Juwon Kong

4th Week of Dec

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3rd Week of Dec

Author: Junichi Susaki, Yoshifumi Yasuoka, Koji Kajiwara, Yoshiaki Honda, and Keitarou Hara

Year: 2007

Title: Validation of MODIS Albedo Products of Paddy Fields in Japan

Journal:  IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, VOL. 45, NO. 1, JANUARY 2007

Summary: Comparison of ground-point-based albedo with 1-km resolution albedo data

Comment: Point spread function for aggregating satellite data! STUDY PSF!!!

1st Week of Dec

Author: Amanda Veloso, Stéphane Mermoz, Alexandre Bouvet, Thuy Le Toan, Milena Planells, Jean-François Dejoux, Eric Ceschia

Year: 2017

Title: Understanding the temporal behavior of crops using Sentinel-1 and Sentinel-2-like data for agricultural applications

Journal: Remote Sensing of Environment 199 (2017) 415–426

Summary: Using SAR data, Sentinel-1, and Sentinel-2 to crop monitoring

Comment: SAR data SAR data!!!

5th Week of Nov

Author: Youngryel Ryu, Galam Lee, Soohyun Jeon, Youngkeun Song, Hyungsuk Kimm

Year: 2014

Title: Monitoring multi-layer canopy spring phenology of temperate deciduous and evergreen forests using low-cost spectral sensors

Journal: Remote Sensing of Environment 149 (2014) 227–238

Summary: Near-surface remote sensor (LED sensor) - derived LAI agreed well with the other instruments, indicating that LED-sensors could be used to monitor

Comment: Sky toward sensor in different hight can capture the phenology in multi-layer canopy.

4th Week of Nov

Author: Liang Liang, Mark D. Schwartz

Year: 2009

Title: Landscape phenology: an integrative approach to seasonal vegetation dynamics

Journal: Landscape Ecol (2009) 24:465–472

Summary: The theory and methodology of landscape phenology and landscape phenology index

Comment: Landscape scale phenology.

3rd Week of Nov

Author: Qunming Wang, Peter M. Atkinson

Year: 2018

Title: Spatio-temporal fusion for daily Sentinel-2 images

Journal: Remote Sensing of Environment 

Summary: A three-step method consisting of regression model fitting (RM fitting), spatial
filtering (SF) and residual compensation (RC) is proposed, which is abbreviated as Fit-FC. The Fit-FC method can be performed using only one Sentinel-3–Sentinel-2 pair and is advantageous for cases involving strong temporal changes.

Comment: Regression model for better prediction especially using Sentinel-2 and Sentibel-3

2nd Week of Nov

Author: Xiaoyang Zhang, Jianmin Wang, Feng Gao, Yan Liu, Crystal Schaaf, Mark Friedl, Yunyue Yu,
Senthilnath Jayavelu, Joshua Gray, Lingling Liu, Dong Yan, Geoffrey M. Henebry

Year: 2017 

Title: Exploration of scaling effects on coarse resolution land surface phenology

Journal: Remote Sensing of Environment 190 (2017) 318-330

Summary: the SOS(Start of growing season) detections at coarser resolution are controlled more by the earlier SOS pixels at the finer resolution rather than by the later SOS pixels, and (2) it should be possible to well simulate the coarser SOS value by selecting the timing at 30th percentile SOS at the finer resolution.

Comment: Good question for scale effects on coarse resolution land surface phenology..

1st Week of Nov

Author: Yinghai Ke, Jungho Im, Seonyoung Park, Huili Gong

Year: 2017

Title: Spatiotemporal downscaling approaches for monitoring 8-day 30 m actual evapotranspiration

Journal: ISPRS Journal of Photogrammetry and Remote Sensing 126 (2017) 79–93

Summary: Image fusion can also be used to increase the spatial and temporal resolution of ET data, and it shows a high correlation.

Comment: Looking at the analysis flowchart, the method, and what data it uses will reduce trial and error in other studies.

4th Week of Oct

Author: Guijun Yang, Qihao Weng, Ruiliang Pu, Feng Gao, Chenhong Sun, Hua Li and Chunjiang Zhao

Year: 2016

Title: Evaluation of ASTER-Like Daily Land Surface Temperature by Fusing ASTER and MODIS Data during the HiWATER-MUSOEXE

Journal: Remote Sensing 2016, 8, 75

Summary: There is a high correlation between the observed and predicted values and it is possible to observe one change of the surface temperature.

Comment: It is also a good idea to use image fusion to observe urbanization with daily temperature variation.

3rd Week of Oct

Author: Yen-Ching Chen, Hao-Wei Chiu, Yuan-Fong Su, Yii-Chen Wu, Ke-Sheng Cheng

Year: 2017

Title: Does urbanization increase diurnal land surface temperature variation? Evidence and implications

Journal: Landscape and Urban Planning 157 (2017) 247–258

Summary: As the UI(Urban index) increases, day-change surface temperature increases. Since the change is large when the UI is 0.4 or less, it can be said that the influence is large at the initial stage of urbanization.

Comment: The idea of studying different time-scale subject in the same image is interesting. In other words, the approach of observing changes of urbanization at the same time was good.

2nd Week of Oct

Author: Kai Liu, Hongbo Su, Senior Member, IEEE, Xueke Li, Weimin Wang, Lijun Yang, and Hong Liang

Year: 2016

Title: Quantifying Spatial-Temporal Pattern of Urban Heat Island in Beijing: An Improved Assessment Using Land Surface Temperature (LST) Time Series Observations From LANDSAT, MODIS, and Chinese New Satellite GaoFen-1

Journal: IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING, VOL. 9, NO. 5, MAY 2016

Summary: The image fusion can detect the impact of four landscape metrics on land surface temperature. NO obvious linear relationships were observed between subplot LST and impervious surface. However, the four impervious surface LSMs were correlated well with the temporal dynamics of LST.

Comment: Combine with heat accumulation or land cover and land use!

1st Week of Oct

Author: Qihao Weng, Peng Fu, Feng Gao

Year: 2014

Title: Generating daily land surface temperature at Landsat resolution by fusing Landsat and MODIS data

Journal: Remote Sensing of Environment 145 (2014) 55-67

Summary: The result of proposing data fusion model, SADFAT, suggests that the prediction accuracy yield from 1.25K to 2K. And another merit of SADFAT is to employ LSMA to address the issue of thermal landscape heterogeneity. 

Comment: The limitation will be like other data fusion method, SADFAT did not have the ability to predict LST changes that were not reflected in the MODIS and/or Landsat pixels.

4th Week of Sep

Author: Chunhua Liao, Jinfei Wang

Year: 2016

Title: Evaluation of spatio-temporal data fusion methods for generating NDVI time series in cropland areas

Journal: 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)

Summary: the evaluation of fusion methods(FSDAF, DPM-STVIFM) in NDVI time series.

Comment: The limitation of FSDAF for generating NDVI time series is significant changes during the two dates of the input images.

3rd Week of Sep

Author: Shaohui Chen, Renhua Zhang, Hongbo Su, Jing Tian, Jun Xia

Year: 2008

Title: Introducing object reflectance property and sensor spectral response into empirical mode decomposition based MODIS and TM image fusion

Journal: Canadian Journal of Remote Sensing Vol.34 No.6

Summary: Considering the object reflectance property and sensor spectral response for image fusion

Comment: Check the 'objective assessment using quantitative indexes'

2nd Week of Sep

Author: Jeffrey T. Morisette, Jaime E. Nickeson, Paul Davis, Yujie Wang, Yuhong Tian, Curtis E. Woodcock, Nikolay Shabanov, Matthew Hansen, Warren B. Cohen, Doug R. Oetter, Robert E. Kennedy

Year: 2003

Title: High spatial resolution satellite observations for validation of MODIS land products: IKONOS observations acquired under the NASA Scientific Data Purchase

Journal: Remote Sensing of Environment 88 (2003) 100

Summary: IKONOS is for to bridge between field or ground-based 'point' measurements and the large area represented by a MODIS pixel. Although there is the uncertainty of acquisition timing.

Comment: To write a paper about validation, check the '4.Case study1 & 2' and see the tables and expressions!!

