The author regards fundamental root functions as underpinning photosynthesis activities by vegetation and as affecting
environmental issues, grain production, and desertification. This paper describes the present development of monitoring
and near real-time forecasting of environmental projects and crop production by approaching established operational
monitoring step-by-step. The author has been developing a thematic monitoring structure (named RSEM system) which
stands on satellite-based photosynthesis models over several continents for operational supports in environmental fields
mentioned above. Validation methods stand not on FLUXNET but on carbon partitioning validation (CPV). The models
demand continuing parameterization. The entire frame system has been built using Reanalysis meteorological data, but
model accuracy remains insufficient except for that of paddy rice. The author shall accomplish the system that
incorporates global environmental forces. Regarding crop production applications, industrialization in developing
countries achieved through direct investment by economically developed nations raises their income, resulting in
increased food demand. Last year, China began to import rice as it had in the past with grains of maize, wheat, and
soybeans. Important agro-potential countries make efforts to cultivate new crop lands in South America, Africa, and
Eastern Europe. Trends toward less food sustainability and stability are continuing, with exacerbation by rapid social and
climate changes. Operational monitoring of carbon sequestration by herbaceous and bore plants converges with efforts at
bio-energy, crop production monitoring, and socio-environmental projects such as CDM A/R, combating desertification,
and bio-diversity.
Sustainability of world crop production and food security has become uncertain. The authors have developed an
environmental research system called Remote Sensing Environmental Monitor (RSEM) for treating carbon sequestration
by vegetation, grain production, desertification of Eurasian grassland, and CDM afforestation/ reforestation to a
background of climate change and economic growth in rising Asian nations. The RSEM system involves vegetation
photosynthesis and crop yield models for grains, including land-use classification, stomatal evaluation by surface energy
fluxes, and daily monitoring for early warning. This paper presents a validation method for RSEM based on carbon
partitioning in plants, focusing in particular on the effects of area sizes used in crop production statistics on carbon
fixation and on sterility-based corrections to accumulated carbon sequestration values simulated using the RSEM
photosynthesis model. The carbonhydrate in grains has the same chemical formula as cellulose in grain plants. The
method proposed by partitioning the fixed carbon in harvested grains was used to investigate estimates of the amounts of
carbon fixed, using the satellite-based RSEM model.
The authors have developed a photosynthesis crop model for grain production under the background of climate
change and Asian economic growth in developing countries. This paper presents an application of the model to grain
fields of paddy rice, winter wheat, and maize in China and Southeast Asia. The carbon hydrate in grains has the same
chemical formula as that of cellulose in grain vegetation. The partitioning of carbon in grain plants can validate
fixation amounts of computed carbon using a satellite-based photosynthesis model. The model estimates the
photosynthesis fixation of rice reasonably in Japan and China. Results were validated through examination of carbon
in grains, but the model tends to underestimate results for winter wheat and maize. This study also provides daily
distributions of the PSN, which is the CO2 fixation in Asian areas combined with a land-cover distribution classified
from MODIS data, NDVI from SPOT VEGETATION, and meteorological re-analysis data by European Centre for
Medium-Range Forecasts (ECMWF). The mean CO2 and carbon fixation rates in paddy areas were 25.92 (t CO2/ha)
and 5.28 (t/ha) in Japan, respectively. The method is based on routine observation data, enabling automated
monitoring of crop yields.
This research is intended to develop a model to monitor rice yields using the photosynthetic yield index, which integrates
solar radiation and air temperature effects on photosynthesis and grain-filling from heading to ripening. Monitoring crop
production using remotely sensed and daily meteorological data can provide an important early warning of poor crop
production to Asian countries, with their still-growing populations, and also to Japan, which produces insufficient grain
for its population. The author improved a photosynthesis-and-sterility-based crop production CPI index to crop yield
index CYI, which estimates rice yields, in place of the crop situation index CSI. The CSI gives a percentage of rice
yields compared to normal annual production. The model calculates photosynthesis rates including biomass effects, lowtemperature
sterility, and high-temperature injury by incorporating: solar radiation, effective air temperature, normalized
difference vegetation index NDVI, and the effect of temperature on photosynthesis by grain plant leaves. The method is
based on routine observation data, enabling automated monitoring of crop production at arbitrary regions without special
observations. The method aims to quantity grain production at an early stage to raise the alarm in Asian countries, which
are facing climate fluctuation through this century of global warming.
This research aims to develop a remote sensing method for monitoring grain production in the early stages of crop growth in Japan and Asia. A photosynthesis based crop production index CPI for rice is proposed that takes into consideration the solar radiation, the effective air temperature, and NDVI as a factor representing vegetation biomass. The CPI index incorporates temperature influences such as the effect of temperature on photosynthesis by grain plant leaves, low-temperature effects of sterility, cool summer damage due to delayed growth, and high-temperature injury. These latter factors are significant at around the heading period of crops. The CPI index for rice was validated at ten monitoring sites in the central and northern half of Japan. The method is based on routine observation data, allowing automated monitoring of crop production at arbitrary sites without any special observations. The CPI index is applied to rice production in five regions of China, using solar irradiation data from the Japanese Geostationary Satellite, the Normalized Vegetation Index (NDVI) derived from NOAA AVHRR, and world weather data.
This paper aims to develop a remote sensing method of monitoring grain production in the early stages of crop growth. It is important to oversee the quantity of grain in production at an early stage in order to raise the alarm well in advance if a poor harvest is looming, especially in view of the rapid population increase in Asia and the long-term squeeze on water resources. Grain production monitoring would allow orderly crisis management to maintain food security in Japan, which is far from producing enough grain for its own population. We propose a photosynthesis-based crop production index CPI that takes into account all of: solar radiation, effective air temperature, vegetation biomass, the effect of temperature on photosynthesis by leaves of grain plants, low-temperature sterility, and high-temperature injury. These later factors, which extend the model of Rasmussen, are significant around the heading period of crops. The proposed photosynthesis-based crop production index CPI has accurately predicted the rice yield expressed by the Japanese Crop Situation Index in three years, including the worst yield in recent years, at a test site in Japan.
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