2016

4th, December 2016

Suyker, A. E., Verma, S. B., Clement, R. J., & Billesbach, D. P. (1996). Methane flux in a boreal fen: Season‐long measurement by eddy correlation. Journal of Geophysical Research: Atmospheres, 101(D22), 28637-28647.

-  Methane emission was 12 days shift from the water table depth (m) pattern. Also, the peak of the peat temperature was same with the peak methane flux. From this paper, I compared the seasonal methane flux with the water table depth and precipitation in my study site. There was a high conformity with rising methane flux with the water situation. However, there weren't any correlation with the soil temperature and water contain in soil.  

3rd, December 2016

Tanaka, A., & Navasero, S. A. (1964). Loss of nitrogen from the rice plant through rain or dew. Soil Science and Plant Nutrition, 10(1), 36-39.

- Recently, we identified nitrogen decreased suddenly during the summer season in our rice paddy site. Korea has a heavy rainfall during the monsoon season (Jun-Aug). By overlaying the precipitation pattern and nitrogen pattern the decrease signal of nitrogen agreed well with the heavy rainfall period. This paper concluded rain can reduce 30% of the nitrogen in rice caused by rain or dew which supplied nitrogen in the early stage. Also, the lower rice and shaded rice nitrogen losses nitrogen easier compared to the higher sun leaf rice. This paper helped us to understand the nitrogen decrease phenomenon in the summer season. 

2nd, December 2016

Zhou, Z., Plauborg, F., Kristensen, K., & Andersen, M. N. (2017). Dry matter production, radiation interception and radiation use efficiency of potato in response to temperature and nitrogen application regimes. Agricultural and Forest Meteorology, 232, 595-605.

- This paper studied about how potato dry matter, radiation interception, and radiation use efficiency response to temperature and nitrogen through the growing season. The increase of nitrogen affects the APAR and increase the dry matter and the temperature increase affects the RUE and decreases the dry matter. Therefore, when the temperature is high, less nitrogen has to be supplied. It as also interesting to know leaf area index could also estimate the fPAR.

1st, December 2016

C. Wu, D. Peng, K. Soudani, L. Siebicke, C. M. Gough, M. A. Arain, G. Bohrer, P. M. Lafleur, M. Peichl, A.Gonsamo, S. Xu, B. Fang & Q. Ge (2017). Land surface phenology derived from normalized difference vegetation index (NDVI) at global FLUXNET sites. Agricultural and Forest Meteorology, 233, 171-182.

- Estimated the start and end of growing season (SOS and EOS) using different algorithm, model, sensor, and plant functional types based on NDVI data in FLUXNET sites. Identified evergreen and mixed forests showed low phenology correlation with the observed data and complicated algorithm did not always show the best results. Also, MODIS-based SOS showed reasonable result compared to the SPOT-VGT result, but EOS did not. This paper recommended validating the phenology modeling  before applying the algorithms to identify the SOS and EOS trends.

4th, November 2016

Taha, H. (1997). Urban climates and heat islands: albedo, evapotranspiration, and anthropogenic heat. Energy and buildings, 25(2), 99-103.

Kobayashi, H., & Iwabuchi, H. (2008). A coupled 1-D atmosphere and 3-D canopy radiative transfer model for canopy reflectance, light environment, and photosynthesis simulation in a heterogeneous landscape. Remote Sensing of Environment, 112(1), 173-185.

- Albedo, evapotranspiration, and anthropogenic heat was the major factors which decrease the urban heat island. This paper concluded the normal increase of albedo and vegetation led two degrees of air temperature compared to none. Also, by reading Kobayashi et al (2008) it was interesting to know the cone-shaped crown are more effective in photosynthesis than the sphere canopy.  

3rd, November 2016

Churkina, G. (2008). Modeling the carbon cycle of urban systems. Ecological Modelling, 216(2), 107-113.

- Churkina reviewed about urban carbon cycle systems. There were many interesting simulating models that I did not know. She compared the methods of the models and concluded when developing an urban carbon cycle model both biophysical and human-related fluxes has to be included. 

