2017

4th, November 2017

Forzieri, G., Alkama, R., Miralles, D. G., & Cescatti, A. (2017). Satellites reveal contrasting responses of regional climate to the widespread greening of Earth. Science, 356(6343), 1180-1184.

- The founding of the relationship between the leaf area index and the energy flux, which affects the surface temperature, was interesting. The author has found high leaf area index decreases the albedo and cause the warming effect in cold regions (annual temp<280K and pricipitation>800mm). However, I think maybe the temperature increase was caused by the CO2 raised in the cold regions (since cold regions could be more sensitive compared to the warm regions (annual temp>290K)).

3rd, November 2017

Knop, E., Zoller, L., Ryser, R., Gerpe, C., Hörler, M., & Fontaine, C. (2017). Artificial light at night as a new threat to pollination. Nature, 548(7666), 206-209.

- I always wondered will the streetlamp affect something in the ecosystem. Thought the light intensity would be limited to run the photosynthetic cycle. The streetlamp, however, affected the pollinators. The nocturnal visits to plants have decreased 62% compared to the dark area. Since human need the streetlamp for safety or landscape how could we compromise the two necessity?

2nd, November 2017

Peterson, S. H., Ackerman, J. T., & Costa, D. P. (2015, July). Marine foraging ecology influences mercury bioaccumulation in deep-diving northern elephant seals. In Proc. R. Soc. B (Vol. 282, No. 1810, p. 20150710). The Royal Society.

- This paper has measured the mercury contamination in the ocean by using the satellite transmitters and time-depth recorders attached to the female elephant seals. The results came out to be the deeper-diving and offshore-foraging seals had the gratest mercurt concentration and shallower-diving and offsher-foraging and soastal and northerly foraging seals has followed next. The auther conclused that foraging behaviour influences mercury exposure and mercury bioaccumulation would be high in the northeast Pacific Ocean.

1st, November 2017

Schwalm, C. R., Anderegg, W. R., Michalak, A. M., Fisher, J. B., Biondi, F., Koch, G., ... & Huntzinger, D. N. (2017). Global patterns of drought recovery. Nature, 548(7666), 202-205.

- Drought event is the key event in decreasing the carbon sink. However, the important thing is how fast would the ecosystem recover the drought effect. Here, they have found out that the temperature and the precipitation are the control factors in determining the recovery time. By reading this paper I was curious about the figure 1c (precipitation). I understand that in low precipitation condition (-), the recovery time would increase. Then why high precipitation condition (+) recovery time increase?

4th, October 2017

Bastin, J. F., Berrahmouni, N., Grainger, A., Maniatis, D., Mollicone, D., Moore, R., ... & Aloui, K. (2017). The extent of forest in dryland biomes. Science, 356(6338), 635-638.

- This paper has used the manpower to classify the land cover type in dryland using Google Earth image. The estimated biomass area has increased 40~47 % (or 467 million ha) compared to the previous studies which have used the modern remote sensing satellite.

3rd, October 2017

Andela, N., Morton, D. C., Giglio, L., Chen, Y., van der Werf, G. R., Kasibhatla, P. S., ... & Bachelet, D. (2017). A human-driven decline in global burned area. Science, 356(6345), 1356-1362.

- It was interesting to know in some ecosystem the decrease of burn area (fire) could impact negatively on biodiversity. Generally, I learned human beings are harmful in the ecosystem by deforestation, cropland expands, and industry develops, however, in the fire active side, human can decrease the burn area and give positive feedback to the ecosystem.

2nd, October 2017

Betts, M. G., Wolf, C., Ripple, W. J., Phalan, B., Millers, K. A., Duarte, A., ... & Levi, T. (2017). Global forest loss disproportionately erodes biodiversity in intact landscapes. Nature, 547(7664), 441-444.

