- Email: [email protected]
Chapter 16 Global Mapping of Net Primary Production
Chapter 16
Global Mapping of Net Primary Production Haruhisa Shimoda1,, Yoshio Awaya2 and Ichio Asanuma3 1
Tokai University Research & Information Center, 2-28-4 Tomigaya, Shibuya, Tokyo 151-0063, Japan Forestry and Forest Products Research Institute, 1 Matsunosato, Tsukuba, Ibaraki 305-8687, Japan 3 Tokyo University of Information Sciences, 1200-2, Yato, Wakaba, Chiba 265-8501, Japan 2
Abstract It is a well-known fact that atmospheric greenhouse gases are rapidly increasing within these 100 years, however, the sinks and sources of these gases are not necessarily clarified. Especially, sinks of carbon dioxide, which have the largest effects on global warming, are not evident. Generation of global map of net primary production (NPP) using earth-observing satellite data was performed in the research project named ‘‘International joint researches on global mapping of carbon cycle and its advancement’’ sponsored by the Ministry of Education, Culture, Sports, Science and Technology (MEXT). The results of this mapping project are briefly described in this chapter. The first global NPP maps using satellite data, which cover both ocean and continental ecosystems, have been obtained in this project. These global NPP maps have sufficient accuracy for a primary approximation. However, many problems remain, and various efforts are required to increase the accuracy of the global NPP data.
Keywords: NPP; global NPP; AVHRR; SeaWiFS
1
Introduction
It is a well-known fact that atmospheric greenhouse gases are rapidly increasing within these 100 years, however, the sinks and sources of these gases Corresponding author.
E-mail address: [email protected] (H. Shimoda).
Elsevier Oceanography Series 73 Edited by H. Kawahata and Y. Awaya ISBN: 0-444-52948-9
383
r Elsevier B.V. All rights reserved.
384
Haruhisa Shimoda et al.
are not necessarily clarified. Especially, sinks of carbon dioxide, which have the largest effects on global warming, are not clearly described. Many researches have been done to clarify those sinks both in the land and the ocean. The difficulties in these researches are that ground-based researches are executed on a local scale and need a long period. For example, researches in forests need at least 10 years for each independent forest to derive meaningful results. A global map of net primary production (NPP) was produced using earth-observing satellite data in the research project named ‘‘International joint researches on global mapping of carbon cycle and its advancement’’ sponsored by MEXT. The results of this mapping project are briefly described in this chapter.
2
Primary Production
It is necessary to estimate net carbon flux, which shows exchange of carbon dioxide between the ecosystems and atmosphere, in order to identify sinks of carbon dioxide (Field et al., 1998; Battle et al., 2000). However, only NPP maps, which show uptake of carbon dioxide by ecosystems, were generated in this project. The reasons are as follows. Vegetations on land and phytoplankton in the ocean both fix atmospheric carbon dioxide through photosynthesis process. This amount of fixation is called as gross primary production (GPP). After the fixation (mostly in night), vegetations emit carbon dioxide by respiration. NPP is the amount of difference between GPP and R (respiration). This way of retrieving NPP is the same both on land and in the ocean. On the contrary, the induction of net flux (NEP: net ecosystem production) is different between the land and the ocean. Over land, carbon dioxide is further emitted through soil respirations (Matamala et al., 2003). In order to estimate NEP, amount of these soil respirations should be estimated, but estimation of the soil respiration is rather difficult using satellite data. In the ocean, final carbon dioxide fixations are achieved through sinking of dead phytoplanktons or their consumption zooplankton or fishes. It is also very difficult to estimate the fixation amount from satellite data (Baret and Guyot, 1991). In addition to these biological processes, absorption and emission of carbon dioxide through physical processes exist. It is necessary to know difference of carbon dioxide partial pressures just above and below sea surface to estimate this physical process. However, estimating difference of partial pressures is also difficult from satellite data. Although the target of this project was set to estimate NPP, not NEP for those reasons above, the NPP maps obtained in this project are far from completeness. Many assumptions accompany the NPP retrieval processes both in the land and the ocean, and further, accurate validations have not
Global Mapping of Net Primary Production
385
yet been achieved on a global scale. However we suppose that these results are still useful, and advanced and accurate NPP maps would be generated through detailed investigations of our results.