2nd, November 2016

Hideki Kobayashi (October 20, 2008). Forest Light Environmental Simulator (FLiES), Version 2.0: User's Manual

- I read this user's manual since nowadays I am getting interested in the urban area and want to make some kind of modeling. I will read the 2008 paper by Kobayashi, H., & Iwabuchi, H.  in  Remote Sensing of Environment journal. Hope this user's manual got me some sense of the FLiES simulator. 

1st, November 2016

Alberto, M. C. R., Wassmann, R., Hirano, T., Miyata, A., Kumar, A., Padre, A., & Amante, M. (2009). CO 2/heat fluxes in rice fields: comparative assessment of flooded and non-flooded fields in the Philippines. Agricultural and Forest Meteorology, 149(10), 1737-1750.

- Using less water is not only answer to reduce the CO2 flux since by reducing the water table the rice yield difference was relatively high.  

4th, October 2016

Nouchi, I., Hosono, T., Aoki, K., & Minami, K. (1994). Seasonal variation in methane flux from rice paddies associated with methane concentration in soil water, rice biomass and temperature, and its modelling. Plant and soil, 161(2), 195-208.

- They also identified methane flux increase at 10hh and hit the maximum at 13hh. In this paper, the phenomenon was construed as the gas bubbles on sunny days by multiple linear regression using global solar radiation and air temperature (19.34*solar radiation + 8.13*temperature in 13hh -197.85). Myself also identified in the total growing season there is a two peak in methane flux. This paper described the reasons by their results. The first peak was due to the anaerobic breakdown of organic matter in the soil and second peak was due to the organic matter of rice straw.  Also, there was a strong correlation between methane flux and soil temperature. The entire growing season methane flux was 3.2 g CH4 m-2 y-1 in this study site. 

3rd, October 2016

Said-Pullicino, D., Miniotti, E. F., Sodano, M., Bertora, C., Lerda, C., Chiaradia, E. A., ... & Celi, L. (2016). Linking dissolved organic carbon cycling to organic carbon fluxes in rice paddies under different water management practices.Plant and Soil, 401(1-2), 273-290.

- Recently, I only focused on CO2 and CH4 flux in rice paddy through the atmosphere. This paper focused on the same flux but in a different view. Another sampled the water from three different water management rice paddies (continuous flooding, flooding at a particular season, and intermittent irrigated) to calculate carbon concentration in the water (dissolved organic carbon; DOC) and monitor CH4 flux using chamber. The intermittent irrigated paddy showed highest soil Eh (redox potential) and lowest pH value, this leads the lowest DOC at every soil depth (25, 50, and 75 cm) and mitigate the CH4 flux in three different water management rice paddy.

2nd, October 2016

Reba, M. L., & Counce, P. A. (2016). Seasonal variation in measured H2O and CO2 flux of irrigated rice in the Mid-South.

- Flooded rice paddy site was near the Mississippi river, maximum and average H2O flux were 6.05 and 4.45mm per day, respectively.  It was interesting that the H2O flux was not relatively higher than the Alberto et al. (2011) site. Does this mean there is none influence of the Mississippi river?  In the opening, they have mentioned the Mississippi river so I thought there would be some difference compared to other studies. Also, without the methane flux this study site was a carbon sink.

1st, October 2016

Kim, Y., Talucder, M. S. A., Kang, M., Shim, K. M., Kang, N., & Kim, J. (2016). Interannual variations in methane emission from an irrigated rice paddy caused by rainfalls during the aeration period. Agriculture, Ecosystems & Environment,223, 67-75.

- This study area was located in Korea which observed three-year data of methane (CH4 ) in irrigated rice paddy. There is a mid-season drainage period in Korea cultivation. This cultivation mitigates the CH4 emission. However, since Asian regions have the monsoon season in summer this two phenomenon were overlapped and decreased the CH4 mitigation effect. Three-year CH4 flux showed a large difference (198 to 450 kg CH4 ha-1). This study also recommended to test the mid-season drainage in various conditions in the rice paddy. 

4th, September 2016

Detto, M., Verfaillie, J., Anderson, F., Xu, L., & Baldocchi, D. (2011). Comparing laser-based open-and closed-path gas analyzers to measure methane fluxes using the eddy covariance method. Agricultural and forest meteorology,151(10), 1312-1324.