- Deforestation threatened the biodiversity or upgrading to a higher threat category and decline the populations. To minimize the biodiversity loss we think we should limit human effect, such as deforestation. However, by reading this paper, we could identify the risks were disproportionately high in relatively intact landscapes. This means species in such area which has been touched by the humans has some resistance to protect themselves from extinction. 

1st, October 2017

Laforest-Lapointe, I., Paquette, A., Messier, C., & Kembel, S. W. (2017). Leaf bacterial diversity mediates plant diversity and ecosystem function relationships. Nature, 546(7656), 145-147.   

- The leaf bacterial diversity has an impact on plant diversity and ecosystem function relationships. Without the leaf bacterial diversity, the ecosystem functional model showed unstable results, which means the bacterial diversity is an important factor. Although there were some similar papers this result had an impact since the result was shown in a quantitative way.

4th, September 2017

Pollock, L. J., Thuiller, W., & Jetz, W. (2017). Large conservation gains possible for global biodiversity facets. Nature, 546(7656), 141-144.

- From this paper, I have known that by expanding additional 5% of the protected space (land) protected range of species, functional, or phylogenetic units will triple. They have provided a framework and quantitative tools to advance the goals for multi-faceted biodiversity conservation.

3rd, September 2017

LaManna, J. A., Mangan, S. A., Alonso, A., Bourg, N. A., Brockelman, W. Y.,Bunyavejchewin, S., ... & Condit, R. (2017). Plant diversity increases with the strength of negative density dependence at the global scale. Science, 356(6345), 1389-1392.

- When population density is high the competition strength gets high and the disease is easy to get spread. Therefore the population density arrives at a specific range. This is called negative density dependency (NDD). However, there are two kinds of NDD. One is conspecific NDD and the other is heterospecific NDD. When CNDD is strong the population would not increase to a certain density, but when HNDD is strong one species would be the dominant species and out-compete the other species. 

2nd, September 2017

Campos-Taberner, M., García-Haro, F. J., Camps-Valls, G., Grau-Muedra, G., Nutini, F., Crema, A., & Boschetti, M. (2016). Multitemporal and multiresolution leaf area index retrieval for operational local rice crop monitoring. Remote Sensing of Environment, 187, 102-118.

- This paper reported an operational chain for high-resolution LAI retrieval from multiresolution satellite data using Gaussian process regression method in Mediterranean rice paddy area. The estimated LAI was highly correlated with the in situ LAI data. They have used a smartphone application called 'PocketLAI' which computes indirect LAI measurement by calculating the gap fraction from the 57.5-degree image. 

1st, September 2017

Hirooka, Y., Homma, K., Maki, M., & Sekiguchi, K. (2015). Applicability of synthetic aperture radar (SAR) to evaluate leaf area index (LAI) and its growth rate of rice in farmers’ fields in Lao PDR. Field Crops Research, 176, 119-122.

- There was an interesting way to estimate leaf area index of rice in this paper by multiplying the growth rate with the day after transplanting date and add the approximate LAI at the transplanting date [LAI= growing rate * DAT + approximate LAI at the transplanting date]. However, this method also had to consume effort on the early growing stage and at the senescent stage. 

4th, August 2017

de Moura, Y. M., Galvão, L. S., Hilker, T., Wu, J., Saleska, S., do Amaral, C. H., ... & de Oliveira, R. C. (2017). Spectral analysis of amazon canopy phenology during the dry season using a tower hyperspectral camera and modis observations. ISPRS Journal of Photogrammetry and Remote Sensing, 131, 52-64.

- The hyperspectral camera was mounted on top of a tower in the Amazon forest and took images of three different species to identify the canopy structure and phonology with spectral indexes. As a result enhanced vegetation index was more sensitive to canopy structure such as leaf area and green-red normalized difference index was more associated with the new leaf flushing. 

3rd, August 2017

LaManna, J. A., Mangan, S. A., Alonso, A., Bourg, N. A., Brockelman, W. Y., Bunyavejchewin, S., ... & Condit, R. (2017). Plant diversity increases with the strength of negative density dependence at the global scale. Science, 356(6345), 1389-1392.