3 3.1
Outlines of NPP Estimation Methods
Primary production is given by a product of absorbed radiant flux and photosynthetic efficiency. Absorbed radiant flux is given by a product of total radiant flux and absorption coefficient, while photosynthetic efficiency are affected by atmospheric temperature, water supply, nutrient supply, and so on. In the case of primary production estimation using satellite data, total radiant flux is estimated from the satellite data and then photosynthetic efficiency according to each target is determined. The total radiant flux corresponds to photosynthetic active radiation (PAR) and can be obtained from amounts of cloud retrieved from satellite data and solar irradiance at the top of the atmosphere. As for PAR, PAR distribution maps obtained from geostationary satellites are most appropriate, since PAR is an integrated value over a full day and geostationary satellites observe the earth’s surface hour by hour. Other parameters, which are required for NPP estimation, are not specific and dependent upon each algorithm.
3.2
Estimation Over Land
Three algorithms were compared to estimate land NPP. In the first algorithm, satellite data were used to estimate light absorption coefficients (fraction of photosynthetical active radiation: fAPAR) and the light use efficiency was fixed in this algorithm. The second algorithm used different light use efficiency according to each vegetation type and satellite data. Light use efficiencies were mainly determined on the basis of published field data. The third algorithm was a process model and did not use satellite data, however, results of this algorithm were used for comparisons. This process model used land cover data and meteorological data as input parameters. After the comparison of first and second algorithms, we decided to use the first algorithm which showed a better result compared to the ground data and the results of process model. The details of this algorithm are described in chapter 15 of this book and Awaya et al. (2004). The second algorithm showed higher values of NPP over agricultural fields. The reason for this overestimate may come from the fact that light use efficiency of agricultural fields was mainly decided by experiments in Japan. Light use efficiency of agricultural fields may differ based upon nutrient fertilization, hence overestimation of NPP occurred in fields in developing countries.
386
3.3
Haruhisa Shimoda et al.
Estimation in the Ocean
Only one algorithm was used for NPP estimation in the ocean. The most popular algorithm in this field is that given by Behrenfeld and Falkowski (1997). In this algorithm, light use efficiency is given by a 7th order polynomial as a function of sea surface temperature. The coefficients of this polynomial were obtained by a least squares fit to the in-situ measurements, however, a 7th order polynomial is rather unnatural to describe natural phenomena. In this project, light use efficiency was given by a function of chlorophyll-a concentration obtained by satellite data considering vertical light attenuation. As a result, light use efficiency was expressed by a 3rd order polynomial of sea surface temperature. The details of this algorithm are described in chapter 4 of this book and Asanuma et al. (2003).
4 4.1
Results and Discussions Satellite Data Used
NOAA AVHRR Path Finder Land dataset between 1982 and 1998, which are distributed by NOAA only for this period, were applied to calculate the land NPP distribution. On the contrary, in the ocean, SeaWiFs dataset between 1998 and 2002, which are distributed by NASA, were applied to calculate the ocean NPP distribution. We could retrieve the overlapping period of land and ocean NPP map in 1998. The result and discussions for the year of 1998 are described in the following section.
