With the launch of the first civilian early-morning orbit satellite Fengyun-3E(FY-3E),higher demands are placed on the accuracy of radiative transfer simulations for hyperspectral infrared data.Therefore,several key i...With the launch of the first civilian early-morning orbit satellite Fengyun-3E(FY-3E),higher demands are placed on the accuracy of radiative transfer simulations for hyperspectral infrared data.Therefore,several key issues are investigated in the paper.First,the accuracy of the fast atmospheric transmittance model implemented in the Advanced Research and Modeling System(ARMS)has been evaluated with both the line-by-line radiative transfer model(LBLRTM)and the actual satellite observations.The results indicate that the biases are generally less than 0.25 K when compared to the LBLRTM,while below 1.0 K for the majority of the channels when compared to the observations.However,during both comparisons,significant biases are observed in certain channels.The accuracy of Hyperspectral Infrared Atmospheric Sounder-II(HIRAS-II)onboard FY-3E is comparable to,and even superior to that of the Cross-track Infrared Sounder(CrIS)onboard NOAA-20.Furthermore,apodization is a crucial step in the processing of hyperspectral data in that the apodization function is utilized as the instrument channel spectral response function to produce the satellite channel-averaged transmittance.To further explore the difference between the apodized and unapodized simulations,Sinc function is adopted in the fast transmittance model.It is found that the use of Sinc function can make the simulations fit the original satellite observations better.When simulating with apodized observations,the use of Sinc function exhibits larger deviations compared to the Hamming function.Moreover,a correction module is applied to minimize the impact of Non-Local Thermodynamic Equilibrium(NLTE)in the shortwave infrared band.It is verified that the implementation of the NLTE correction model leads to a significant reduction in the bias between the simulation and observation for this band.展开更多
Global solar radiation (GSR) is an essential physical quantity for agricultural management and designing infrastructures. Because GSR has often been modeled as a function of sunshine duration (SD) and day length for a...Global solar radiation (GSR) is an essential physical quantity for agricultural management and designing infrastructures. Because GSR has often been modeled as a function of sunshine duration (SD) and day length for a given set of locations and calendar days, analyzing interannual trends in GSR and SD is important to evaluate, predict or regulate the cycles of energy and water between geosphere and atmosphere. This study aimed to exemplify interannual trends in GSR and SD, which had been recorded from 2001 to 2022 in 40 meteorological stations in Japan, and validate the applicability of an SD-based model to the evaluation of GSR. Both the measured GSR and SD had increased in many of the stations in the study period with averaged rates of 0.252 [W·m−2·y−1] and 0.015 [h·d−1·y−1], respectively. The offset and the slope of the SD-based model were estimated by fitting the model to the measured data sets and were found to have been almost constant with the averages of 0.201[-] and 0.566[-], respectively, indicating that characteristics of the SD-GSR relation had not varied for the 22-year period and that the model and its parameter set can be stationarily applicable to the analyses and predictions of GSR in recent years. The stable trends in both parameters also implied that the upward trend in SD can be a main explanatory factor for that in the measured GSR. The upward trend in SD had coincided with the increase in the frequency of heavy-shortened rains, suggesting that the time period of each rainfall event had gradually decreased, which may be attributable to the obtained upward trend in SD. Further studies are required to clarify if there is some cause-effect relation between the changes in rainfall patterns and the standard level of solar radiation reaching the land surface.展开更多
Canopy photosynthesis,rather than leaf photosynthesis,is highly related to plant biomass and yield formation.Studying canopy photosynthesis and identifying the parameters that control it can help optimize agricultural...Canopy photosynthesis,rather than leaf photosynthesis,is highly related to plant biomass and yield formation.Studying canopy photosynthesis and identifying the parameters that control it can help optimize agricultural management and achieve crop yield potential.Compared with traditional parameters,canopy occupation volume(COV)offers an integrative parameter on canopy architecture related to canopy photosynthetic rates.In this study,we developed a high-throughput method to derive COV for different rice varieties.We first used multi-perspective two-dimensional imaging to reconstruct three-dimensional point clouds of rice plants and developed a suite of pipelines to calculate plant height,leaf number,tiller number,and biomass,with R^(2) values of 91.8%,95.9%,82.3%,and 94.3%,respectively.We further employed point cloud data to reconstruct the surfaces of rice plants and construct a virtual canopy model of the rice population.Light distribution was simulated using a ray-tracing algorithm and canopy photosynthetic rates were simulated via photosynthetic rate-incident light intensity curve fitting.Furthermore,we systematically explored the relationships between canopy phenotypes and photosynthetic rates,and found that COV was the most effective predictor of canopy photosynthesis,achieving an R^(2) value of 92.1%.Adjustment in atmospheric transmittance showed that COV strongly correlated with canopy photosynthesis under different light conditions,with higher accuracy observed under diffuse light.Variations in planting density confirmed that this correlation remained strong at the community level.In summary,this study demonstrates that COV is closely linked to simulated canopy photosynthesis and the developed pipeline can support future agronomic and breeding research.展开更多