- This paper was about the conspecific negative density dependence in a global scale. From the previous studies, it is known that higher biodiversity in the tropics is maintained by the result of CNDD. However, this study also identified the relationship between CNDD and species abundance (the strength of CNDD decreased when the distance from the equator increased). The finding was interesting, but since it was my first time reading papers about CNDD it was difficult to understand at once.  

2nd, August 2017

Liang, J., Crowther, T. W., Picard, N., Wiser, S., Zhou, M., Alberti, G., ... & de-Miguel, S. (2016). Positive biodiversity-productivity relationship predominant in global forests. Science, 354(6309), aaf8957.

- This paper stresses the importance of the biodiversity of the forest ecosystem. They have bridged the gap to a global scale from a regional scale. From their study, we could perceive the loss of species in forest ecosystem could reduce forest productivity and decrease the forest carbon absorption. Biodiversity economic value in global scale was also a negligible value. One large scale question came out to my mind by reading this paper. Now we know the forest biodiversity is an important factor in the carbon cycle and its economic value then what is the next step for us (the globe)? Still it would be difficult to control the deforestation and climate change and some of the forest trees must be used for human life (e.g. paper use, medicine, etc.). Also, as I remember rougher surface (different tree species: forest biodiversity) increase the sensible heat flux. Is heat mitigating a minor topic in this paper economic analysis part? Or is this a non-related story? 

1st, August 2017 

Lee, J., Lee, Y., Kim, G., & Shim, K. (2005). CO2 and water vapor flux measurement by eddy covariance method in a paddy field in Korea. Korean Journal of Agricultural and Forest Meteorology, 7(1), 45-50.

- This paper has monitored the carbon and water flux in Icheon rice paddy and identify the relationship with the vegetation index, solar radiation, and so on. To measure the leaf area index they used the LI-3000 sensor. One interesting thing is the dry mass showed an Omega pattern during the total growing season but the LI-3300 measured LAI did not and only showed an increasing pattern. The increasing pattern may have appeared in LAI-2200 or digital camera based LAI measurement caused by the grain. However, in my knowledge LI-3000 only scans the leaves. Then why?

4th, July 2017

Nagai, S., Yoshitake, S., Inoue, T., Suzuki, R., Muraoka, H., Nasahara, K. N., & Saitoh, T. M. (2014). Year-to-year blooming phenology observation using time-lapse digital camera images. Journal of Agricultural Meteorology, 70(3), 163-170.

- Cherry tree blooming phenology is an important event in Japan.This paper has monitored the blooming phenology successfully using the red-green-blue digital camera. The most effective vegetation index among six, green excess index [(DNg-DNr)+(DNg-DNb)] was the useful index for detecting the timing of the blooming.

3rd, July 2017

Douglas, E. M., Jacobs, J. M., Sumner, D. M., & Ray, R. L. (2009). A comparison of models for estimating potential evapotranspiration for Florida land cover types. Journal of Hydrology, 373(3), 366-376.

-This paper has compared three different models to estimate the potential evapotranspiration in Florida. Individual models are 1)Turc (Tc), 2) Priestley-Taylor (PT), and 3) Penman-Monteith (PM) method. I have thought the PM method would show the best performance over different land cover types. However, Tc and PT performed comparable to the PM method and in daily scale, PT method appeared to be the best method for estimating potential evapotranspiration in Florida. 

2nd, July 2017

Mu, X., Hu, R., Zeng, Y., McVicar, T. R., Ren, H., Song, W., ... & Yan, G. (2017). Estimating structural parameters of agricultural crops from ground-based multi-angular digital images with a fractional model of sun and shade components. Agricultural and Forest Meteorology, 246, 162-177.