4.2
Global NPP Map
Fig. 1 shows the daily mean of global NPP for each month in 1998, where Fig. 1-(a) to (l), are corresponding to January to December. It should be noted that one largest El-Nino occurred from the end of 1997 to 1998, and these results could be slightly different from other years. On the land, large values of NPP are obtained in tropical rain forests and savanna as well as boreal forests around 50 to 60 degree north. In the ocean, large values are obtained on the continental shelf regions, like the west coast of Africa, the East China Sea, and the west coast of South Africa during phytoplankton bloom. In the eastern equatorial Pacific ocean, which is known as the equatorial upwelling region, a rather high NPP values was observed. Comparisons from Fig. 1(a) to Fig. 1(l) exhibit a large seasonal change on a local scale. Fig. 2(a) shows a monthly change of zonal mean of NPP. The highest values are obtained in September to October at 10 to 0 degree north. These areas correspond to the northern part of South America, a savanna in Africa, and a part of Indonesia and Malaysia. These seasonal increases of
Global Mapping of Net Primary Production
387
Figure 1: Global NPP maps in 1998. (a) January, (b) February, (c) March, (d) April, (e) May, (f) June, (g) July, (h) August, (i) September, (j) October, (k) November, (l) December (For colour version, see Colour Plate Section).
388
Figure 1 (Continued)
Haruhisa Shimoda et al.
Global Mapping of Net Primary Production
Figure 1 (Continued)
389
390
Figure 1 (Continued)
Haruhisa Shimoda et al.
391
Global Mapping of Net Primary Production
80
Latitude 0
-80
Month
Jan
Dec
Longitude 180
360
Month
Jan
0
Dec
Figure 2: Seasonal change of zonal and meridional mean NPP. (a) Zonal mean. Horizontal axis corresponds to 80 80 degree from north to south and vertical axis corresponds to January to December. (b) Meridional mean. Horizontal axis corresponds to 0–3601 eastbound and vertical axis corresponds to January to December (for colour version, see Colour Plate Section). NPP may correspond to rainy season in these areas. High NPPs are next obtained in June to July at 30–50 degree north. These areas cover most of the large crop field in the North America and the Eurasia, which correspond to agricultural fields and exhibit a distinct seasonal change. In contrast, the regions from 10 to 20 degree north and 0–20 degree south show rather high NPP with very weak seasonal changes, where tropical forests are major contributors with weak seasonal changes. Fig. 2(b) shows a monthly change of meridional mean of NPP. The highest values are obtained in May to July between 30 and 40 degree east, where the eastern part of Western Europe, some part of savanna, and some part of tropical forests are included. As tropical forests show very weak seasonal
392
Haruhisa Shimoda et al.
change as discussed in a zonal mean of NPP, the seasonal change observed between 30 and 40 degree may be the result of the change in savanna and agricultural fields in Europe. This seasonal change is also evident between 0 and 30 degree east, where the western part of Europe could be a major contribution. Next high NPP regions are observed from June to August between 100 and 120 degree east, which covers the most of the South Eastern Asia and exhibits high seasonal variations, because of agricultural fields in this region. The North and South America also show high NPP values, which is corresponding to the region between 110 and 40 degrees west. The North America shows high seasonal variations, i.e. high NPPs in May to September. South America, however, shows a small seasonal change because of tropical forests in this region. However, it should be noted again that NPP in 1998 was influenced by a large El-Nino event. Therefore the Western part of the American continent had high-level precipitations, while Indonesia suffered from drought. Also, NPP in the equatorial Pacific was rather small because of reduction of equatorial upwelling in this zone.
4.3
Validations
As mentioned in the Introduction, validations of these values are extremely difficult in the global scale. In this project, the results were validated using data in China main land for the land products, and the North Western Pacific and the equatorial Pacific Ocean for the ocean products. The details of these experiments are described by Asanuma et al. (2003) and Awaya et al. (2004). A correlation coefficient of 0.7 was derived in the validation of NPP using the surface-based data in both the land and the ocean, which shows some degree of credibility to the results of global NPP maps obtained in this project.
5
Conclusion
The first global NPP maps using satellite data have been obtained in this project. The accuracy of these global NPP maps is sufficient for a first-degree approximation. However, many problems remain, and many efforts are required to increase the accuracy of the global NPP data.