1st Week of Sep

Author:  Yuhong Tian, Curtis E. Woodcock, Yujie Wang,  Jeff L. Privette, Nikolay V. Shabanov, Liming Zhou, Yu Zhang, Wolfgang Buermann, Jiarui Dong, Brita Veikkanen, Tuomas Häme, Kaj Andersson, Mutlu Ozdogan, Yuri Knyazikhin, Ranga B. Myneni

Year: 2002 

Title: Multiscale analysis and validation of the MODIS LAI product

Journal: Remote Sensing of Environment 83 (2002) 414-430

Summary: Consistency between LAI retrievals from 30m ETM+ data and field measurements indicates satisfactory performance of the algorithm. Values of LAI estimated from a spatially heterogeneous scene depend strongly on the spatial resolution of the image scene.

Comment: It is essential to identify the cover type accurately for validation and operational mapping of LAI.

4th Week of Aug

Author:  Jeffrey T. Morisette,  Jeffrey L. Privette,  Christopher O. Justice

Year: 2002

Title: A framework for the validation of MODIS Land products

Journal: Remote sensing of environment 83 (2002) 77-96

Summary: Validation work on the comparison of MODIS products to similar products derived from independent sensors.

Comment: The process of validation can be applied to data fusion validation.

3rd Week of Aug

Author: Watson, A. and J. Lovelock

Year: 1983

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Title: Biological homeostasis of the global environment: the parable of Daisyworld

Journal: Tellus 35b:286-289.

Summary: By simplified model(Daisyworld), the biological feedback system(both positive and negative) regulates the temperature. It shows greater stability with daisies than it does without them.

Comment: Few equations borrowed directly from population ecology theory works clearly in modeling and effective to show the feedback system. The amount of equation does not guarantee the clear process of system.

2nd Week of Aug

Author: Van Bodegom, P. M., J. C. Douma, J. P. M. Witte, J. C. Ordoñez, R. P. Bartholomeus, and R. Aerts

Year: 2012

Title: Going beyond limitations of plant functional types when predicting global ecosystem-atmosphere fluxes: exploring the merits of traits-based approaches

Journal: Global Ecology and Biogeography 21:625-636.

Summary: Traits-based vegetation model can overcome several drawbacks of dynamic global vegetation model that use PFT classification with fixed attributes like direct calculation of trait values and PFT occurrence by strong environmental filtering. (the most important filters are known and include light availability, water availability, nutrient availability and disturbance regime)

For this two particular challenges are exist.

(1) Developing trait-environment relationships with low residual trait variability

(2) Integrating trait modeling, including trait trade-off, in the predictive framework that does justice to plant strategies and vegetation.

Comment: Very attractive approach! The mechanism for trait value selection needs to be well understood and quantitatively correctly incorporated. But lack of a robust theoretical basis for liking vegetation to ecosystem dynamics (energy, water, and CO2 fluxes = ecosystem function). So I need to find papers which are using traits-based model and explaining traits between vegetation and ecosystem.

1st Week of Aug

Author: Reich, P. B., M. B. Walters, and D. S. Ellsworth.

Year: 1997

Title: From tropics to tundra: Global convergence in plant functioning.

Journal: PNAS 94:13730-13734

Summary: Convergent evolution and global generality in plant functioning (potential canrbon gain(photosynthesis) and carbon loss (respiration) increase in similar proportion with dcreasing leaf life-span, increasing leaf nitrogen concentration, and increasing leaf surface area-to-masss ratio.

Comment: For trait-based model, this result could be evidence for we do not need to classify in PFT instead of traits. Global convergence could be optimal point for global 'leaf' modeling. (Be aware of traits like leaf nitrogen, specific leaf area, leaf life-span etc)

4th Week of July

Author: Jackson, R. B., J. T. Randerson, J. G. Canadell, R. G. Anderson, R. Avissar, D. D. Baldocchi, G. B. Bonan, K. Caldeira, N. S. Diffenbaugh, C. B. Field, B. A. Hungate, E. G. Jobbagy, E. G.; Kueppers, L. M.; Nosetto, M. D.; Pataki, D. E.,

Year: 2008.

Title: Protecting climate with forests

Journal: Environmental Research Letters 3 (4):44006.

Summary: Biophysical factors, such as reflectivity(albedo), evaporation, and surface roughness can alter temperatures much more than carbon sequestration does. In the tropics, avoiding deforestation, forest restoration, afforestation provide the greatest value for slowing climate. In the boreal forest, planting forest will help to stabilize global atmospheric CO2 but accelerate climate warming regionally, further speeding the loss of snow and ice cover. The greatest uncertainties lie in the temperate forest. So, adding biophysical effects into frameworks for evaluating carbon sequestration program is a challenge.

Comment: Forest is not always cooling atmosphere and slowing climate change. We MUST aware of biophysics factors in climate policy.

3rd Week of July

Author: Sellers, P.J, R.E. Dickinson, D.A. Randall, A.K.Betts, F.G. Hall, J.A. Berry, G.J. Collatz, A.S. Denning, H.A. Mooney, C.A. Norbre, N.Sate, C.B.Field, A.Henderson-Sellers

Year: 1997

Title: Modeling the exchanges of energy, water, and carbon between continents and the atmosphere.

Journal: Science, 275(5299): 502-509.

Summary: Improvements of sub models called land surface parameterization from simple, unrealistic schemes into credible representations of the global soil-vegetation-atmosphere.(1 to 3rd generation). Future land models can be coupled with comprehensive atmospheric and ocean model to explore different global change scenario.

Comment: Overlooking development of models and a good idea of coupling land-ocean model that will be capable of modeling the bio-modeling the biological and physical responses of the Earth system to global change.

2nd Week of July

Author: Wright, I. J., P. B. Reich, M. Westoby, D. D. Ackerly, Z. Baruch, F. Bongers, J. Cavender-Bares, F. A. Chapin, J. H. C. Cornelissen, M. Diemer, J. Flexas, E. Garnier, P. K. Groom, J. Gulias, K. Hikosaka, B. B. Lamont, T. Lee, W. Lee, C. Lusk, J. J. Midgley, M.-L. Nava, Ü. Niinemets, J. Oleksyn, N. Osada, H. Poorter, P. Poot, L. Prior, V. I. Pyankov, C. Roumet, S. C. Thomas, M. G. Tjoelker, E. J. Veneklaas, and R. VillarC.B.Field, A.Henderson-Sellers

Year: 2004

Title: The worldwide leaf economics spectrum.

Journal: Nature 428:821-827.

Summary: A universal spectrum of leaf economics consisting of key chemical, structural and physiological properties. Six key features that together capture many essentials of leaf economic are leaf mass per area, photosynthetic capacity, Leaf nitrogen, leaf phosphorus, dark respiration, leaf lifespan. Scaling up to whole plants, mass-based expressions of leaf nutrient concentrations are more tightly correlated than area-based expressions to relatives growth rates of seedlings or to absolute height growth rates of young trees. (area- and mass-based traits can be interconverted via LMA. And the influence of climate was, in general, quite modest.

Comment: Global spectrum of leaf traits can be used to further studies.

1st Week of July

Author: Bonan, G. B.

Year: 2008

Title: Forests and climate change: forcings, feedbacks, and the climate benefits of forests.

Journal: Science 320:1444-1449.

Summary: Complex and nonlinear forest-atmosphere interactions can dampen or amplify anthropogenic climate change.(Biophysical effects differ from tropical, boreal to temperate forest) So interdisciplinary science is necessary to identify and understand as yet unexplored feedbacks in the Earth system and the potential of forests to mitigate climate change.

Comment: Good review paper with interesting facts. To improve the model , it is necessary to consider n-cycle, land-use(sociology), fire etc. So think about forest fire algorithm/data from forest research institute.

5th Week of June

Author: Foley, J. A., R. DeFries, G. P. Asner, C. Barford, G. Bonan, S. R. Carpenter, F. S. Chapin, M. T. Coe, G. C. Daily, H. K. Gibbs, J. H. Helkowski, T. Holloway, E. A. Howard, C. J. Kucharik, C. Monfreda, J. A. Patz, I. C. Prentice, N. Ramankutty, and P. K. Snyder.

Year: 2005.

Title: Global consequences of land use.

Journal: Science 309:570-574.

Summary: Land-use has generally been considered a local environment issue, but it is becoming a force of global importance. Land-use activities have effected to food production, freshwater resources, forest resources, regional climate and air quality and infectious disease. The challenge of managing trade-offs between immediate human needs and maintaining the capacity of the biosphere to provide goods and services in long term. Developing and implementing regional lad-use strategies that recognize both short- and long-term needwill require much more cross-disciplinary research on human-dominated ecosystem.

