Inconsistencies of interannual variability and trends in long-term satellite leaf area index products

2017 ◽  
Vol 23 (10) ◽  
pp. 4133-4146 ◽  
Author(s):  
Chongya Jiang ◽  
Youngryel Ryu ◽  
Hongliang Fang ◽  
Ranga Myneni ◽  
Martin Claverie ◽  
...  
Keyword(s):  
Leaf Area Index ◽  
Interannual Variability ◽  
Leaf Area ◽  
Area Index

Long term and seasonal courses of leaf area index in a semi-arid forest plantation

2011 ◽  
Vol 151 (5) ◽  
pp. 565-574 ◽  
Author(s):  
Michael Sprintsin ◽  
S. Cohen ◽  
K. Maseyk ◽  
E. Rotenberg ◽  
J. Grünzweig ◽  
...  
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Forest Plantation ◽  
Area Index ◽  
Semi Arid

scholarly journals EVALUATION OF SIMPLE HAND-HELD MECHANICAL SYSTEMS FOR HARVESTING TEA (CAMELLIA SINENSIS)

2006 ◽  
Vol 42 (2) ◽  
pp. 165-187 ◽  
Author(s):  
P. J. BURGESS ◽  
M. K. V. CARR ◽  
F. C. S. MIZAMBWA ◽  
D. J. NIXON ◽  
J. LUGUSI ◽  
...  
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Camellia Sinensis ◽  
Step Height ◽  
Area Index ◽  
Yield Increase ◽  
Coarse Material ◽  
Terminal Bud ◽  
The Mean

Over an eight-year period, harvesting methods based on simple mechanical aids (blade and shear) were evaluated against hand harvesting on mature morphologically contrasting tea clones in Southern Tanzania. The effects of shear step height (5–32 mm) and the harvest interval (1.8–4.2 phyllochrons) were also examined. Except in the year following pruning, large annual yields (5.7–7.9 t dry tea ha−1) were obtained by hand harvesting at intervals of two phyllochrons. For clones K35 (large shoots) and T207 (small shoots), the mean harvested shoot weights were equivalent to three unfurled leaves and a terminal bud. The proportions of broken shoots (40–48 %) and coarse material (4–6 %) were both relatively high. Using a blade resulted in similar yields to hand harvesting from K35 but larger yields from T207 (+13 %). The yield increase from clone T207 was associated with the harvest of more shoots and heavier shoots, smaller increases in canopy height, and a higher proportion (7–9 %) of coarse material compared to hand harvesting. On bushes, which had been harvested by hand for two years following pruning, using flat shears (no step) supported on the tea canopy resulted, over a three year period, in yields 8–14 % less than those obtained by hand harvesting and, for clone K35, a reduction in the leaf area index to below 5. The development of a larger leaf area index is made possible by adding a step to the shear. However, since annual yields were reduced by 40–50 kg ha−1 per mm increase in step height, the step should be the minimum necessary to maintain long-term bush productivity. As mean shoot weights following shear harvesting were about 13 % below those obtained by hand harvesting, there is scope, when using shears, to extend the harvest interval from 2 to 2.5 phyllochrons.

Daily leaf area index from photosynthetically active radiation for long term records of canopy structure and leaf phenology

2021 ◽  
Vol 304-305 ◽  
pp. 108407
Author(s):  
Cheryl Rogers ◽  
Jing M. Chen ◽  
Holly Croft ◽  
Alemu Gonsamo ◽  
Xiangzhong Luo ◽  
...  
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Photosynthetically Active Radiation ◽  
Canopy Structure ◽  
Leaf Phenology ◽  
Area Index ◽  
Active Radiation

scholarly journals Regional modeling of vegetation and long term runoff for Mesoamerica

2010 ◽  
Vol 7 (1) ◽  
pp. 801-846
Author(s):  
P. Imbach ◽  
L. Molina ◽  
B. Locatelli ◽  
O. Roupsard ◽  
P. Ciais ◽  
...  
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Annual Runoff ◽  
Annual Rainfall ◽  
Average Model ◽  
Area Index ◽  
Potential Vegetation ◽  
Monthly Runoff ◽  
Terra Modis

Abstract. Regional runoff, evapotranspiration, leaf area index (LAI) and potential vegetation were modeled for Mesoamerica using the SVAT model MAPSS. We calibrated and validated the model after building a comprehensive database of regional runoff, climate, soils and LAI. The performance of several gridded precipitation forcings (CRU, FCLIM, WorldClim, TRMM, WindPPT and TCMF) was evaluated and FCLIM produced the most realistic runoff. Annual runoff was successfully predicted (R2=0.84) for a set of 138 catchments with a regression slope of 0.88 and an intercept close to zero. This low runoff bias might originate from MAPSS assumption of potential vegetation cover and to underestimation of the precipitation over cloud forests. The residues were found to be larger in small catchments but to remain homogeneous across elevation, precipitation and land use gradients. Based on the assumption of uniform distribution of parameters around literature values, and using a Monte Carlo-type approach, we estimated an average model uncertainty of 42% of the annual runoff. The MAPSS model was found to be most sensitive to the parameterization of stomatal conductance. Monthly runoff seasonality was fairly mimicked (Kendal tau correlation coefficient higher than 0.5) in 78% of the catchments. Predicted LAI was consistent with EOS-TERRA-MODIS collection 5 and ATSR-VEGETATION-GLOBCARBON remotely sensed global products. The simulated evapotranspiration:runoff ratio increased exponentially for low precipitation areas, stressing the importance of accurately modeling evapotranspiration below 1500 mm of annual rainfall with the help of SVAT models such as MAPSS. We propose the first high resolution (1 km2 pixel) maps combining runoff, evapotranspiration, leaf area index and potential vegetation types for Mesoamerica.

