In this study, we present results from the CASA (Carnegie-Ames-Stanford Approach) model to estimate net primary production (NPP) in grasslands under different management (ranching versus unmanaged) on the Central Coas...In this study, we present results from the CASA (Carnegie-Ames-Stanford Approach) model to estimate net primary production (NPP) in grasslands under different management (ranching versus unmanaged) on the Central Coast of California. The latest model version called CASA Express has been designed to estimate monthly patterns in carbon fixation and plant biomass production using moderate spatial resolution (30 m to 250 m) satellite image data of surface vegetation characteristics. Landsat imagery with 30 m resolution was adjusted by contemporaneous Moderate Resolution Imaging Spectroradiometer (MODIS) data to calibrate the model based on previous CASA research. Results showed annual NPP predictions of between 300 - 450 grams C per square meter for coastal rangeland sites. Irrigation increased the predicted NPP carbon flux of grazed lands by 59 grams C per square meter annually compared to unmanaged grasslands. Low intensity grazing activity appeared to promote higher grass regrowth until June, compared to the ungrazed grassland sites. These modeling methods were shown to be successful in capturing the differing seasonal growing cycles of rangeland forage production across the area of individual ranch properties.展开更多
With remote sensing information products becoming increasingly varied and arguably improved, scientific applications of such products rely on their quality assessment. In an operational context such as the NASA (Natio...With remote sensing information products becoming increasingly varied and arguably improved, scientific applications of such products rely on their quality assessment. In an operational context such as the NASA (National Aeronautics and Space Administration) information production based on the MODIS (Moderate Resolution Imaging Spectroradiometer) instrument on board Earth Observing System (EOS) Terra and Aqua satellites, efficient ways of detecting product anomaly, i.e., to discriminate between product artifacts and real changes in Earth processes being monitored, are extremely important to assist and inform the user communities about potential unreliability in the products. A technique for anomaly detection, known as MAD (the median of absolute deviate from the median), in MODIS land products via time series analysis is described, which can handle intra- and in-ter-annual variation in the data by using MAD statistics of the original data and their first-order difference. This method is shown to be robust and work across major land products, including NDVI, active fire, snow cover, and surface reflectance, and its applicabil-ity to multi-disciplinary products is anticipated.展开更多
In this study,the Surface Energy Balance Algorithms for Land(SEBAL) model and Moderate Resolution Imaging Spectroradiometer(MODIS) products from Terra satellite were combined with meteorological data to estimate evapo...In this study,the Surface Energy Balance Algorithms for Land(SEBAL) model and Moderate Resolution Imaging Spectroradiometer(MODIS) products from Terra satellite were combined with meteorological data to estimate evapotranspiration(ET) over the Sanjiang Plain,Northeast China.Land cover/land use was classified by using a recursive partitioning and regression tree with MODIS Normalized Difference Vegetation Index(NDVI) time series data,which were reconstructed based on the Savitzky-Golay filtering approach.The MODIS product Quality Assessment Science Data Sets(QA-SDS) was analyzed and all scenes with valid data covering more than 75% of the Sanjiang Plain were selected for the SEBAL modeling.This provided 12 overpasses during 184-day growing season from May 1st to October 31st,2006.Daily ET estimated by the SEBAL model was misestimaed at the range of-11.29% to 27.57% compared with that measured by Eddy Covariance system(10.52% on average).The validation results show that seasonal ET from the SEBAL model is comparable to that from ground observation within 8.86% of deviation.Our results reveal that the time series daily ET of different land cover/use increases from vegetation on-going until June or July and then decreases as vegetation senesced.Seasonal ET is lower in dry farmland(average(Ave):491 mm) and paddy field(Ave:522 mm) and increases in wetlands to more than 586 mm.As expected,higher seasonal ET values are observed for the Xingkai Lake in the southeastern part of the Sanjiang Plain(Ave:823 mm),broadleaf forest(Ave:666 mm) and mixed wood(Ave:622 mm) in the southern/western Sanjiang Plain.The ET estimation with SEBAL using MODIS products can provide decision support for operational water management issues.展开更多