基金Supported by the Startup Project of Donghai Laboratory(DH-2023QD0002)National Key Research and Development Program of China(2021YFB3900400)Hunan Provincial Natural Science Foundation of China(2021JC0009)。
文摘With the launch of the first civilian early-morning orbit satellite Fengyun-3E(FY-3E),higher demands are placed on the accuracy of radiative transfer simulations for hyperspectral infrared data.Therefore,several key issues are investigated in the paper.First,the accuracy of the fast atmospheric transmittance model implemented in the Advanced Research and Modeling System(ARMS)has been evaluated with both the line-by-line radiative transfer model(LBLRTM)and the actual satellite observations.The results indicate that the biases are generally less than 0.25 K when compared to the LBLRTM,while below 1.0 K for the majority of the channels when compared to the observations.However,during both comparisons,significant biases are observed in certain channels.The accuracy of Hyperspectral Infrared Atmospheric Sounder-II(HIRAS-II)onboard FY-3E is comparable to,and even superior to that of the Cross-track Infrared Sounder(CrIS)onboard NOAA-20.Furthermore,apodization is a crucial step in the processing of hyperspectral data in that the apodization function is utilized as the instrument channel spectral response function to produce the satellite channel-averaged transmittance.To further explore the difference between the apodized and unapodized simulations,Sinc function is adopted in the fast transmittance model.It is found that the use of Sinc function can make the simulations fit the original satellite observations better.When simulating with apodized observations,the use of Sinc function exhibits larger deviations compared to the Hamming function.Moreover,a correction module is applied to minimize the impact of Non-Local Thermodynamic Equilibrium(NLTE)in the shortwave infrared band.It is verified that the implementation of the NLTE correction model leads to a significant reduction in the bias between the simulation and observation for this band.
文摘Global solar radiation (GSR) is an essential physical quantity for agricultural management and designing infrastructures. Because GSR has often been modeled as a function of sunshine duration (SD) and day length for a given set of locations and calendar days, analyzing interannual trends in GSR and SD is important to evaluate, predict or regulate the cycles of energy and water between geosphere and atmosphere. This study aimed to exemplify interannual trends in GSR and SD, which had been recorded from 2001 to 2022 in 40 meteorological stations in Japan, and validate the applicability of an SD-based model to the evaluation of GSR. Both the measured GSR and SD had increased in many of the stations in the study period with averaged rates of 0.252 [W·m−2·y−1] and 0.015 [h·d−1·y−1], respectively. The offset and the slope of the SD-based model were estimated by fitting the model to the measured data sets and were found to have been almost constant with the averages of 0.201[-] and 0.566[-], respectively, indicating that characteristics of the SD-GSR relation had not varied for the 22-year period and that the model and its parameter set can be stationarily applicable to the analyses and predictions of GSR in recent years. The stable trends in both parameters also implied that the upward trend in SD can be a main explanatory factor for that in the measured GSR. The upward trend in SD had coincided with the increase in the frequency of heavy-shortened rains, suggesting that the time period of each rainfall event had gradually decreased, which may be attributable to the obtained upward trend in SD. Further studies are required to clarify if there is some cause-effect relation between the changes in rainfall patterns and the standard level of solar radiation reaching the land surface.
基金supported by the National Natural Science Foundation of China(Grant Nos.32201654 and U22A20464)National Key Research and Development Program from the Ministry of Science and Technology of China(Grant No.2020YFA0907600)the 2115 Talent Development Program of China Agricultural University.
文摘Canopy photosynthesis,rather than leaf photosynthesis,is highly related to plant biomass and yield formation.Studying canopy photosynthesis and identifying the parameters that control it can help optimize agricultural management and achieve crop yield potential.Compared with traditional parameters,canopy occupation volume(COV)offers an integrative parameter on canopy architecture related to canopy photosynthetic rates.In this study,we developed a high-throughput method to derive COV for different rice varieties.We first used multi-perspective two-dimensional imaging to reconstruct three-dimensional point clouds of rice plants and developed a suite of pipelines to calculate plant height,leaf number,tiller number,and biomass,with R^(2) values of 91.8%,95.9%,82.3%,and 94.3%,respectively.We further employed point cloud data to reconstruct the surfaces of rice plants and construct a virtual canopy model of the rice population.Light distribution was simulated using a ray-tracing algorithm and canopy photosynthetic rates were simulated via photosynthetic rate-incident light intensity curve fitting.Furthermore,we systematically explored the relationships between canopy phenotypes and photosynthetic rates,and found that COV was the most effective predictor of canopy photosynthesis,achieving an R^(2) value of 92.1%.Adjustment in atmospheric transmittance showed that COV strongly correlated with canopy photosynthesis under different light conditions,with higher accuracy observed under diffuse light.Variations in planting density confirmed that this correlation remained strong at the community level.In summary,this study demonstrates that COV is closely linked to simulated canopy photosynthesis and the developed pipeline can support future agronomic and breeding research.