Comment: Summarizing statements and economic examples are quite interesting.

4th Week of June

Author: Kramer, K., I. Leinonen, and D. Loustau.

Year: 2000.

Title: The importance of phenology for the evaluation of the impact of climate change on growth of boreal, temperate and Mediterranean ecosystems, an overview.

Journal: International Journal of Biometeorology 44:67-75.

Summary: Overviewing phenological drive factor of boreal forest(Temperature), temperature-zone forest(Temperature) and Mediterranean conifers forest(Water availability). Factors affect leaf unfolding, frost hardiness, growth. Three phenological models were coupled with FORGRO which contains detail descriptions of light interception, photosynthesis, respiration, and allocation.

Comment: Climate change includes global warming, CO2 concentration, precipitation and unusual climate event like deep frost so it's quite complex than the model.

3rd Week of June

Author: G.H. Krause, E. Weis

Year: 1991

Title: Chlorophyll fluorescence and photosynthesis: The basics

Journal: Annu. Rev. Plant Physiol. Plant Mol. Biol. 42 313–349.

Summary: Chlorophyll fluorescence, various types of nonphotochemical fluorescence quenching and its relation to control of photosynthesis.

Comment: Basic information about chlorophyll fluorescence with its complexity. The astonishing idea for me when I heard it used for measure photosynthesis by remote sensing. I need to study more about it from now on!  Easy to understand in youtube (https://youtu.be/C7oCQtWjNlM)

2nd Week of June

Author: J. Mason Heberling, Jason D.Fridley

Year: 2012

Title: Biogeographic constraints on the world-wide leaf economic spectrum

Journal: Global ecology and biogeography

Summary: Although most studies emphasize the global generality of leaf economics spectrum patterns and global evolutionary convergence un leaf trait relations, leaf allometric relationships across global floras should be expected given a Darwinian perspective of natural selection operating in isolated regions.

Comment: Evaluate differences between global floras as a result of historical influences not only climate, biome and growth form.

1st Week of June

Author: Porcar-Castell A1, Tyystjärvi E2, Atherton J3, van der Tol C4, Flexas J5, Pfündel EE6, Moreno J7, Frankenberg C8, Berry JA9 

Year: 2014

Title: Linking chlorophyll a fluorescence to photosynthesis for remote sensing applications: mechanisms and challenges.

Journal: J. Exp. Bot. (2014) 65 (15): 4065-4095.doi: 10.1093/jxb/eru191

Summary: It describes chlorophyll fluorescence from molecular level to leaf-level, canopy-level, and landscape-level. And comparing the passive solar-induced chlorophyll fluorescence and pulse amplitude-modulated measurements for challenges remain to be solved usually temporal and spacial-scale.(seasonal and landscape variations) 

Comment: Biology for traits of fluorescence and Ecology for seasonal or inter-annual scale fluorescence. Still, we have a lot of challenges to link ChlF and GPP. In this paper, it's easy to understand by figures.

4th Week of May

Author: J. Joinera, Y. Yoshidab, A.P. Vasilkovb, K. Schaeferc, M. Jungd, L. Guantere, Y. Zhange, S. Garrityf, , 1, E.M. Middletona, K.F. Huemmrichg, L. Guh, L. Belelli Marchesinii

Year: 2014

Title: The seasonal cycle of satellite chlorophyll fluorescence observations and its relationship to vegetation phenology and ecosystem-atmosphere carbon exchange

Journal: Remote sensing of environment Vol.152 Pg 375-391

Summary: Satellite fluorescence captures seasonality of gross primary productivity (validate with Flux tower, MPI-BGC and MODIS APAR). And it may help improve modeling of carbon uptake period(=timing indicator). + Better indicator of photosynthesis than vegetation indices(NDVI, EVI, LAI..)

Comment: ChlF shows seasonality but still need to figure out the relationship  between LUE and ChlF for an early decline in autumn.

3rd Week of May

Author: Luis Guantera, Yongguang Zhang, Martin Jung , Joanna Joiner , Maximilian Voigt , Joseph A. Berry , Christian Frankenberg , Alfredo R. Huete, Pablo Zarco-Tejada , Jung-Eun Lee, M. Susan Moran , Guillermo Ponce-Campos , Christian Beer , Gustavo Camps-Valls , Nina Buchmann , Damiano Gianelle, Katja Klumpp , Alessandro Cescatti , John M. Baker , and Timothy J. Griffis

Year: 2014

Title: Global and time-resolved monitoring of crop photosynthesis with chlorophyll fluorescence

Journal: PNAS vol.111 no.14 

Summary: Correlations between the tower and  SIF/models(data-driven, process-based) showed GPP were underestimated in highly productive crop sites. So using SIF to estimate GPP without additional information is effective but it is difficult to account for heterogeneous land cover given the coarse resolution of current SIF retrieval. Still, the possibility of SIF-based estimates of crop photosynthesis is large to become a unique data set for both an unbiased monitoring of agricultural productivity and the benchmarking of carbon cycle models.

Comment: Reading LUE papers, I will find adequate the fraction of SIF photons escaping from the canopy tp space in different plant type and the different region. May be modules for the model!! I mean the fraction model for global GPP model!

2nd Week of May

Author: Menzel, A., T. H. Sparks, N. Estrella, E. Koch, A. Aasa, R. Ahas, K. Alm-KÜBler, P. Bissolli, O. G. BraslavskÁ, A. Briede, F. M. Chmielewski, Z. Crepinsek, Y. Curnel, Å. Dahl, C. Defila, A. Donnelly, Y. Filella, K. Jatczak, F. MÅGe, A. Mestre, Ø. Nordli, J. PeÑUelas, P. Pirinen, V. RemiŠOvÁ, H. Scheifinger, M. Striz, A. Susnik, A. J. H. Van Vliet, F.-E. Wielgolaski, S. Zach, and A. N. A. Zust.

Year: 2006

Title: European phenological response to climate change matches the warming pattern

Journal: Global Change Biology 12:1969-1976.

Summary: Suspicion of being biased reporting climate change-induced impact on single-site or single-species studies. This paper concludes that previously published results of phenological changes were not biased.(Advanced in spring, summer phases but obscure in leaf coloring)

Comment: Data analyzed in NDVI I need to do it in ChlF and trace the changes in leaf-coloring)

1st Week of May

Author: Juang, J.-Y., G. Katul, M. Siqueira, P. Stoy, and K. Novick.

Year: 2007

Title: Separating the effects of albedo from eco-physiological changes on surface temperature along a successional chronosequence in the southeastern United States.

Journal: Geophysical Research Letters 34.

Summary: In southeastern US, surface temperature effected by major two factors: albedo and eco-physiology(aerodynamics). By eco-physiology / aerodynamics, converting grass-covered old field to planted pine forest or hardwood forest results in a surface cooling effect on annual time scale although albedo decreased(=Temperature increased)

Comment: How does it change in 'heterogenous forest'(mixed forest) surface temperature by albedo and eco-physiology?

4th Week of Apr

Author: Juang, J. Y., G. G. Katul, A. Porporato, P. C. Stoy, M. S. Siqueira, M. Detto, H. S. Kim, and R. Oren

Year: 2007

Title: Eco-hydrological controls on summertime convective rainfall triggers.

Journal: Global Change Biology 13:887-896.

Summary: A simple semianalytical model that links the soil moisture state(=heat flux) has remarkable skills in predicting the timing of convective rainfall. In conclusion, land-use change may have to change a regional convective rainfall.

Comment: Shortwave radiation, albedo, soil moisture are coupled to be converted in heat flux cycle.

3rd Week of Apr

Author: McNaughton, K.G. and Spriggs, T.W.

Year: 1986 

Title: A Mixed-Layer Model for Regional Evaporation.

Journal: Boundary-Layer Meteorology, 34(3): 243-262

Summary: Simplified 'a convective planetary boundary layer' model which is sounder physical basis model show good agreement for regional evaporation. (For this it's necessary to simplify entrainment.)

Comment: An extremely accurate entrainment sub-model is not the necessary component of a useful regional evaporation model. But good to know the equations of this paper.