- This paper compared two sensors closed- (LGR) and the other sensor is an open-path (LI-7700) methane gas analyzer, in three different ecosystems in the USA. Overall, the closed-path system required more care in maintaining the instrument and the open-path system seemed appropriate for unattended sites caused by the low power requirement. The data loss was 9% lower than the closed-path sensor during the same period. 

3rd, September 2016

Meijide, A., Gruening, C., Goded, I., Seufert, G., & Cescatti, A. (2016). Water management reduces greenhouse gas emissions in a Mediterranean rice paddy field. Agriculture, Ecosystems & Environment.

- From this article we could identify water table depth is the control factor of CH4 in Mediterranean rice paddy field (it mitigated approximately 16 gC m-2 of CH4). Comparing the two-year observation of NEE, 2010 year (controlled the water use) NEE was approximately 20 gC m-2 lower, however, the rice yield was similar between the two years.  

2nd, September 2016

Zhao, H., Fu, Y. H., Wang, X., Zhao, C., Zeng, Z., & Piao, S. (2016). Timing of rice maturity in China is affected more by transplanting date than by climate change. Agricultural and Forest Meteorology, 216, 215-220.

- Would this article conclusion also relevant to our rice paddy site? Our site transplanted two weeks earlier compare to the previous year (2015). I should cheek the result when analyzing.  

1st, September 2016

Hatala, J. A., Detto, M., & Baldocchi, D. D. (2012). Gross ecosystem photosynthesis causes a diurnal pattern in methane emission from rice.Geophysical Research Letters, 39(6).

- Hatala and coauthor concluded methane flux is related to the gross ecosystem photosynthesis in growing season. They conclude the GEP leads the methane flux by 1.3 hours. 

4th, August 2016

Naser, H. M., Nagata, O., Tamura, S., & Hatano, R. (2007). Methane emissions from five paddy fields with different amounts of rice straw application in central Hokkaido, Japan. Soil Science and Plant Nutrition, 53(1), 95-101.

- I have studied there is three pathway to emit methane gas, one way was by the rice straw. I found this article and this author concluded that there is a relationship between rice straw and methane gas emitting. Since methane emission increased with increasing amounts of rice straw.

3rd, August 2016

Allen, R. G., Pereira, L. S., Raes, D., & Smith, M. (1998). Crop evapotranspiration-Guidelines for computing crop water requirements-FAO Irrigation and drainage paper 56. FAO, Rome, 300(9), D05109.

- Listed the equations, how to compute the crop evapotranspiration.

2nd, August 2016

Knox, S. H., Matthes, J. H., Sturtevant, C., Oikawa, P. Y., Verfaillie, J., & Baldocchi, D. (2016). Biophysical controls on interannual variability in ecosystem‐scale CO2 and CH4 exchange in a California rice paddy. Journal of Geophysical Research: Biogeosciences, 121(3), 978-1001.

- This paper wanted to know the pattern of the methane flux in diel, seasonal, and interannual timescale. And which factors are important for predicting methane flux. This paper had 6.5 years of eddy covariance data in a rice paddy. Discovered photosynthesis was the dominant factor influencing the diel pattern in methane flux and soil temperature influenced the amplitude of diel methane fluctuations. Also, we had a curiosity if stomata conductance is related with the methane flux. However, it has turn out there is no relationship between the stomata conductance and methane. 

1st, August 2016

Zeng, N., Zhao, F., Collatz, G. J., Kalnay, E., Salawitch, R. J., West, T. O., & Guanter, L. (2014). Agricultural Green Revolution as a driver of increasing atmospheric CO2 seasonal amplitude. Nature, 515(7527), 394-397.

- Crop production tripled in the past 50 years, from 0.5 Pg C yr-1 to 1.5Pg C yr-1. By analyzing the carbon flux per 2.5o latitude, Asia, Europe and North America showed out to be the major of agricultural lands. Interesting thing is that in some region (latitude between 20 to 40o) cropland had more carbon flux to the atmosphere than the natural vegetation. 