- It was interesting to know how the authors have partitioned a single red-green-blue (RGB) image to sunlit -, shaded soil and leaves. In the case of photosynthesis studies, it would be a helpful methodology since sunlit and shaded leaves will have a different contribution to gross primary productivity. Yet, I should invest more time to understand the methodology.  

1st, July 2017

Motohka, T., Nasahara, K. N., Oguma, H., & Tsuchida, S. (2010). Applicability of green-red vegetation index for remote sensing of vegetation phenology. Remote Sensing, 2(10), 2369-2387.

- This paper compared green-red vegetation index (green-red/green+red) and normalized difference vegetation index (nir-red/nir+red). The GRVI sensitively responded to the vegetation color change and it showed different pattern depend on the vegetation type (deciduous broad leaf- and coniferous forest, grassland, and paddy field).

4th, June 2017

Liu, Y., Hill, M. J., Zhang, X., Wang, Z., Richardson, A. D., Hufkens, K., ... & Schaaf, C. B. (2017). Using data from Landsat, MODIS, VIIRS and PhenoCams to monitor the phenology of California oak/grass savanna and open grassland across spatial scales. Agricultural and Forest Meteorology, 237, 311-325.

- Multiple satellite images were used to detect phenologies despite differences in spatial resolution. MODIS, VIIRS, and Landsat (ETM+, OLI) satellites were analyzed to identify the similarities and differences between the two. As a result, all satellite sensors were highly correlated with the PhenoCam NDVI but the different viewing geometries and spatial coverage caused the NDVI differences. 

3rd, June 2017

Laurin, G. V., Chen, Q., Lindsell, J. A., Coomes, D. A., Del Frate, F., Guerriero, L., ... & Valentini, R. (2014). Above ground biomass estimation in an African tropical forest with lidar and hyperspectral data. ISPRS Journal of Photogrammetry and Remote Sensing, 89, 49-58.

- The above ground biomass was estimated with LIDAR and hyperspectral data since it is an important factor to understand the global climate changes. Both of the sensors had individual strength and error. By using both of the sensors the model was improved by R2 = 0.64 to 0.7. This paper also stresses out the importance of field measurements.

2nd, June 2017

Sass, R. L., & Cicerone, R. J. (2002). Photosynthate allocations in rice plants: Food production or atmospheric methane?. Proceedings of the National Academy of Sciences, 99(19), 11993-11995.

- Solar radiation and biomass seem to affect the methane emission. In the growing season (heading +- 21 days), 1 % reduction in solar radiation reduced 1.7 % (R2= 0.75) methane emission.

1st, June 2017

Pan, G., Zhou, P., Li, Z., Smith, P., Li, L., Qiu, D., ... & Chen, X. (2009). Combined inorganic/organic fertilization enhances N efficiency and increases rice productivity through organic carbon accumulation in a rice paddy from the Tai Lake region, China. Agriculture, ecosystems & environment, 131(3), 274-280.

- The combined inorganic and organic fertilization increased the nitrogen efficiency and enhanced the carbon storage in soil and reduce carbon emission. Combined inorganic and organic fertilization will also provide good effects on food security and reduce the net greenhouse gas emissions. 

4th, May 2017

Motohka, T., Nasahara, K. N., Miyata, A., Mano, M., & Tsuchida, S. (2009). Evaluation of optical satellite remote sensing for rice paddy phenology in monsoon Asia using a continuous in situ dataset. International Journal of Remote Sensing, 30(17), 4343-4357.

- This paper has monitored the rice paddy phenology for a year and a half, which is located in Japan. They have used in situ system (PEN-eye network) and satellite data (MODIS Terra/Aqua). Since the satellite is highly affected by the cloud (65%) they have combined the Terra and Aqua data and decreased the cloud effect (43%). The enhanced vegetation index showed the best correlation with the PEN-eye data. In conclusion, in monsoon Asia, daily EVI data from MODIS Terra and Aqua is the best dataset for monitoring rice paddy phenology. 