2nd Week of Apr

Author: Zhixiang Lu, Songbing Zou. Zuodong Qin, Yonggang Yang, Honglang Xiao, Yongping Wei, Kai Zhang, and Jiali Xie

Year: 2015

Title: Hydrologic Responses to Land Use Change in the Loess Plateau: Case Study in the Upper Fenhe River Watershed

Journal: Advances in Meteorology, Volume 2015, Article ID 676030, 10 pages, http://dx.doi.org/10.1155/2015/676030

Summary: By linking a hydrological model to remote sensing image analysis, the approach of quantifying the impacts of LULC changes on hydrology at different scales provide quantitative information for stakeholders in making decisions for land and water resource management. For that investigation, the paper use SWAT model on hydrology at different scales in the Loess Plateau of China. Model calibration and validation had been done with input variables for SWAT model like soil data, climate data, DEM, LULC data

Comment: Think for cumulative effects!!

1st Week of Apr

Author: Yulong Guo, Yunmei Li, Li Zhu, Ge Liu, Shuai Wang and Chenggong Du

Year: 2015

Title: An Improved Unmixing-Based Fusion Method: Potential Application to Remote

Monitoring of Inland Waters

Journal: Remote Sens. 2015, 7, 1640-1666; doi:10.3390/rs70201640

Summary: an IUBF algorithm based on the UBF algorithm. First, a proper HJ1-CCD image
band for each MERIS band is selected. Then, an unsupervised classification method is applied to each sliding window. Finally, the spatial interpolation method is introduced into the new algorithm to obtain a more reasonable fusion result.

Comment: Global waters classification case 2 (whose optical properties are significantly influenced by other constituents such as mineral particles, CDOM, or microbubbles is always changeable, its optical properties are more complicated than those of ocean water​)

1st Week of Apr

Author: Yong Xu, Bo Huang, Yuyue Xu, Kai Cao, Chunlan Guo, and Deyu Meng

Year: 2015

Title: Spatial and Temporal Image Fusion via Regularized Spatial Unmixing

Journal: IEEE GEOSCIENCE AND REMOTE SENSING LETTERS, VOL. 12, NO. 6, JUNE 2015

Summary: The method requires the optimization of the following three parameters: the number of classes of Landsat data, the neighborhood size of the MODIS data for spatial unmixing, and a regularization parameter added to the cost function to reduce unmixing error. Indexes of relative dimensionless global error in synthesis (ERGAS) were used to determine the best combination of the three parameters by evaluating the quality of the fused result at both Landsat and MODIS spatial resolutions

Comment: ERGAS / unmixing-based method!! unsupervised clustering is needed!!

1st Week of Apr

Author: Jerome Chave*

Year: 2013

Title: The problem of pattern and scale in ecology: what have we learned in 20 years?

Journal: Ecology Letters, (2013) 16: 4–16

Summary: Conceptual unification across ecology, genetics, evolution and physiology has fostered even more fertile questions. The maintenance of ecosystem functions depends on shifts in species assemblages and on cellular metabolism, not only on flows of energy and matter.

Comment: A dynamic and individual-based view of ecological networks!! The problem of scale could be also addressed in the abstracted multidimensional space of an interaction networks.

1st Week of Apr

Author: Zhuosen Wang, Crystal B. Schaaf, Qingsong Sun, JiHyun Kim, Angela M. Erb, Feng Gao, Miguel O. Román, Yun Yang, Shelley Petroy, Jeffrey R. Taylor, Jeffrey G. Masek, Jeffrey T. Morisette, Xiaoyang Zhang, Shirley A. Papuga

Year: 2017

Title: Monitoring land surface albedo and vegetation dynamics using high spatial and  temporal resolution synthetic time series from Landsat and the MODIS BRDF/NBAR/albedo product

Journal: International Journal of Applied Earth Observation and Geoinformation 59 (2017) 104–117

Summary: Fused image and tower observation to validate albedo!

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Comment: How they design the algorithm to compare satellite and tower!

5th Week of Mar

Author: Govindjee, W. J. S.  Downton, D. C. Fork, P. A. Armond,

Year: 1981

Title: CHLOROPHYLL A FLUORESCENCE TRANSIENT AS AN INDICATOR OF WATER POTENTIAL OF

LEAVES

Journal:  Plant Science Letters 20(3): 191-194.

Summary: The P/O decreases as the water potential is decreased. Thus, the ratio of the maximum (P level) to the minimum (O level) chlorophyll a fluorescence serves as a qualitative indicator of the leaf water potential. (Water stress blocks electron flow in the water side of PSII.)

Comment: Chlorophyll a fluorescence not only photosynthesis indicator(carbon cycle) but also the water potential indicator(water cycle)

4th Week of Mar

Author: Anthony Brusa, Daniel E. Bunker

Year: 2014

Title: Increasing the precision of canopy closure estimates from hemispherical photography: Blue channel analysis and under-exposure

Journal: Agricultural and Forest Meteorology 195–196 (2014) 102–107

Summary: The sky has a high transmittance of blue for the majority of the day, while most plants have pigments that strongly absorb both blue and red light. So estimating canopy closure from the hemispherical image is reasonable.

Comment: taking from understory at the clear sky is much easier than satellite image which is taken from above. The background color is not always same.

3rd Week of Mar

Author: M. Rossini, L. Nedbal, L. Guanter, A. Ač, L. Alonso, A. Burkart, S. Cogliati, R. Colombo, A. Damm, M. Drusch, J. Hanus, R. Janoutova, T. Julitta, P. Kokkalis, J. Moreno, J. Novotny, C. Panigada, F. Pinto, A. Schickling, D. Schüttemeyer, F. Zemek, and U. Rascher

Year: 2015 

Title: Red and far-red Sun-induced chlorophyll fluorescence as a measure of plant photosynthesis

Journal: Geophysical Research Letter, 42, 1632–1639, doi:10.1002/2014GL062943.

Summary: An airborne instrument with specifications and performance comparable to the actual FLEX high-resolution spectrometer was flown over two grass carpets, one treated with a herbicide known to inhibit photosynthesis and selectively intensify fluorescence emission. A significant increase of both red and far-red fluorescence was detected on the treated grass carpet from the airborne platform, while the reflectance signals of the control and treated grass were indistinguishable.

Comment: To understand photosynthesis, using estimated sun-induced chlorophyll fluorescence is linked to actual photosynthetic efficiency. By comparing with controlled grass carpet and herbicide-treated grass carpet, experimental proof enables to use remote sensing data for estimation of photosynthesis.

3rd Week of Mar

Author: Chuixiang Yi, Elise Pendall and Philippe Ciais

Year: 2015

Title: Focus on extreme events and the carbon cycle

Journal: Environmental Research Letter 10 (2015) 070201

Summary: The extreme events can disrupt terrestrial carbon dynamics dramatically
by triggering ecological disturbances and potentially forcing climate–carbon feedbacks.

​

Comment: Extreme weather(flood, drought, violent wind etc) by carbon cycle balance.

2nd Week of Mar

Author: Dengfeng Xie, Jinshui Zhang, Xiufang Zhu, Yaozhong Pan, Hongli Liu, Zhoumiqi Yuan and Ya Yun

Year: 2016

Title: An Improved STARFM with Help of anUnmixing-Based Method to Generate High

Spatial and Temporal Resolution Remote Sensing Data in Complex Heterogeneous Regions

Journal: Sensors 2016, 16, 207

Summary: USTARFM is better than STARFM for heterogeneous!

Comment: Fusing methods for image fusion !!!

2nd Week of Mar

Author: Boris Zhukov, Dieter Oertel, Franz Lanzl, and G¨otz Reinh¨ackel

Year: 1999

Title: Unmixing-Based Multisensor Multiresolution Image Fusion

Journal: IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, VOL. 37, NO. 3, MAY 1999

Summary: The constrained unmixing preserves all the available radiometric information of the low-resolution image.

Comment: Unmixing-based image fusion with energy balance requirements.

1st Week of Mar

Author: Feng Gao, William P. Kustas and Martha C. Anderson

Year: 2012

​

Title: A Data Mining Approach for Sharpening Thermal Satellite Imagery over Land

​

Journal: Remote Sens. 2012, 4, 3287-3319; doi:10.3390/rs4113287

​

Summary: In thermal band image sharpening methods, data mining sharpening(DMS) outperformed than the methods relying on a biophysical functional relationship between temperature and reflectance.

​

Comment: Data mining for shortwave reflectances!!! Good idea that can be used for image fusion.

5th Week of Feb

Author: MICHAEL A. LEFSKY, WARREN B. COHEN, GEOFFREY G. PARKER, AND DAVID J. HARDING

Year: 2002

Title: Lidar Remote Sensing for Ecosystem Studies

Journal: BioScience, 52(1):19-30.