4th, July 2016

Timm, A. U., Roberti, D. R., Streck, N. A., Gustavo G. de Gonçalves, L., Acevedo, O. C., Moraes, O. L., ... & Toll, D. L. (2014). Energy partitioning and evapotranspiration over a rice paddy in Southern Brazil. Journal of Hydrometeorology, 15(5), 1975-1988.

- Data processing in this paper was very well based on Allen et al. 1998 paper. Computed energy balance closer and evapotranspiration in Brazil rice paddy. Also, suggest using parameters such as leaf area index (satellite data could be easy to access) and crop height can be an easy and interesting method to estimate evapotranspiration in lowland regions.

3rd, July 2016

Li, S., Kang, S., Li, F., & Zhang, L. (2008). Evapotranspiration and crop coefficient of spring maize with plastic mulch using eddy covariance in northwest China. Agricultural Water Management, 95(11), 1214-1222.

- Studied energy closure and evapotranspiration in northwest China spring maize. Interestingly the energy closure showed strong 1:1 relationship (ET+H=0.93(Rn-G)). Evapotranspiration-based on water balance method and eddy covariance agreed fine. The daily evapotranspiration had a linear relationship with the net radiation and leaf area index, and an exponential relationship with the temperature.  

2nd, July 2016

Ono, K., Mano, M., Han, G. H., Nagai, H., Yamada, T., Kobayashi, Y., ... & Lal, R. (2015). Environmental Controls on Fallow Carbon Dioxide Flux in a Single‐Crop Rice Paddy, Japan. Land Degradation & Development, 26(4), 331-339.

- Crop residues increase the soil organic carbon when rice paddy was flooded. The author stressed out soil water content is also important for carbon balance caused by its large interannual variability and relatively low permeability of the paddy soil. This paper had three-year data, and the amount of GPP were 37, 52, 47 g C m-2 in 2004, 2005, and 2006, respectively and seasonal respiration were 264, 260, and 226 g C m-2. CH4 emission was only observed in 2005 with 9.3 g C m-3.  

1st, July 2016

Baldocchi, D., Falge, E., Gu, L., & Olson, R. (2001). FLUXNET: A new tool to study the temporal and spatial variability of ecosystem-scale carbon dioxide, water vapor, and energy flux densities. Bulletin of the American Meteorological Society, 82(11), 2415.

- Overall the flow of explanation of the eddy covariance (flux) was very helpful to understand. Got some new knowledge from this paper. 

4th, June 2016

Byun, K., Shin, J., Lee, Y. K., & Choi, M. (2013). Validation of Net Radiation Measured from Fluxtower Based on Eddy Covariance Method: Case Study in Seolmacheon and Cheongmicheon Watersheds. Journal of Korea Water Resources Association, 46(2), 111-122.

- Study in Cheongmicheon and Seolmacheon (South Korea) also had difference between surface energy fluxes and available energy. This paper corrected the latent heat flux (LE_B) by using the Bowen ratio method and the result actually increased the relationship. However, it would have been an interesting study if there were more analysis result about the site, not only applying the Bowen ratio method. 

3rd, June 2016

Yan, X., Ohara, T., & Akimoto, H. (2003). Development of region‐specific emission factors and estimation of methane emission from rice fields in the East, Southeast and South Asian countries. Global Change Biology, 9(2), 237-254.

- I wanted to study about the methane nowadays. This paper listed the methane emission from the rice field in Asian countries. I found out that hot spot of methane emission is nearby the river region (China). Also, methane flux has a linear relationship with the soil organic carbon content and inverse relationship with the soil PH.

2nd, June 2016

Julitta, T., Rossini, M., Burkart, A., Cogliati, S., Davies, N., Hom, M., ... & Colombo, R. (2016). Comparison of Sun-Induced Chlorophyll Fluorescence Estimates Obtained from Four Portable Field Spectroradiometers. Remote Sensing, 8(2), 122.

- Compared four different sensors to estimate the Sun-Induced Chlorophyll Fluorescence (SIF). Ocean optic QEpro and ASD Fieldspec sensors are used in the field. From the result, ASD overestimates both red and far-red fluorescence compared to all sensors. 