References Asanuma, I., Nieke, J., Sasaoka, K., Matsumoto, K., Kawano, T., 2003. Optical properties control primary productivity model on the East China
Global Mapping of Net Primary Production
393
Sea. In: Frouin, R. J. (Ed.), Ocean Remote Sensing and Applications. SPIE, pp. 312–319. Awaya, Y., Kodani, E., Tanaka, K., Liu, J., Zhuang, D., 2004. Estimation of the global net primary productivity using NOAA images and meteorological data: Changes between 1988 and 1993. International Journal of Remote Sensing 25–29, 1597–1613. Baret, F., Guyot, G., 1991. Potentials and limits of vegetation indices for LAI and APAR assessment. Remote Sensing of the Environment 35, 161–173. Battle, M., Bender, M. L., Tans, P. P., White, J. W. C., Ellis, J. T., Conway, T., Francey, R. J., 2000. Global carbon sinks and their variability inferred from atmospheric O2 and d 13C. Science 287, 2467–2470. Behrenfeld, M. J., Falkowski, P. G., 1997. Photosynthetic rates derived from satellite based chlorophyll concentration. Limnology and Oceanography 42, 1–20. Field, C. B., Behrenfeld, M. J., Randerson, J. T., Falkowski, P., 1998. Primary production of the biosphere: Integrating terrestrial and oceanic components. Science 281, 237–240. Matamala, R., Gonzalez-Meler, M. A., Jastrow, J. D., Norby, R. J., Schlesinger, W. H., 2003. Impacts of fine root turnover on forest NPP and soil C sequestration potential. Science 302, 1385–1387.
Plate 16.1: Global NPP maps in 1998. (a) January, (b) February, (c) March, (d) April, (e) May, (f) June, (g) July, (h) August, (i) September, (j) October, (k) November, (l) December.
Plate 16.1: (Continued)
Plate 16.1: (Continued)
Plate 16.1: (Continued)
80
Latitude 0
-80
Month
jan
Dec
Longitude 0
180
360
Month
Jan
Dec
Plate 16.2: Seasonal change of zonal and meridional mean NPP. (a) Zonal mean. Horizontal axis corresponds to 80 80 degree from north to south and vertical axis corresponds to January to December. (b) Meridional mean. Horizontal axis corresponds to 0–3601 eastbound and vertical axis corresponds to January to December.
Global Mapping of Net Primary Production Haruhisa Shimoda1,, Yoshio Awaya2 and Ichio Asanuma3 1
Tokai University Research & Information Center, 2-28-4 Tomigaya, Shibuya, Tokyo 151-0063, Japan Forestry and Forest Products Research Institute, 1 Matsunosato, Tsukuba, Ibaraki 305-8687, Japan 3 Tokyo University of Information Sciences, 1200-2, Yato, Wakaba, Chiba 265-8501, Japan 2
Abstract It is a well-known fact that atmospheric greenhouse gases are rapidly increasing within these 100 years, however, the sinks and sources of these gases are not necessarily clarified. Especially, sinks of carbon dioxide, which have the largest effects on global warming, are not evident. Generation of global map of net primary production (NPP) using earth-observing satellite data was performed in the research project named ‘‘International joint researches on global mapping of carbon cycle and its advancement’’ sponsored by the Ministry of Education, Culture, Sports, Science and Technology (MEXT). The results of this mapping project are briefly described in this chapter. The first global NPP maps using satellite data, which cover both ocean and continental ecosystems, have been obtained in this project. These global NPP maps have sufficient accuracy for a primary approximation. However, many problems remain, and various efforts are required to increase the accuracy of the global NPP data.
Keywords: NPP; global NPP; AVHRR; SeaWiFS
1
Introduction
It is a well-known fact that atmospheric greenhouse gases are rapidly increasing within these 100 years, however, the sinks and sources of these gases Corresponding author.
E-mail address: [email protected] (H. Shimoda).
Elsevier Oceanography Series 73 Edited by H. Kawahata and Y. Awaya ISBN: 0-444-52948-9
383
r Elsevier B.V. All rights reserved.