3rd, May 2017

D’Odorico, P., Gonsamo, A., Gough, C. M., Bohrer, G., Morison, J., Wilkinson, M., ... & Buchmann, N. (2015). The match and mismatch between photosynthesis and land surface phenology of deciduous forests. Agricultural and Forest Meteorology, 214, 25-38.

- To monitor the plant phenology in 19 deciduous broadleaf and mixed forest, this paper has used three vegetation indexes from satellite imagery (i.e., NDVI: Normalized Difference Vegetation Index; PI: Phenology Index; MODIS Land Cover Dynamics Product based on the Enhanced Vegetation Index, EVI) over 2000 to 2012. From the result, there was no particular outstanding vegetation index. Therefore, I think it is important to select the best index depending on the study propose. 

2nd, May 2017

Li, C., Qiu, J., Frolking, S., Xiao, X., Salas, W., Moore, B., ... & Sass, R. (2002). Reduced methane emissions from large‐scale changes in water management of China's rice paddies during 1980–2000. Geophysical Research Letters, 29(20).

- During 1980 to 2000 in China's rice paddies, 5 Tg CH4 yr-1 was reduced. This was possible since flooded rice paddies were changed to midseason drainage (water management). They have mentioned Chain's rice paddies might have played an important role in reducing CH4 in the 1990s and will affect the atmosphere for few decades. 

1st, May 2017

Matthes, J. H., Knox, S. H., Sturtevant, C., Sonnentag, O., Verfaillie, J., & Baldocchi, D. (2015). Predicting landscape-scale CO2 flux at a pasture and rice paddy with long-term hyperspectral canopy reflectance measurements. Biogeosciences, 12(15), 4577.

- By measuring canopy reflectance this paper has predicted the carbon flux (NEE and GPP) in two different sites. 

4th, April 2017

Rossini, M., Nedbal, L., Guanter, L., Ač, A., Alonso, L., Burkart, A., ... & Hanus, J. (2015). Red and far red Sun‐induced chlorophyll fluorescence as a measure of plant photosynthesis. Geophysical research letters, 42(6), 1632-1639.

- Sun-induced chlorophyll fluorescence (SiF) is a new term for me. As I understand, SiF is a sensitive indicator of vegetation stress which cannot be monitored by the NDVI signal (shown in Figure 2) and a high technology. One curiosity is that is the wavelength to extract the SiF signal always constant over the vegetation? Does the leaf surface characteristic differences influence the SiF? Also, what is the benefit of knowing the vegetation stress? Is it for maintenance of the vegetation? because if SiF is for understanding the terrestrial carbon cycle or balance, eddy covariance is already monitoring and quantifying the carbon flux between the atmosphere and vegetation over the various ecosystem.

3rd, April 2017

Cristóbal, J., Prakash, A., Anderson, M. C., Kustas, W. P., Euskirchen, E. S., & Kane, D. L. (2017). Estimation of surface energy fluxes in the Arctic tundra using the remote sensing thermal-based Two-Source Energy Balance model. Hydrology and Earth System Sciences, 21(3), 1339.

 I am not sure that if using MODIS leaf area index product is reliable since even measuring leaf area index manually is very difficult in arctic tundra region. I think there should be some cross-calibration between satellite product and field observation data. Unless there should/must be some unexpected under/over estimation or some error we cannot realize. 

2nd, April 2017

Lu, Z., Zou, S., Qin, Z., Yang, Y., Xiao, H., Wei, Y., ... & Xie, J. (2015). Hydrologic responses to land use change in the loess plateau: case study in the upper fenhe river watershed. Advances in Meteorology, 2015.

- Human has the greatest impact on land cover change. This paper studied about the hydrology difference caused by the land used and land cover (LULC) change at Loess Plateau, China. As a result the forest land contributed the most to the water yield (WY), however, the built-up land produced the greatest WY caused by the small initial loss and infiltration. The authors mentioned this will contribute to making decisions for land and water resource management. 