1st, June 2016

Wilson, K., Goldstein, A., Falge, E., Aubinet, M., Baldocchi, D., Berbigier, P., ... & Grelle, A. (2002). Energy balance closure at FLUXNET sites. Agricultural and Forest Meteorology, 113(1), 223-243.

- First time learning about energy balance closure. The equation was ‘LE+H=Rn-G-S-Q’ but, surface energy fluxes (LE+H) was 10 to 30% underestimated of available energy (Rn-G-S). This paper listed several causes about this underestimated symptom. I should re-read this paper until I could understand the meanings. I also want to the energy balance of my Cheorwon rice paddy site. 

4th, May 2016

Nagai, S., Saitoh, T. M., Nasahara, K. N., & Suzuki, R. (2015). Spatio-temporal distribution of the timing of start and end of growing season along vertical and horizontal gradients in Japan. International journal of biometeorology, 59(1), 47-54.

- This paper analyzed the timing of start and end of growing season in Japan though 2003 to 2013, using 500m resolution satellite image. Found out in both broadleaf and needle leaf forest, leaf expansion is more sensitive to air temperature compared to leaf senescence. 

3rd, May 2016

Li, Y., Chen, D., Walker, C. N., & Angus, J. F. (2010). Estimating the nitrogen status of crops using a digital camera. Field Crops Research, 118(3), 221-227.

- Using RGB camera, the relationship between nitrogen and estimated canopy cover was well correlated. The camera settings (e.i. Exposure setting, white balance) were automatic. To estimate canopy cover or vegetation index, most of the equation used the red and green channel, this point was very new to me since I only used the blue channel to estimate the canopy structure (i.e. Gap fraction).

2nd, May 2016

Wang, Y., Wang, D., Zhang, G., & Wang, J. (2013). Estimating nitrogen status of rice using the image segmentation of GR thresholding method. Field Crops Research, 149, 33-39.

- This paper estimated nitrogen by using digital cover photography. The camera was facing downward and images were collected in raw format and converted to JPEG format. Thresholding method was easy to follow since it subtracts red channel image to green channel image. I am expecting good results by using this method since, according to the figure 1a, the rice paddy was already flooded by water but well classified the vegetation and water soil.

1st, May 2016

Campos-Taberner, M., Garcia-Haro, F. J., Confalonieri, R., Martinez, B., Moreno, A., Sanchez-Ruiz, S., ... & Busetto, L. (2015, July). Intercomparison of instruments for measuring leaf area index over rice. In Geoscience and Remote Sensing Symposium (IGARSS), 2015 IEEE International (pp. 3389-3392). IEEE.

- It was interesting to know that it is possible to estimate gap fraction and leaf area index through a smartphone. Wondering how the algorithm works in the smartphone not only how to take images.

4th, April 2016

Xiao, X., He, L., Salas, W., Li, C., Moore Iii, B., Zhao, R., ... & Boles, S. (2002). Quantitative relationships between field-measured leaf area index and vegetation index derived from VEGETATION images for paddy rice fields. International Journal of Remote Sensing, 23(18), 3595-3604.

- When analyzing crop leaf area index in the use of digital camera, it was difficult to process the water reflected leaves. However, in the use of remote sensing (e.g. Landsat), mid-infrared (1580-1750nm) band could solve this problem since the mid-infrared is highly sensitive to soil moisture content, vegetation cover, and leaf moisture contents. Hope there are some kind of easier and accurate estimation method for the digital camera. 

3rd, April 2016

Reichstein, M., Falge, E., Baldocchi, D., Papale, D., Aubinet, M., Berbigier, P., ... & Grünwald, T. (2005). On the separation of net ecosystem exchange into assimilation and ecosystem respiration: review and improved algorithm. Global Change Biology, 11(9), 1424-1439.

- This paper analyzed the effect of short-term (15days) and long-term (seasonal data) temperature sensitivity on the estimation of daytime Reco and GPP. Among the sites (forest, shrub land, crop) crop site showed the largest difference between long- and short-term temperature sensitivity. As a conclusion, it recommends using the new generic algorithm which uses short-term temperature sensitivity of Reco from eddy covariance data. Because this algorithm would give us less bias of GPP and Reco estimation.