Summary: lidar remote sensing shows great potential for integration with ecological research precisely because it directly measures the physical attributes of vegetation canopy structure that are highly correlated with the basic plant community measurements of interest to ecologists.

Comment: Lidar!! canopy volume method!

5th Week of Feb

Author: Kim Knauer, Ursula Gessner, Rasmus Fensholt and Claudia Kuenzer

Year: 2016

Title: An ESTARFM Fusion Framework for the Generation of Large-Scale Time Series in Cloud-Prone and Heterogeneous Landscapes

Journal: Remote Sensing, vol 8, 2016

Summary: A framework for ESTARFM fusion algorithm was developed to make it applicable for large, cloud-prone and heterogeneous areas. Furthermore, an automatic filling of cloud gaps was developed to maximize the use of available cloud-free Landsat input data.

Comment:

1. Parallelization fo the algorithm for ESTAFM automated and processing.

2. An automatic cloud gap filling of Landsat scenes in the ESTARFM framework to make the best use of scenes with a partial cloud cover without producing gaps in the output of the fused time series.

(The importance of using a shoulder scene from the same phenological stage if possible.)

3. The datasets contain surface reflectances atmospherically corrected with the L8SR algorithm and complemented by cloud and cloud shadow masks based on CFmask, an open source C code version of the Fmask algorithm.

(The masks were applied to the reflectance data prior to the calculation of NDVI.)

5th Week of Feb

Author: Yonghua Qu, Wenchao Han, Lizhe Fu, Congrong Li, Jinling Song, Hongmin Zhou, Yanchen Bo, Jindi Wang

Year: 2014

Title: LAINet – A wireless sensor network for coniferous forest leaf area index measurement: Design, algorithm and validation

Journal: Computers and Electronics in Agriculture 108 (2014) 200–208

Summary: The design of the LAINet framework and the algorithm for estimating the LAI from a multi-point directional transmittance. In conclusion, LAINet can be used as a feasible method to conduct automatic ground observations where continuous observation is required.

Comment: This methods and data can be used for validating models or products.

4th Week of Feb

Author: Christopher Hutengs, Michael Vohland

Year: 2016

Title: Downscaling land surface temperatures at regional scales with random forest regression

Journal: Remote Sensing of Environment 178 (2016) 127–141

Summary: A random forest (RF) regression approach to increase the spatial resolution of LST maps from ~1 km  to ~250 m. LST was downscaled based on its relationship to topographic variables, land cover data, and surface reflectances in the visible red and near infrared. (+DEM)

Comment:  Extended random forest regression is useful to predict downscale image! But one has to be aware that the predictive range of LSTs from RF regression is restricted to those covered by the training data. STUDY random forest regression!!!!

4th Week of Feb

Author: Aleixandre Verger, Frédéric Baret, Marie Weiss, Sivasathivel Kandasamy and Eric Vermote

Year: 2013

Title: The CACAO Method for Smoothing, Gap Filling, and Characterizing Seasonal

Anomalies in Satellite Time Series

Journal: IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, VOL. 51, NO. 4, APRIL 2013
 

Summary: A novel climatology fitting approach called CACAO (Consistent Adjustment of the Climatology to Actual Observations) is proposed to reduce noise and fill gaps in time
series by scaling and shifting the seasonal climatological patterns to the actual observations. The shift and scale CACAO parameters adjusted for each season allow quantifying shifts in the timing of seasonal phenology and inter-annual variations in magnitude as compared to the average climatology.

Comment: Using 'RMSE' in some algorithm can make good results. But still, need to be improved for local rapid changes as it uses mean value of LAI.

4th Week of Feb

Author: Changming Zhu, Dengsheng Lu, Daniel Victoria and Luciano Vieira Dutra

Year: 2015

Title: Mapping Fractional Cropland Distribution in Mato Grosso, Brazil Using Time Series

MODIS Enhanced Vegetation Index and Landsat Thematic Mapper Data

Journal: Remote Sensing. 2016, 8, 22

Summary: A new approach to mapping fractional cropland distribution, using time series MODIS enhanced vegetation index(EVI) and Landsat Thematic Mapper(TM) data.

Comment: EVI, SDI for crop phenology analysis and TSR(time sparse resampling) for cloud contamination.

4th Week of Feb

Author: Gaofei Yin, Ainong Li, Huaan Jin, Wei Zhao, Jinhu Biana, Yonghua Qu,Yelu Zeng, Baodong Xu

Year: 2017

Title: Derivation of temporally continuous LAI reference maps through combining the

LAINet observation system with CACAO

Journal: Agricultural and Forest Meteorology 233 (2017) 209–221

Summary: To obtain temporally observation of LAI(NDVI) map, the methods is based on the combination of the wireless sensor network technology(LAINet) and a data blending approach(CACAO) 

Comment: CACAO(data blending method). In comparison to data fusion, what is the difference and advantage?

3rd Week of Feb

Author: Inge Jonckheere, Stefan Fleck, Kris Nackaerts, Bart Muys, Pol Coppin, Marie Weiss, Frédéric Baret

Year: 2004

Title: Review of methods for in situ leaf area index determination Part I. Theories, sensors and hemispherical photography

Journal: Agricultural and Forest Meteorology 121 (2004) 19–35

Summary: Review of direct and indirect method to estimate LAI

Comment: Brief description of methods of estimating LAI

3rd Week of Feb

Author: Zhou Wang, Alan Conrad Bovik, Hamid Rahim Sheikh, and Eero P. Simoncelli

Year: 2004

Title: Image Quality Assessment: From Error Visibility to Structural Similarity

Journal: IEEE TRANSACTIONS ON IMAGE PROCESSING, VOL. 13, NO. 4, APRIL 2004

Summary: Under the assumption that human visual perception is highly adapted for extracting structural information, structural similarity index performs better than the other models. (i.e error-sensitivity assessment is a bottom-up model.) 

Comment: MATLAB image processing toolbox includes the function SSIM. The paper recommended using locally rather than globally. 

3rd Week of Feb

Author: Canran Liu, Paul Frazier, Lalit Kumar

Year: 2007

Title: Comparative assessment of the measures of thematic classification accuracy

Journal: Remote Sensing of Environment 107 (2007) 606–616

Summary: For accuracy assessment of classified imagery, testing accuracy measures by using nonparametric correlation coefficients(Spearman's rho and Kendall's tau-b) as well as the probability of concordance.(14 category-level and 20 map-level accuracy measures)

Comment: Classification could be more important than image fusion or interpretation because of garbage in garbage out! Using the recommended measurements for accuracy assessment of classified imagery

3rd Week of Feb

Author: Junchang Ju, David P. Roy 

Year: 2008

Title: The availability of cloud-free Landsat ETM+ data over the conterminous United States and globally

Journal: Remote Sensing of Environment 112 (2008) 1196–1211

Summary: Probabilistic analyses indicate that over the conterminous U.S., land applications requiring at least one cloud-free observation in a year, a season, two different seasons, or at least two cloud-free observations occurring within any period of the year, are on average largely unaffected by cloud cover, except for certain Winter applications and cloudy scenes near the U.S.–Canada border and the Great Lakes. Globally, only land applications requiring at least one cloud-free observation per year are largely unaffected by cloud cover and the reduced global ETM+ data availability.

Comment: Do not expect more than two cloud-free observation of Landsat ETM+ in any season or in year except U.S.

3rd Week of Feb

Author: Raul Zurita-Milla, Jan G. P. W. Clevers, and Michael E. Schaepman

Year: 2008

Title: Unmixing-based Landsat TM and MERIS FR data fusion

Journal: IEEE geoscience and remote sensing letters, vol5, no3, July 2008

Summary: A detailed version of the unmixing-based fusion algorithm where two parameters, the number of classes used to classify the Thematic Mapper image and the size of the MERIS FR neighborhood used to solve the unmixing equations, need to be optimized. The ERGAS index is used to support the optimization of these two parameters and to quantitatively assess the quality of the fused images.

Comment: Classification and size of 'window' need to be optimized every fusion algorithm/

3rd Week of Feb

Author: X. YANG and C. P. LO

​

Year: 2002

Title: Using a time series of satellite imagery to detect land use and land cover changes in the Atlanta, Georgia metropolitan area

Journal: International journal of remote sensing, 2002, vol 23, no 9 1775-1798

Summary: A suite of techniques that have been used to develop an operational approach, which will ensure high accuracy and compatibility in image classiŽ cation from the satellite images of diVerent resolutions and varying quality.