384
Haruhisa Shimoda et al.
are not necessarily clarified. Especially, sinks of carbon dioxide, which have the largest effects on global warming, are not clearly described. Many researches have been done to clarify those sinks both in the land and the ocean. The difficulties in these researches are that ground-based researches are executed on a local scale and need a long period. For example, researches in forests need at least 10 years for each independent forest to derive meaningful results. A global map of net primary production (NPP) was produced using earth-observing satellite data in the research project named ‘‘International joint researches on global mapping of carbon cycle and its advancement’’ sponsored by MEXT. The results of this mapping project are briefly described in this chapter.
2
Primary Production
It is necessary to estimate net carbon flux, which shows exchange of carbon dioxide between the ecosystems and atmosphere, in order to identify sinks of carbon dioxide (Field et al., 1998; Battle et al., 2000). However, only NPP maps, which show uptake of carbon dioxide by ecosystems, were generated in this project. The reasons are as follows. Vegetations on land and phytoplankton in the ocean both fix atmospheric carbon dioxide through photosynthesis process. This amount of fixation is called as gross primary production (GPP). After the fixation (mostly in night), vegetations emit carbon dioxide by respiration. NPP is the amount of difference between GPP and R (respiration). This way of retrieving NPP is the same both on land and in the ocean. On the contrary, the induction of net flux (NEP: net ecosystem production) is different between the land and the ocean. Over land, carbon dioxide is further emitted through soil respirations (Matamala et al., 2003). In order to estimate NEP, amount of these soil respirations should be estimated, but estimation of the soil respiration is rather difficult using satellite data. In the ocean, final carbon dioxide fixations are achieved through sinking of dead phytoplanktons or their consumption zooplankton or fishes. It is also very difficult to estimate the fixation amount from satellite data (Baret and Guyot, 1991). In addition to these biological processes, absorption and emission of carbon dioxide through physical processes exist. It is necessary to know difference of carbon dioxide partial pressures just above and below sea surface to estimate this physical process. However, estimating difference of partial pressures is also difficult from satellite data. Although the target of this project was set to estimate NPP, not NEP for those reasons above, the NPP maps obtained in this project are far from completeness. Many assumptions accompany the NPP retrieval processes both in the land and the ocean, and further, accurate validations have not
Global Mapping of Net Primary Production
385
yet been achieved on a global scale. However we suppose that these results are still useful, and advanced and accurate NPP maps would be generated through detailed investigations of our results.
3 3.1
Outlines of NPP Estimation Methods
Primary production is given by a product of absorbed radiant flux and photosynthetic efficiency. Absorbed radiant flux is given by a product of total radiant flux and absorption coefficient, while photosynthetic efficiency are affected by atmospheric temperature, water supply, nutrient supply, and so on. In the case of primary production estimation using satellite data, total radiant flux is estimated from the satellite data and then photosynthetic efficiency according to each target is determined. The total radiant flux corresponds to photosynthetic active radiation (PAR) and can be obtained from amounts of cloud retrieved from satellite data and solar irradiance at the top of the atmosphere. As for PAR, PAR distribution maps obtained from geostationary satellites are most appropriate, since PAR is an integrated value over a full day and geostationary satellites observe the earth’s surface hour by hour. Other parameters, which are required for NPP estimation, are not specific and dependent upon each algorithm.
3.2
Estimation Over Land
Three algorithms were compared to estimate land NPP. In the first algorithm, satellite data were used to estimate light absorption coefficients (fraction of photosynthetical active radiation: fAPAR) and the light use efficiency was fixed in this algorithm. The second algorithm used different light use efficiency according to each vegetation type and satellite data. Light use efficiencies were mainly determined on the basis of published field data. The third algorithm was a process model and did not use satellite data, however, results of this algorithm were used for comparisons. This process model used land cover data and meteorological data as input parameters. After the comparison of first and second algorithms, we decided to use the first algorithm which showed a better result compared to the ground data and the results of process model. The details of this algorithm are described in chapter 15 of this book and Awaya et al. (2004). The second algorithm showed higher values of NPP over agricultural fields. The reason for this overestimate may come from the fact that light use efficiency of agricultural fields was mainly decided by experiments in Japan. Light use efficiency of agricultural fields may differ based upon nutrient fertilization, hence overestimation of NPP occurred in fields in developing countries.