1st, April 2017

Walker, J. J., De Beurs, K. M., Wynne, R. H., & Gao, F. (2012). Evaluation of Landsat and MODIS data fusion products for analysis of dryland forest phenology. Remote Sensing of Environment, 117, 381-393.

- Walker and the co-authors used the MODIS and LANDSAT satellite imagery to produce a high-spatial and temporal resolution data by applying the STARFM algorism for data fusion. They have focused in the dryland forest ecosystem phenology. I learned that data fusion is highly required in monsoon climate region since the cloud ingenerate the data gap. However, it was interesting to see it was also applied in the dry region.

4th, March 2017

Carrijo, D. R., Lundy, M. E., & Linquist, B. A. (2017). Rice yields and water use under alternate wetting and drying irrigation: A meta-analysis. Field Crops Research, 203, 173-180.

- The alternate wetting and drying (AWD) system in rice paddy was a similar term with the intermittently-irrigating system which is already used the system in Korea agriculture. This study has identified compared to the AWD timing, AWD threshold had a major effect on rice yields. It is well known that rice paddy uses a large amount of water, which has to be reduced without decreasing the rice yield. However, the problem is depending on the cost of water and rice. Higher water saving does not necessarily indicate more economic benefits for farmers. Therefore, I think researchers also has to point out the AWD (or intermittently-irritating) system reduces the greenhouse gasses compared to always flooding rice paddy sites. 

3rd, March 2017

Origo, N., Calders, K., Nightingale, J., & Disney, M. (2017). Influence of levelling technique on the retrieval of canopy structural parameters from digital hemispherical photography. Agricultural and Forest Meteorology, 237, 143-149.

- It was good to know digital hemispherical photography (DHP) leveling has less influence in the effective leaf area index (<2%) and gap fraction (<1%). How about digital cover photography (DCP)? this was my curiosity from this paper. Maybe DCP could have relatively higher influence because the field of view is much narrower than the DHP. 

2nd, March 2017

Liang, L., Schwartz, M. D., & Fei, S. (2011). Validating satellite phenology through intensive ground observation and landscape scaling in a mixed seasonal forest. Remote Sensing of Environment, 115(1), 143-157.

- Landscape phenology map was made by scaling up the ground-based observation using IKONOS and QuickBird imagery. Since the study site was mixed forest, separating the species and estimating the abundance were the main task. Fortunately, scaling up individual phenology to population and community phenology would be straightforward because my study sites are 1) relatively homogeneous, 2) continuous data would be collected (less error for SOS and EOS), 3) there is no background vegetation, and we will use 4) high-resolution satellite data.

1st, March 2017

Yi, C., Pendall, E., & Ciais, P. (2015). Focus on extreme events and the carbon cycle. Environmental Research Letters, 10(7), 070201.

- I could overall understand the importance of the extreme events. The events were highly correlated with the global warming and terrestrial carbon cycling. It was well indicated in Figure 1 to 3. It was interesting to know the various ways to monitor the extremes by using satellite imagery, eddy covariance measurements, and even tree- rings. However, I think the interpretation of extreme events would be difficult since the subjective opinion is involved. Also, it would be tough to identify the control factor because every factor gives negative and positive feedbacks to each other. My curiosity by reading this paper was, such as global CO2, can’t we predict the extreme event and be prepared?

4th, February 2017

Körner, C., & Basler, D. (2010). Phenology under global warming. Science, 327(5972), 1461-1462.

-I had an opportunity to read this paper since I was studying the importance of the phenological events, which is highly related to the global warming, climate change. I shortly had a thought that if the growing season length is increasing, doesn't that have benefit it up taking carbon? Here, however, answered my question with this sentence:  "Ecosystem nutrient losses are a potential consequence of trees getting out of phase with the climate system. Climatic warming should thus not be seen as a self-evident cause for more tree growth.".