Comment: Check the image processing procedures that used in this paper from geometric rectification, radiometric normalization(RCS), classification scheme design, image classification- image clustering(ISODATA) and cluster labeling, spatial reclassification- reducing boundary errors and resolving spectral confusion, accuracy assessment.

(The techniques include radiometric normalization to establish a common radiometric response among multi-date/multi-sensor data, an unsupervised image classification approach using image clustering and cluster labelling, a GIS-based image spatial  reclassification procedure to deal with classification errors caused by spectral confusion, and post-classification comparison with GISoverlay to map the spatial dynamics of land use/cover change)

2nd Week of Feb

Author: G.H. Krause, E. Weis

Year: 1991

Title: Chlorophyll fluorescence and photosynthesis: The basics

Journal: Annu. Rev. Plant Physiol. Plant Mol. Biol. 42 313–349.

Summary: Chlorophyll fluorescence, various types of nonphotochemical fluorescence quenching and its relation to control of photosynthesis.

Comment: Basic information about chlorophyll fluorescence with its complex. Astonishing idea for me when I heard it used for measure photosynthesis by remote sensing. I need to study more about it from now on!  Easy to understand in youtube (https://youtu.be/C7oCQtWjNlM)

2nd Week of Feb

Author:  Jing M. Chen

Year: 1996

Title: Optically-based methods for measuring seasonal variation of leaf area index in boreal conifer stands

Journal: Agricultural and Forest Meteorology 80 (1996) 135- 163

Summary: Determining the component affects seasonal variation in LAI and evaluating the feasibility of detecting the seasonal variability using TRAC and LAI-2000.

Comment: It explains the theories well from gap fraction to effective leaf area index, needle-to-shoot area ratio and element clumping index.

2nd Week of Feb

Author: A.R.G. Lang

Year: 1987

Title: Simplified estimate of leaf area index from transmittance of the sun's beam

Journal: Agricultural and Forest Meteorology, 41 (1987) 17~186

Summary: Estimating LAI from the transmittance of the direct sun's beam. Equation L =2(A+B) (a value of the contact number at angle of the sun's beam of 1 radian and the value of the mean projection of leaf area G is 0.5)

Comment: The theoretical assumtion makes easy to estimate LAI which is mathematically proved. 

2nd Week of Feb

Author: Desheng Liu and Xiaolin Zhu

Year: 2012

Title: An enhanced physical method for downscaling thermal infrared radiance

Journal: IEEE GEOSCIENCE AND REMOTE SENSING LETTERS, VOL. 9, NO. 4, JULY 2012

Summary: enhanced PM for downscaling thermal infrared radiance by using the 'pure' coarse pixel for parameter estimating and using LST of fine-pixel for predicting fine-resolution TIR radiances. 

Comment: The need for thermal infrared radiance image fusion!! Based on physics of radiance and atmosphere effect to improve myself.

2nd Week of Feb

Author: Dongjie Fu, Baozhang Chen, Juan Wang, Xiaolin Zhu and Thomas Hilker

Year: 2013

Title: An improved image fusion approach based on enhanced spatial and temporal the adaptive reflectance fusion

model

Journal: Remote Sens. 2013, 5, 6346-6360

Summary: A modified version of ESTARFM by adding the land cover data as an auxiliary for searching for spectrally similar neighbor pixel

Comment: Availablity of land cover data! Think about other classification.

2nd Week of Feb

Author: Huihui Song and Bo Huang

Year: 2013

Title: Spatiotemporal Satellite Image Fusion Through One-Pair Image Learning

Journal: IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, VOL. 51, NO. 4,

Summary: Image fusion through Land-MODIS correspondence stage by sparse representation and linear temporal change model by high-pass modulation.

Comment: For understanding sparse representation, I need to study linear algebra and signal processing. (K-SVD, OMP)

2nd Week of Feb

Author: Xuehong Chen, Wentao Li, Jin Chen, Yuhan Rao and Yasushi Yamaguchi

Year: 2014 

Title: Combination of TsHARP and Thin Plate Spline Interpolation for Spatial Sharpening of Thermal Imagery

Journal: Remote Sens. 2014, 6, 2845-2863

Summary: A combined method of TsHARP and TPS(thin plates line) interpolation was proposed for improving the accuracy and robustness of TsHARP for thermal sharpening.

Comment: Interpolation method and reducing errors by error estimation of the regression method is more important for validation.

1st Week of Feb

Author: Bo Huang and Hankui Zhang

Year: 2014

Title: Spatio-temporal reflectance fusion via unmixing: accounting for both phenological and land-cover changes

Journal: International Journal of Remote Sensing, 2014 Vol. 35, No. 16, 6213–6233

Summary: U-STFM to estimate the reflectance change trend without reference to the change. It is achieved by obtaining the change trend of the Landsat pixels within homogenous change regions(HCRs) that are delineated by segmenting the Landsat reflectance difference image. 

Comment: The most important parameters in U-STFM are the scale parameter used to define the HCRs and the constraint parameter for minimum and maximum solutions.For modeling, it is important to optimize parameter to achieve the goal.

1st Week of Feb

Author: Wilson, J. W.

Year: 1965.

Title: Stand Structure and Light Penetration. I. Analysis by Point Quadrats.

Journal: Journal of Applied Ecology 2:383-390

Summary: The interception of light entering at different inclinations by foliage at different heights in a stand can be studied practically by inclined point quadrats, and can be interpreted by point quadrat theory in terms of foliage 

area per unit of ground, foliage angle, and inclination. (With increasing depth, light penetration is increasingly restricted to higher inclination.) It refer about random disperstion that is not inappropriate to assume in the following general study, in which the relations of p(intercepted) and 1-p to foliage area, leaf inclination, the inclination of quadrat. are examined by means of the mathematical relations. However, it is not present in the actual stand. The expected values follow the same general pattern as the observed values but consistently underestimate them.

Comment: By calculating the proportion of 'gap' in different height above ground, penetration or interception can be calculated automatically (1-p). Be aware of clumping effect!!! When you assume as random dispersion.

1st Week of Feb

Author:  Qihao Weng, Peng Fu, Feng Gao

Year: 2014

Title: Generating daily land surface temperature at Landsat resolution by fusing Landsat and MODIS data

Journal: Remote Sensing of Environment 145 (2014) 55–67

Summary: SADFAT(Spatio-temporal Adaptive Data Fusion Algorithm for Temperature mapping.(Improved and modified STARFM for predicting thermal radiance and LST data). The Parameter is land surface emissivity(LSE) and emissivity data product from the ASTER global emissivity database 3.0

Comment: Study about land surface temperature. 'Jimenez-Munoz and Sobrino(2003)', 'Bechtel, 2012'

1st Week of Feb

Author: Cornelius Senf, Pedro J. Leitão, Dirk Pflugmacher, Sebastian van der Linden, Patrick Hostert

Year: 2015

Title: Mapping land cover in complex Mediterranean landscapes using Landsat: Improved classification accuracies from integrating multi-seasonal and synthetic imagery

Journal: Remote Sensing of Environment 156 (2015) 527–536

Summary: Comparing several classification scenarios based on single-date Landsat imagery, multi-seasonal Landsat imagery, and STARFM-simulated imagery to improve land cover maps of a complex Mediterranean pseudo-steppe landscape. Supplementing single-date Landsat land cover models with STARFM-simulated imagery increases classification accuracy, but only if Landsat temporal sampling is insufficient for capturing the important phenological events of the study area.

Comment: Data fusion only for insufficient temporal sampling in the medium-resolution image. Instead of NDVI, the validation can be done in better way like ground observation.

5th Week of Jan

Author: Yinghao Li, Zhongshi He, Hao Zhu, Weiwei Zhang, Yuhao Wu

Year: 2016

Title: Jointly registering and fusing images from multiple sensors

Journal: Information Fusion 27 (2016) 85–94

Summary: The proposed approach simultaneously models the mapping from the fused image to the source images and the joint intensity of all images with motion parameters at first, and then combines these models into a maximum likelihood function. The relevant parameters are determined through employing an expectation maximization algorithm.

Comment: In most image fusion cases, completely accurate registration cannot be achieved in advance and the registration errors will have a bad influence on the subsequent fusion results. Therefore, the performance of image fusion is decided by both registration and fusion.