2nd, April 2016

Nijland, W., de Jong, R., de Jong, S. M., Wulder, M. A., Bater, C. W., & Coops, N. C. (2014). Monitoring plant condition and phenology using infrared sensitive consumer grade digital cameras. Agricultural and forest meteorology, 184, 98-106.

- This paper compared the performance of phenology and plant health monitoring between RGB digital camera and NIR camera. Conclude NIR is limited in monitoring phenology compared to RGB camera caused by the limited in dynamic range and band separation. I am still curious why NIR camera showed less performance in phenology. Since NIR camera showed good performance at plant health, isn’t that result related to the phenology result? 

1st, April 2016

Sakamoto, T., Gitelson, A. A., Wardlow, B. D., Arkebauer, T. J., Verma, S. B., Suyker, A. E., & Shibayama, M. (2012). Application of day and night digital photographs for estimating maize biophysical characteristics. Precision Agriculture, 13(3), 285-301.

- I was wondering why they did not use the NIR camera (night time image) because it seemed it could estimate leaf area index more accurately compare to RGB camera (day time image). The reason why they used RGB camera was NIR camera showed brightness pixels, however, I think it would be promising to use NIR camera (showed more contrast between soil and vegetation). Also realized extracting only green leaf form the crop is needed (figure 1a).

4th, March 2016

Casanova, D., Epema, G. F., & Goudriaan, J. (1998). Monitoring rice reflectance at field level for estimating biomass and LAI. Field Crops Research,55(1), 83-92.

- Estimated LAI and biomass using reflectance in the crop field. From this paper, I could organize the gaps of the rice paddy studies. Also, the most important issue in the rice paddy is the flooding period.  

3rd, March 2016

Tanaka, S., Kawamura, K., Maki, M., Muramoto, Y., Yoshida, K., & Akiyama, T. (2015). Spectral Index for Quantifying Leaf Area Index of Winter Wheat by Field Hyperspectral Measurements: A Case Study in Gifu Prefecture, Central Japan. Remote Sensing, 7(5), 5329-5346.

- The relationship between leaf area index (LAI) and normalized difference spectral index (NDSI) showed similar results from Ryu, et al. (2010) Figure 10 (b). However, this paper found out the difference between reflectance values at 760 and 739nm (DSIr760-r739) showed significant and strong linear relationship with LAI and showed an outstanding estimation of LAI.

* Ryu, Y., Baldocchi, D. D., Verfaillie, J., Ma, S., Falk, M., Ruiz-Mercado, I., ... & Sonnentag, O. (2010). Testing the performance of a novel spectral reflectance sensor, built with light emitting diodes (LEDs), to monitor ecosystem metabolism, structure, and function. Agricultural and Forest Meteorology, 150(12), 1597-1606.

2nd, March 2016

Nagai, S., Saigusa, N., Muraoka, H., & Nasahara, K. N. (2010). What makes the satellite-based EVI–GPP relationship unclear in a deciduous broad-leaved forest?. Ecological research, 25(2), 359-365.

- EVI-GPP large variability was caused by the cloud condition in the satellite. Also, the error showed larger during the leaf-expansion period. Reading this paper I realized field measurements are also highly required to understand the ecosystem. 

1st, March 2016

Saito, M., Miyata, A., Nagai, H., & Yamada, T. (2005). Seasonal variation of carbon dioxide exchange in rice paddy field in Japan. Agricultural and Forest Meteorology, 135(1), 93-109.

- Most of the Fluxnet site were located in forest ecosystems. However, this paper study area was rice paddy site. Also, most of the study period in crop land were around one week or month but this paper observed one cycle of the growing season. The most important thing from this paper to me was the storage flux. Although the single height of measurement, it is recommended to estimate the CO2 storage term.

4th, February 2016

Fang, H., Li, W., Wei, S., & Jiang, C. (2014). Seasonal variation of leaf area index (LAI) over paddy rice fields in NE China: Intercomparison of destructive sampling, LAI-2200, digital hemispherical photography (DHP), and AccuPAR methods. Agricultural and Forest Meteorology, 198, 126-141.

- By reading this paper I realized separating green and yellow leaf is important but also, separating stem and leaf is an important factor. 