386
3.3
Haruhisa Shimoda et al.
Estimation in the Ocean
Only one algorithm was used for NPP estimation in the ocean. The most popular algorithm in this field is that given by Behrenfeld and Falkowski (1997). In this algorithm, light use efficiency is given by a 7th order polynomial as a function of sea surface temperature. The coefficients of this polynomial were obtained by a least squares fit to the in-situ measurements, however, a 7th order polynomial is rather unnatural to describe natural phenomena. In this project, light use efficiency was given by a function of chlorophyll-a concentration obtained by satellite data considering vertical light attenuation. As a result, light use efficiency was expressed by a 3rd order polynomial of sea surface temperature. The details of this algorithm are described in chapter 4 of this book and Asanuma et al. (2003).
4 4.1
Results and Discussions Satellite Data Used
NOAA AVHRR Path Finder Land dataset between 1982 and 1998, which are distributed by NOAA only for this period, were applied to calculate the land NPP distribution. On the contrary, in the ocean, SeaWiFs dataset between 1998 and 2002, which are distributed by NASA, were applied to calculate the ocean NPP distribution. We could retrieve the overlapping period of land and ocean NPP map in 1998. The result and discussions for the year of 1998 are described in the following section.
4.2
Global NPP Map
Fig. 1 shows the daily mean of global NPP for each month in 1998, where Fig. 1-(a) to (l), are corresponding to January to December. It should be noted that one largest El-Nino occurred from the end of 1997 to 1998, and these results could be slightly different from other years. On the land, large values of NPP are obtained in tropical rain forests and savanna as well as boreal forests around 50 to 60 degree north. In the ocean, large values are obtained on the continental shelf regions, like the west coast of Africa, the East China Sea, and the west coast of South Africa during phytoplankton bloom. In the eastern equatorial Pacific ocean, which is known as the equatorial upwelling region, a rather high NPP values was observed. Comparisons from Fig. 1(a) to Fig. 1(l) exhibit a large seasonal change on a local scale. Fig. 2(a) shows a monthly change of zonal mean of NPP. The highest values are obtained in September to October at 10 to 0 degree north. These areas correspond to the northern part of South America, a savanna in Africa, and a part of Indonesia and Malaysia. These seasonal increases of
Global Mapping of Net Primary Production
387
Figure 1: Global NPP maps in 1998. (a) January, (b) February, (c) March, (d) April, (e) May, (f) June, (g) July, (h) August, (i) September, (j) October, (k) November, (l) December (For colour version, see Colour Plate Section).
388
Figure 1 (Continued)
Haruhisa Shimoda et al.
Global Mapping of Net Primary Production
Figure 1 (Continued)
389
390
Figure 1 (Continued)
Haruhisa Shimoda et al.
391
Global Mapping of Net Primary Production
80
Latitude 0
-80
Month
Jan
Dec
Longitude 180
360
Month
Jan
0
Dec
Figure 2: Seasonal change of zonal and meridional mean NPP. (a) Zonal mean. Horizontal axis corresponds to 80 80 degree from north to south and vertical axis corresponds to January to December. (b) Meridional mean. Horizontal axis corresponds to 0–3601 eastbound and vertical axis corresponds to January to December (for colour version, see Colour Plate Section). NPP may correspond to rainy season in these areas. High NPPs are next obtained in June to July at 30–50 degree north. These areas cover most of the large crop field in the North America and the Eurasia, which correspond to agricultural fields and exhibit a distinct seasonal change. In contrast, the regions from 10 to 20 degree north and 0–20 degree south show rather high NPP with very weak seasonal changes, where tropical forests are major contributors with weak seasonal changes. Fig. 2(b) shows a monthly change of meridional mean of NPP. The highest values are obtained in May to July between 30 and 40 degree east, where the eastern part of Western Europe, some part of savanna, and some part of tropical forests are included. As tropical forests show very weak seasonal
392
Haruhisa Shimoda et al.