4th Week of Jan

Author: Vijay Solanky, S.K. Katiyar 

Year: 2016

Title: Pixel-level image fusion techniques in remote sensing: a review

Journal: Spatial Information Research (2016) 24:475-483

Summary: Analyzing pixel-level image fusion techniques, which integrates a low-resolution muti-spectral (MS) image and high-resolution panchromatic (PAN) to produce a more informative image. It is recommended to use UNB or GS algorithm for fusion IRS images.

Comment: Pre-processing steps need to be taken care! Selection of fusion algorithm should be appropriate, based on the application for which fused image is to be used.

4th Week of Jan

Author: Irina V. Emelyanova, Tim R. McVicar, Thomas G. Van Niel, Ling Tao Li, Albert I.J.M. van Dijk

Year:  2013

Title: Assessing the accuracy of blending Landsat-MODIS surface reflectances in two landscapes with contrasting

spatial and temporal dynamics: A framework for algorithm selection

Journal: Remote Sensing of Environment 133 (2013) 193–209

Summary: Evaluation of algorithms shows that ESTARFM was superior for those spectral bands where/when the spatial variance was dominant: and STARFM was preferable where/when a given spectral band is dominated by temporal variance.

Comment: Method of comparing and evaluating algorithm.

4th Week of Jan

Author: Caroline M. Gevaert, F. Javier García-Haro

Year: 2015

Title: A comparison of STARFM and an unmixing-based algorithm for Landsat and MODIS data fusion

Journal: Remote Sensing of Environment 156 (2015) 34–44

Summary: A comparison and quality assessment of spatial downscaling through the data fusion methods: STARFM and two unmixing-based methods. The two unmixing-based methods rely on a Bayesian approach that optimally incorporates the available prior spectral information to constrain the unmixing process. The first unmixes MODIS imagery directly which maintain the spectral information of the medium-resolution image. The second, STRUM, unmixes the temporal differences in MODIS imagery and then add it back to the Landsat reflectance in a similar manner as the STARFM. The STRUM is expected to have the best performances in most applications and observing temporal dynamics in situations where limited high-resolution images are available.(STARFM may have a higher performance in situations where many high-resolution images are available.

Comment:  Study about unmixing-based methods!!(include Bayesian theory) + STRUM

4th Week of Jan

Author: Feng Gao, Jeffrey G. Masek, Robert E. Wolfe, Chengquan Huang

Year: 2010

Title: Building a consistent medium resolution satellite data set using moderate resolution imaging spectroradiometer products as reference

Journal: Journal of Applied Remote Sensing, Vol. 4, 043526 (26 April 2010)

Summary: MODIS-like data from multiple sensors by non-requiring explicit calibration and atmospheric correction procedures method can be used for time-series analysis, biophysical parameter retrievals, and other downstream analysis. And there are additional advantages like inputs for STARFM and STAARCH(image fusion), a way to standardize surface reflectance from different medium resolution sensors. But the accuracy of the general reference-based approach depends on several factors.

(A generalized reference-based approach, linear correction model + merit function(Press et al 2007)

Comment:  An accurate automatic cloud and shadow detection algorithm for medium resolution data is needed for automatic processing. And aerosol optical thickness(AOT) is a major input to physical approaches for atmospheric correction.

3rd Week of Jan

Author: Menzel, A., T. H. Sparks, N. Estrella, E. Koch, A. Aasa, R. Ahas, K. Alm-KÜBler, P. Bissolli, O. G. BraslavskÁ, A. Briede, F. M. Chmielewski, Z. Crepinsek, Y. Curnel, Å. Dahl, C. Defila, A. Donnelly, Y. Filella, K. Jatczak, F. MÅGe, A. Mestre, Ø. Nordli, J. PeÑUelas, P. Pirinen, V. RemiŠOvÁ, H. Scheifinger, M. Striz, A. Susnik, A. J. H. Van Vliet, F.-E. Wielgolaski, S. Zach, and A. N. A. Zust.

​

Year: 2006

Title: European phenological response to climate change matches the warming pattern

Journal: Global Change Biology 12:1969-1976.

Summary: Suspicion of being biased reporting climate change-induced impact on single-site or single-species studies. This paper conclude that previously published results of phenological changes were not biased.(Advanced in spring, summer phases but obscure in leaf coloring)

Comment: For data analyzed in NDVI didn't catch the change well because of coarse temporal resolution, so I need to do it in ChlF and trace the changes in leaf-coloring)

3rd Week of Jan

Author: Thomas Hilker, Michael A. Wulder, Nicholas C. Coops, Nicole Seitz, Joanne C. White, Feng Gao,
Jeffrey G. Masek, Gordon Stenhouse

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Year: 2009

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Title: Generation of dense time series synthetic Landsat data through data blending with
MODIS using a spatial and temporal adaptive reflectance fusion model

Journal: Remote Sensing of Environment 113 (2009) 1988–1999

Summary: The STARFM algorithm seems less well suited to predicte sudden changes in land cover. And the use of  MODIS composites may reduce the quality of STARFM predictions due to changes in pixel brightness resulting from remaining directional or atmospheric imapcts in the different MODIS images.

​

Comment: This paper is logical claim for STAARCH. And I need to improve cloud mask algorithm. (Every problem comes from clouds in satellite observation.) ('The greater impact of  atmospheric contamination at shorter wavelengths which has been reported to affect the prediction accuracy also for other fusion techniques.')

​

The issue for data fusion.

- Cloud mask algorithm
- Fusion algorithm

- Weight function assumption

- Data acquisition

3rd Week of Jan

Author: Thomas Hilker, Michael A.Wulder, Nicholas C. Coops, Julia Linke, Greg McDermid, Jeffrey G. Masek, Feng Gao, Joanne C. White

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Year: 2009

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Title: A new data fusion model for high spatial- and temporal-resolution mapping of forest disturbance based on Landsat and MODIS

​

Journal: Remote Sensing of Environment 113 (2009) 1613–1627

​

Summary: To detect forest disturbance, the Disturbance Index(Brightness, wetness, NDVI) is used with ACCA(Automated Cloud-Cover Assessment algorithm) and land cover classification provided for EOSD. The result have shown that the MODIS disturbance was able to determine the date of disturbance of area well below the size of a MODIS pixel.

​

Comment: With STARFM(data fusion-high spatial and temporal resolution) and STAARCH(detecting disturbance), fused data can predict well, but algorithm needs to be improved for non-forested environments and more cloud area.

- Modification for automatic cloud cover detection

- Improvement for non-forest area

- Higher temporal resolution than 8-day time steps.

3rd Week of Jan

Author:  Feng Gao, Jeff Masek, Matt Schwaller, Forrest Hall

Year: 2006

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Title:  On the blending of the Landsat and MODIS surface reflectance: Predicting daily Landsat surface reflectance

​

Journal: IEEE Transactions on Geoscience and Remote Sensing, 44 no.8 2207-2218

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Summary: Algorithm of STARFM 

​

Comment: Detail of algorithm which is widely use 'moving window'!! Using thresholds in surface reflectance of red and NIR to dermine the spectrally similar pixels.

3rd Week of Jan

Author: Xiaolin Zhu, Jin Chen, Feng Gao, Xuehong Chen, Jeffrey G. Masek

​

Year: 2010

​

Title: An enhanced spatial and temporal adaptive reflectance fusion model for complex
heterogeneous regions

Journal: Remote Sensing of Environment 114 (2010) 2610–2623

Summary: For accurately predicting the surface reflectance of hetetogeneous landscapes, enhance STARFM uses conversion coefficient ,temporal weight  and all bands for improing prediction and selecting similar pixel.

​

Comment: Be carefull using sensors with different spectral band passes. It need to show linear relationship between sensors. And size of moving window and the number of classes need to be set! Improvement for how long does it assure the prediction? one or two year? The prediction may be available for homogeneous place close distance to made image!

2nd Week of Jan

Author: Kramer, K., I. Leinonen, and D. Loustau.

Year: 2000.

Title: The importance of phenology for the evaluation of impact of climate change on growth of boreal, temperate and Mediterranean ecosystems, an overview.

Journal: International Journal of Biometeorology (2000) 44:67-75.

Summary: Overviewing phenological dirve-factor of boreal foreast(Temperature), temperater-zone foreast(Temperature) and Mediterranean confierous foreast(Water availability). Factors affects leaf unfolding, frost hardiness, growth. Three phenological models were coupled with FORGRO which contains detail descriptions of light interception, photosynthesis, respiration and allocation.