scholarly journals Long-Term Variation of Global GEOV2 and MODIS Leaf Area Index (LAI) and Their Uncertainties: An Insight into the Product Stabilities

2021 ◽  
Vol 2021 ◽  
pp. 1-17
Author(s):  
Hongliang Fang ◽  
Yao Wang ◽  
Yinghui Zhang ◽  
Sijia Li
Keyword(s):  
Leaf Area Index ◽  
Product Quality ◽  
Leaf Area ◽  
Vegetation Change ◽  
Temporal Variations ◽  
Area Index ◽  
Moderate Resolution ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Global Vegetation

Leaf area index (LAI) is an essential climate variable that is crucial to understand the global vegetation change. Long-term satellite LAI products have been applied in many global vegetation change studies. However, these LAI products contain various uncertainties that are not been fully considered in current studies. The objective of this study is to explore the uncertainties in the global LAI products and the uncertainty variations. Two global LAI datasets—the European Geoland2 Version 2 (GEOV2) and Moderate Resolution Imaging Spectroradiometer (MODIS) (2003-2019)—were investigated. The qualitative quality flags (QQFs) and quantitative quality indicators (QQIs) embedded in the product quality layers were analyzed to identify the temporal anomalies in the quality profile. The results show that the global GEOV2 (0.042/10a) and MODIS (0.034/10a) LAI values have steadly increased from 2003 to 2019. The global LAI uncertainty (0.016/10a) and relative uncertainty (0.3%/10a) from GEOV2 have also increased gradually, especially during the growing season from April to October. The uncertainty increase is larger for woody biomes than for herbaceous types. Contrastingly, the MODIS LAI product uncertainty remained stable over the study period. The uncertainty increase indicated by GEOV2 is partly attributed to the sensor shift in the product series. Further algorithm enhancement is necessary to improve the cross-sensor performance. This study highlights the importance of studying the LAI uncertainty and the uncertainty variation. Temporal variations in the LAI products and the product quality revealed herein have significant implications on global vegetation change studies.

scholarly journals Effect of year-to-year variability of leaf area index on variable infiltration capacity model performance and simulation of streamflow during drought

2014 ◽  
Vol 11 (9) ◽  
pp. 10515-10552 ◽  
Author(s):  
Z. K. Tesemma ◽  
Y. Wei ◽  
M. C. Peel ◽  
A. W. Western
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Model Performance ◽  
Hydrologic Model ◽  
Variable Infiltration Capacity ◽  
Infiltration Capacity ◽  
Area Index ◽  
Vic Model ◽  
Monthly Streamflow

Abstract. This study assessed the effect of using observed monthly leaf area index (LAI) on hydrologic model performance and the simulation of streamflow during drought using the variable infiltration capacity (VIC) hydrological model in the Goulburn–Broken catchment of Australia, which has heterogeneous vegetation, soil and climate zones. VIC was calibrated with both observed monthly LAI and long-term mean monthly LAI, which were derived from the Global Land Surface Satellite (GLASS) observed monthly LAI dataset covering the period from 1982 to 2012. The model performance under wet and dry climates for the two different LAI inputs was assessed using three criteria, the classical Nash–Sutcliffe efficiency, the logarithm transformed flow Nash–Sutcliffe efficiency and the percentage bias. Finally, the percentage deviation of the simulated monthly streamflow using the observed monthly LAI from simulated streamflow using long-term mean monthly LAI was computed. The VIC model predicted monthly streamflow in the selected sub-catchments with model efficiencies ranging from 61.5 to 95.9% during calibration (1982–1997) and 59 to 92.4% during validation (1998–2012). Our results suggest systematic improvements from 4 to 25% in the Nash–Sutcliffe efficiency in pasture dominated catchments when the VIC model was calibrated with the observed monthly LAI instead of the long-term mean monthly LAI. There was limited systematic improvement in tree dominated catchments. The results also suggest that the model overestimation or underestimation of streamflow during wet and dry periods can be reduced to some extent by including the year-to-year variability of LAI in the model, thus reflecting the responses of vegetation to fluctuations in climate and other factors. Hence, the year-to-year variability in LAI should not be neglected; rather it should be included in model calibration as well as simulation of monthly water balance.

scholarly journals Quality-assured long-term satellite-based leaf area index product

2017 ◽  
Vol 23 (12) ◽  
pp. 5027-5028 ◽  
Author(s):  
Jian Peng ◽  
Simon Blessing ◽  
Ralf Giering ◽  
Benjamin Müller ◽  
Nadine Gobron ◽  
...  
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Area Index
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