change as discussed in a zonal mean of NPP, the seasonal change observed between 30 and 40 degree may be the result of the change in savanna and agricultural fields in Europe. This seasonal change is also evident between 0 and 30 degree east, where the western part of Europe could be a major contribution. Next high NPP regions are observed from June to August between 100 and 120 degree east, which covers the most of the South Eastern Asia and exhibits high seasonal variations, because of agricultural fields in this region. The North and South America also show high NPP values, which is corresponding to the region between 110 and 40 degrees west. The North America shows high seasonal variations, i.e. high NPPs in May to September. South America, however, shows a small seasonal change because of tropical forests in this region. However, it should be noted again that NPP in 1998 was influenced by a large El-Nino event. Therefore the Western part of the American continent had high-level precipitations, while Indonesia suffered from drought. Also, NPP in the equatorial Pacific was rather small because of reduction of equatorial upwelling in this zone.
4.3
Validations
As mentioned in the Introduction, validations of these values are extremely difficult in the global scale. In this project, the results were validated using data in China main land for the land products, and the North Western Pacific and the equatorial Pacific Ocean for the ocean products. The details of these experiments are described by Asanuma et al. (2003) and Awaya et al. (2004). A correlation coefficient of 0.7 was derived in the validation of NPP using the surface-based data in both the land and the ocean, which shows some degree of credibility to the results of global NPP maps obtained in this project.
5
Conclusion
The first global NPP maps using satellite data have been obtained in this project. The accuracy of these global NPP maps is sufficient for a first-degree approximation. However, many problems remain, and many efforts are required to increase the accuracy of the global NPP data.
References Asanuma, I., Nieke, J., Sasaoka, K., Matsumoto, K., Kawano, T., 2003. Optical properties control primary productivity model on the East China
Global Mapping of Net Primary Production
393
Sea. In: Frouin, R. J. (Ed.), Ocean Remote Sensing and Applications. SPIE, pp. 312–319. Awaya, Y., Kodani, E., Tanaka, K., Liu, J., Zhuang, D., 2004. Estimation of the global net primary productivity using NOAA images and meteorological data: Changes between 1988 and 1993. International Journal of Remote Sensing 25–29, 1597–1613. Baret, F., Guyot, G., 1991. Potentials and limits of vegetation indices for LAI and APAR assessment. Remote Sensing of the Environment 35, 161–173. Battle, M., Bender, M. L., Tans, P. P., White, J. W. C., Ellis, J. T., Conway, T., Francey, R. J., 2000. Global carbon sinks and their variability inferred from atmospheric O2 and d 13C. Science 287, 2467–2470. Behrenfeld, M. J., Falkowski, P. G., 1997. Photosynthetic rates derived from satellite based chlorophyll concentration. Limnology and Oceanography 42, 1–20. Field, C. B., Behrenfeld, M. J., Randerson, J. T., Falkowski, P., 1998. Primary production of the biosphere: Integrating terrestrial and oceanic components. Science 281, 237–240. Matamala, R., Gonzalez-Meler, M. A., Jastrow, J. D., Norby, R. J., Schlesinger, W. H., 2003. Impacts of fine root turnover on forest NPP and soil C sequestration potential. Science 302, 1385–1387.
Plate 16.1: Global NPP maps in 1998. (a) January, (b) February, (c) March, (d) April, (e) May, (f) June, (g) July, (h) August, (i) September, (j) October, (k) November, (l) December.
Plate 16.1: (Continued)
Plate 16.1: (Continued)
Plate 16.1: (Continued)
80
Latitude 0
-80
Month
jan
Dec
Longitude 0
180
360
Month
Jan
Dec
Plate 16.2: Seasonal change of zonal and meridional mean NPP. (a) Zonal mean. Horizontal axis corresponds to 80 80 degree from north to south and vertical axis corresponds to January to December. (b) Meridional mean. Horizontal axis corresponds to 0–3601 eastbound and vertical axis corresponds to January to December.