Comment: Climate change includes global warming, CO2 concentration, precipitation and unsual climate event like deep frost so it's quite complex than model. For citation, 'In the boreal zone, a mechanistic description of the seasonal development of frost hardness and the seasonal changes of the photosynthetic capacity is essential to assess the impact of climate change on this region.'

2nd Week of Jan

Author: Kathryn A. Semmens, Martha C. Anderson, William P. Kustas, Feng Gao, Joseph G. Alfieri, Lynn McKee, John H. Prueger, Christopher R. Hain, Carmelo Cammalleri, Yun Yang, Ting Xia, Luis Sanchez, Maria Mar Alsina, Mónica Vélez

​

Year: 2016 

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Title: Monitoring daily evapotranspiration over two California vineyards using Landsat 8 in a multi-sensor data fusion approach

​

Journal: Remote sensing of environment (2016) 185: 155-170

​

Summary: Performance of multi-sensor and scale data fusion approach to mapping ET using GOES,MODIS and Landsat TIR and shortwave imagery was assessed in an application over two vineyard sites in central California using data products from the new Landsat 8 satellite. The ET data fusion methodology can provide detailed information about daily and seasonal water use patterns that may not be easily acquired with other methodologies.

​

Comment: For water management, use pattern and observation are essential! So it can be applied to rice field in Korea. For data, I need to easily handle the data fusion in any satellite image. (+ check out ALEXI model)

2nd Week of Jan

Author: QunmingWang, Wenzhong Shi, Zhongbin Li, Peter M. Atkinson

Year: 2016

Title: Fusion of Sentinel-2 images

​

Journal: Remote sensing of envirionment (2016) 187: 241-252

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Summary: By using image fusion approach(ATPRK), spatial information in the four 10 m band to downscale six 20 m band to the finer spatial resolusion, to provide user with more detailed information in the six band, and create a complete set of 10 band with a fine spatial resolution of 10 m which is evaluated with four indices.(CC, ERGAS, UIQI, SAM)

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Comment: Sentinel-2, 2B, 3 image is next level of image fusion with higher spatial-temporal resolution than Landsat and MODIS. By image fusion Sentinel-2,2B provides 5 day temporal resolution and 10m spatial resolution. Sentinel-3 for fine temporal resolution(<2 days). Keep in mind for next level after 

2nd Week of Jan

Author: Dongjie Fu, BaozhangChen, Huifang Zhang, JuanWang, T.Andy Black, Brian D.Amiro, Gil Bohrer, Paul Bolstad, Richard Coulter, Abdullah F. Rahman, Allison Dunn, J. Harry McCaughey, Tilden Meyers, Shashi Verma

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Year: 2014 

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Title: Estimating landscape net ecosystem exchange at high spatial–temporal resolution based on Landsat data, an improved upscaling model framework, and eddy covariance flux measurements

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Journal: Remote sensing of environment 141 (2014) 90-104

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Summary: A modeling approach for landscape NEE estimation at high spatial-temporal resolutions based on sixteen EC flux-tower measurements and related remotely sensed data. Results show that the data-driven satellite-based NEE simulation model has the potential to upscale EC flux NEE observations to landscape and regional scales with hi spatial-temporal resolutions.

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Comment: Ground observation data (EC flux data) will be better the performance of the diagnosed model. For precise calibration, EC data is needed anyway. This paper wrote well about data acquisition.

2nd Week of Jan

Author: Feng Gao, Thomas Hilker, Xiaolin Zhu, Martha C. Anderson, Jeffrey G. Masek, Peijuan Wang, Yun Yang

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Year: 2015

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Title: Fusing Landsat and MODIS datafor vegetation monitoring

Journal: IEEE Geoscience and Remote Sensing Magazine, 3 47-60

Summary: Reviewing STARFM and two extended data fusion data(STAARCH and ESTARFM) // STARFM (for homogeneous area) ESTARFM (for heterogeneous area) 

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Comment: STAACH is quite interesting! Identifying the date of disturbance!! Apply this method's idea in future study of image fusion(May be making some functions.)

1st Week of Jan

Author: Richardson, A.D., Keenan, T.F., Migliavacca, M., Ryu, Y., Sonnentag, O., & Toomey, M.

Year: 2013

Title: Climate change, phenology, and phenological control of vegetation feedbacks to the climate system.

Journal: Agricultural and Forest Meteorology, 169: 156-173

Summary: Review paper of feedback between phenology and climate system. As climate changes, we need to understand not only early spring but also late autumn(the end of the growing season). A conceptual understanding is not enough when quantitative estimates of feedbacks of vegetation to the climate system is missing. And last, Improving modeling of phenology is essential!

Comment: Easy to overlook and understand the field of phenology!! Check the references and work hard! Is there some ways to detect BVOCs by remote sensing? (BVOCs are known to play multiple ecological roles related to plant protection.)

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(unknown field)@ 2013

 1) Long-term. multi-species or multi-site studies that have used ground observations to investigate the cause of interannual variability

 2) The factors controling autumn senescence and the impact of climate change in senescence (ground observation)

 3) Quantification of climate system feedbacks resulting from phenology shifts influencing the seasonal course of surface roughness length

1st Week of Jan

Author: Xiaolin Zhu, Eileen H. Helmer, Feng Gao, Desheng Liu, Jin Chen, Michael A. Lefsky

Year: 2016

Title: A flexible spatiotemporal method for fusing satellite images with different resolutions

Journal: Remote sensing of Environment 172(2016) 1165-177

Summary: Brand-new method to fusing satellite image for high spatiotemporal resolution.(FSDAF) It is suitable for heterogeneous landscape and it can predict both gradual change and land cover type change.Compare with STARFM,UBDF, FSDAF creates more accurate images(+more spatial detail). To improve distribution of residual which is difference between predicted and original image, FSDAF use weighted function and neighborhood pixels by distances.

Comment: Issues that I need to solve in near future were found in this paper like capture tiny changes,weighted function for distribution of residual, optimal dates or conditions for a initial(benchmark) pair of images, typical image fusion for specific 'band'.

1st Week of Jan

Author: J.J. Walker, K.M. de Beurs, R.H. Wynne, F. Gao

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Year: 2012

Title: Evaluation of Landsat and MODIS data fusion products for analysis of dryland forest phenology

Journal: Remote sensing of environment 117 (2012) 381-393

Summary: The feasibility of using the STARFM algorithm to create synthetic, high-resolution imagery in dryland ecosystem. The range of MODIS datasets evaluated suggests that the MODIS NBAR product is the most applicable for use with the Landsat-5 data, given the 8-day tmeporal offset of the Landsat-5 and Terra nadir observation.

Comment: Initial pairs of Landsat/MODIS are essential to bridging data collection gaps and providing the higher spatial resolution. For Cloud-contaminated pixels, automatical identification must be made by JUWON in near future.

1st Week of Jan

Author:  Feng Gao, Martha C. Anderson, Xiaoyang Zhang, Zhengwei Yang, Joseph G. Alfieri, William P. Kustas, Rick Mueller, David M. Johnson, John H. Prueger

Year: 2017

Title: Toward mapping crop progress at field scales through fusion of Landsat and MODIS imagery

Journal: Remote Sensing of Environment 188 (2017) 9-25

Summary: Assessing fused Landsat-MODIS data(STARFM) over cropland and identify the relationships between

remotely sensed crop phenology metrics and the progress stages reported by NASS. For verification, result were assessed in comparison with carbon flux measurements, field observation, and crop progress statistics obtain from NASS CP reports at both the country level and the district level. 

Comment: Comparing fused data and Landsat in red/NIR band shows low-correlation, but in crop phenology stage shows quite high-correlation. (To improve quality of data) Landsat temporal frequency, matching Landsat-MODIS and computational efficiency have to be considered.

Find a problem in subjective ground observation and overcome with fusion method.

1st Week of Jan

Author: Hassan Ghassemian

Year: 2016

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Title: A review of remote sensing image fusion methods

Journal: Information Fusion 32 (2016) 75-89

Summary: Reviewing the methods in different levels (pixel, feature, decision), especially pixel level - CS, MRA,

hybrid, Model based algorithm

Comment: It explains general needs of image fusion methods and classifies methods by different levels. It's easy to get the list of papers about image fusion methods in overall. Under present condition, I need to study more and hard to understand well.

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