SHIBISTOVA ◽  
ALMUT ARNETH ◽  
CAROLA ROSER ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Boreal Forest ◽  
Light Use Efficiency ◽  
Use Efficiency

Spatio-temporal variations in water use efficiency and its drivers in China over the last three decades

2018 ◽  
Vol 94 ◽  
pp. 292-304 ◽  
Author(s):  
Shaobo Sun ◽  
Zhaoliang Song ◽  
Xiuchen Wu ◽  
Tiejun Wang ◽  
Yuntao Wu ◽  
...  
Keyword(s):  
Water Use Efficiency ◽  
Water Use ◽  
Temporal Variations ◽  
Use Efficiency ◽  
Spatio Temporal

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.

scholarly journals SPATIO-TEMPORAL DYNAMIC OF LAND DEGRADATION USING REMOTE SENSING-BASED INDEX

AGROFOR ◽  
2021 ◽  
Vol 4 (2) ◽  
Author(s):  
Seyed Hamidreza SADEGHI ◽  
Fahimeh MIRCHOOLI ◽  
Abdulvahed KHALEDI DARVISHAN
Keyword(s):  
Remote Sensing ◽  
Land Degradation ◽  
Net Primary Productivity ◽  
Temporal Analysis ◽  
Mean Value ◽  
Annual Rainfall ◽  
Landsat Images ◽  
Policy Makers ◽  
Use Efficiency ◽  
Spatio Temporal

Land degradation is the major issue which affect watershed sustainability and following social, economic and environmental of livelihood people. So, early detection of land degradation is necessary for policy-makers to make appropriate decision. In this way, remote sensing method is a candidate choice for assessments and monitoring. In this study, land degradation was assessed using Rain-Use Efficiency (RUE) in the Shazand Watershed, Iran in 1986, 1998, 2008 and 2016. Thus, annual rainfall was calculated using inverse distance weight (IDW), net primary productivity (NPP) were calculated using Landsat images. The results indicated that RUE had increasing and then decreasing trends which were 10.66, 33.77, 20.03 and 9.47 kg C ha-1 yr-1. The results also illustrate that the mean value of RUE in different land uses varied between the irrigated land and orchard that had the highest value and outcrop dominant areas and bareland had the lowest value of RUE among land use categories. It is also established that spatio-temporal analysis of RUE can provide valuable information about the trend of watershed’s sustainability over years.

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