3rd, February 2016

Liu, J., Pattey, E., & Admiral, S. (2013). Assessment of in situ crop LAI measurement using unidirectional view digital photography. Agricultural and forest meteorology, 169, 25-34.

- Compared leaf area index (LAI) and green plant area index (GAI) estimated with 57.5◦ view angle and nadir photographic method using the digital camera. For wheat, it was difficult to estimate LAI and GAI after elongation stage, therefore, LAI and GAI were overestimated compared to corn and soybean. For wheat this paper concluded, only GAI should be estimated when using nadir photographic method. Since wheat is sensitive to clumping index (0.88) and extinction coefficient (0.61). Also, the classification method should be improved for the wheat crop.  

2nd, February 2016 

Nagai, S., Nasahara, K. N., Inoue, T., Saitoh, T. M., & Suzuki, R. (2015). Review: advances in in situ and satellite phenological observations in Japan.International journal of biometeorology, 1-13.

- It was interesting to know that citizen could participate in phenology studies. Also, surprised to know that cherry blossoms had such long recorded history (since the 9th century AD). 

1st, February 2016

Meyer, G. E., & Neto, J. C. (2008). Verification of color vegetation indices for automated crop imaging applications. Computers and Electronics in Agriculture, 63(2), 282-293.

- This paper used ExG-ExR images (ExG was calculated as 2G-R-B and ExR were 1.4R-G) to classify vegetation (crop) and soil from a single digital cover image. There was not any difficult threshold equation. The threshold was just ‘zero’. However, the separation seemed to be ideal. Two concerns appeared 1) the mixed pixels and 2) the reflected leaves from the underwater.

4th, January 2016

Matsushita, B., Yang, W., Chen, J., Onda, Y., & Qiu, G. (2007). Sensitivity of the enhanced vegetation index (EVI) and normalized difference vegetation index (NDVI) to topographic effects: a case study in high-density cypress forest. Sensors, 7(11), 2636-2651.

- I found this paper since I did not clearly understand the difference between Enhanced Vegetation index (EVI) and normalized difference vegetation index (NDVI). Notice EVI incorporates both background adjustment and atmospheric resistance concepts. Also, EVI has better results than NDVI showed. This paper concluded that the soil adjustment factor in EVI makes the sensitivity compared to NDVI. 

3rd, January 2016

Nagai, S., Saitoh, T. M., Kurumado, K., Tamagawa, I., Kobayashi, H., Inoue, T., ... & Nishida Nasahara, K. (2013). Detection of bio-meteorological year-to-year variation by using digital canopy surface images of a deciduous broad-leaved forest. SOLA, 9(0), 106-110.

- By obtaining 8 years (2004-2011) data, this paper analyzed the relationship between the start of leaf-expansion, end of leaf-fall date and temperatures. As a result, in Japan deciduous forest 2 degrees and 18 degrees showed the affected timings of SLF and ELF, respectively. However, there was no significant long-term trend in the timing of SLE. 

2nd, January 2016

Sakamoto, T., Shibayama, M., Kimura, A., & Takada, E. (2011). Assessment of digital camera-derived vegetation indices in quantitative monitoring of seasonal rice growth. ISPRS Journal of Photogrammetry and Remote Sensing,66(6), 872-882.

- I had a hard time estimating leaf area index (LAI) in crop land (rice paddy) using the RGB digital camera caused by the reflected leaves in water. However, this paper used a near-infrared camera at night time and had high accuracy in estimating LAI. One unfortunate thing was the authors used the find JPEG image and calibrated the DNs to have a linear relationship with the light intensity. I think this complicated analysis could be eliminated when using the raw image for the first time.  

1st, January 2016

Choi, J. P., Kang, S. K., Choi, G. Y., Nasahara, K. N., Motohka, T., & Lim, J. H. (2011). Monitoring canopy phenology in a deciduous broadleaf forest using the Phenological Eyes Network (PEN). Journal of Ecology and Environment, 34(2), 149-156.

- This paper used the pen-eye system in Gwangneung deciduous forest site and compared with MODIS data in 2009. However, I am curious why authors mentioned about the hemispherical spectroradiometer even though they do not have any results about it on the paper.