3rd, February 2017

Morin, T. H., Bohrer, G., Stefanik, K. C., Rey-Sanchez, A. C., Matheny, A. M., & Mitsch, W. J. (2017). Combining eddy-covariance and chamber measurements to determine the methane budget from a small, heterogeneous urban floodplain wetland park. Agricultural and Forest Meteorology, 237, 160-170.

- This paper is related to the methane eddy covariance and chamber measurements. Chamber measurements covered the flux tower footprint. However, they determined that methane flux showed similar value during the measurements. 

2nd, February 2017

Lee, K. J., & Lee, B. W. (2011). Estimating canopy cover from color digital camera image of rice field. Journal of Crop Science and Biotechnology, 14(2), 151-155.

- By using the RGB digital camera, they have quantified the canopy cover over the rice paddy (flooded and bare soil background). The equation was 1.2553EGI+0.01735G-0.01474B and the threshold was 0.03. From the figure 4. it seemed to be well working. However, I am curious if the coefficient would work continuously in the diverse ecosystem. 

1st, February 2017

Viña, A., Gitelson, A. A., Nguy-Robertson, A. L., & Peng, Y. (2011). Comparison of different vegetation indices for the remote assessment of green leaf area index

- This paper tested eight indices to identify the green leaf area index using the spectral reflectance measurement data (range was 400-900nm). From eight different indices, 'Red-edge Chlorophyll Index' showed the best result. Red-edge Chlorophyll Index divides red-edge to NIR and minus one (this equation was driven from Gitelson et al., (2003a), (2003c), (2005)) paper.

4th, January 2017

Olsson, L., Ye, S., Yu, X., Wei, M., Krauss, K. W., & Brix, H. (2015). Factors influencing CO2 and CH4 emissions from coastal wetlands in the Liaohe Delta, Northeast China. Biogeosciences, 12(16), 4965-4977.

- This study focused on wetland carbon dioxide and methane emission since rice paddy is one of the wetlands in agriculture I compared the methane emission related to water temperature. As written in the paper the methane increased around 20-30 degree celsius. This was caused by the methanogenic archaea. There are some previous papers that root is one of the control factors in methane emission. However, I am still having some curiosity in this contend.

3rd, January 2017

Frolking, S., Qiu, J., Boles, S., Xiao, X., Liu, J., Zhuang, Y., ... & Qin, X. (2002). Combining remote sensing and ground census data to develop new maps of the distribution of rice agriculture in China. Global Biogeochemical Cycles, 16(4).

- At first, I thought this paper was related to methane emission map since I searched 'methane global map, rice paddy' in google scholar. However, Frolking and other coworkers developed a novel China rice map by using remote sensing data (satellite: Landsat) and field survey data. It was interesting that they could classify how many times the field has been harvested (single or multi) in such large country. I hope to find other paper which used this kind of map and estimate the methane emission from the rice paddy on a global scale.

2nd, January 2017

Giampietro, M., Cerretelli, G., & Pimentel, D. (1992). Energy analysis of agricultural ecosystem management: human return and sustainability. Agriculture, Ecosystems & Environment, 38(3), 219-244.

- This paper converted the biological unit to an energy unit. For example, when GPP unit is kg m-2 y-1, by multiplying 0.56 the value could be converted to W m-2. As following the equation, our study site (rice paddy: CRK) GPP value was 0.62 W m-2 in energy unit. 

1st, January 2017

Watanabe, A., Yamada, H., & Kimura, M. (2001). Effects of shifting growth stage and regulating temperature on seasonal variation of CH4 emission from rice. Global biogeochemical cycles, 15(3), 729-739.

- I was wondering if the methane flux could be related to air temperature. In the introduction of this paper, the author mansion the diurnal variation in methane could be influenced by the air or soil temperature. However, the relationship of seasonal variation in methane flux with air or soil temperature has not been clear. Also, it was useful to know that shifting the date of transplanting does not shift the methane emissions.