Satellite-derived surface temperature data is increasingly required to supplement the limited weather stations for the assessment of temperature trend over the data-sparse Antarctic Ice Sheet. To accomplish this, it i...Satellite-derived surface temperature data is increasingly required to supplement the limited weather stations for the assessment of temperature trend over the data-sparse Antarctic Ice Sheet. To accomplish this, it is essential to assess the relationship and difference between satellite-based land-surface temperature (LST) retrieval and air temperature observation. In this study, we made a comparison between monthly averaged LST from Moderate Resolution Imaging Spectroradiometer (MODIS) and the corresponding air temperature at the nominal heights of 1 m and 2 m from automatic weather stations (AWSs) over the Lambert Glacier basin, East Antarctica. This comparison reveals a statistically significant correlation between the two types of temperature measurements with correlation coefficient (R) above 0.6. Also, the time difference between satellite overpass and air temperature observation is not critical for the R values. Although MODIS LST evidently deviates from air temperature (Mean difference fluctuates from 2.87°C to 8.08°C) probably due to the temperature inversion effect, heterogeneity in surface emissivity, representative of AWS measurements and satellite self limitation. MODIS LST measurements have a great potential for the accurate evaluation or monitoring of regional air temperature over Antarctica, and thus better improve current reconstruction of spatial and temporal reconstruction variability in Antarctic temperature.展开更多
The accuracy of the cloud-aerosol lidar with orthogonal polarization (CALIOP), moderate resolution imaging spectroradiometer (MODIS), Multi-Angle Implementation of Atmospheric Correction (MAIAC), and Geostationary Ope...The accuracy of the cloud-aerosol lidar with orthogonal polarization (CALIOP), moderate resolution imaging spectroradiometer (MODIS), Multi-Angle Implementation of Atmospheric Correction (MAIAC), and Geostationary Operational Environmental Satellite (GOES) aerosol optical depth (AOD) products for the Arctic north of 59.75°N was examined by means of 35 aerosol robotic network (AERONET) AOD sites. The assessment for June to October 2006 to 2020 showed MAIAC AOD agreed the best with AERONET AOD;CALIOP AOD differed the strongest from the AERONET AOD. Cross-correlations of CALIOP AOD along the satellite path indicated that AOD-values 40 km up-and-down the path often failed to represent the AERONET AOD-values within ±30 min of the overpass in this region dominated by easterly winds. Typically, CALIOP AOD was lower than AERONET AOD and MAIAC AOD at the sites, especially, at sites with mean AOD below 0.1. Generally, MODIS AOD values exceeded those of MAIAC. Comparison of CALIOP, MAIAC, and MODIS products resampled on a 0.25° × 0.25° grid revealed differences among the products caused by their temporal and spatial resolution, sample habit and size. Typically, the MODIS AOD-product showed the most details in AOD distribution. Despite differences in AOD-values, all products provided similar temporal evolution of elevated and lower AOD.展开更多
该文采用M OD IS N DV I时序数据对东北区土地覆盖分类进行研究,以验证M OD IS区域土地覆盖制图的可靠性。通过试验发现经过Sav izky-G o lay滤波处理能有效去除云、缺失数据及异常值的影响,使得N DV I时序曲线能更好的反映植被季相变...该文采用M OD IS N DV I时序数据对东北区土地覆盖分类进行研究,以验证M OD IS区域土地覆盖制图的可靠性。通过试验发现经过Sav izky-G o lay滤波处理能有效去除云、缺失数据及异常值的影响,使得N DV I时序曲线能更好的反映植被季相变化特征,分类结果表明N DV I时序数列能较好的区分植被与非植被、草本(一年生)与木本(多年生)覆盖类型。但研究区内一年一熟的农作物与高盖度草地、落叶针叶林与落叶阔叶林具有相似的物候特征,混分现象比较严重。该研究通过添加地表温度(land surface tem perature,LST)数据解决这一问题,利用所得温度/植被指数TV I对研究区进行土地覆盖分类。所得结果用363个野外调查样区进行验证,N DV I及TV I时序数据的分类精度分别为62.26%与71.63%。结果表明TV I比N DV I对土地覆盖类型中的植被类型识别更有效。展开更多
文摘In this study, we present results from the CASA (Carnegie-Ames-Stanford Approach) model to estimate net primary production (NPP) in grasslands under different management (ranching versus unmanaged) on the Central Coast of California. The latest model version called CASA Express has been designed to estimate monthly patterns in carbon fixation and plant biomass production using moderate spatial resolution (30 m to 250 m) satellite image data of surface vegetation characteristics. Landsat imagery with 30 m resolution was adjusted by contemporaneous Moderate Resolution Imaging Spectroradiometer (MODIS) data to calibrate the model based on previous CASA research. Results showed annual NPP predictions of between 300 - 450 grams C per square meter for coastal rangeland sites. Irrigation increased the predicted NPP carbon flux of grazed lands by 59 grams C per square meter annually compared to unmanaged grasslands. Low intensity grazing activity appeared to promote higher grass regrowth until June, compared to the ungrazed grassland sites. These modeling methods were shown to be successful in capturing the differing seasonal growing cycles of rangeland forage production across the area of individual ranch properties.
基金Funded by the National 973 Program of China(No.2006CB701302).
文摘With remote sensing information products becoming increasingly varied and arguably improved, scientific applications of such products rely on their quality assessment. In an operational context such as the NASA (National Aeronautics and Space Administration) information production based on the MODIS (Moderate Resolution Imaging Spectroradiometer) instrument on board Earth Observing System (EOS) Terra and Aqua satellites, efficient ways of detecting product anomaly, i.e., to discriminate between product artifacts and real changes in Earth processes being monitored, are extremely important to assist and inform the user communities about potential unreliability in the products. A technique for anomaly detection, known as MAD (the median of absolute deviate from the median), in MODIS land products via time series analysis is described, which can handle intra- and in-ter-annual variation in the data by using MAD statistics of the original data and their first-order difference. This method is shown to be robust and work across major land products, including NDVI, active fire, snow cover, and surface reflectance, and its applicabil-ity to multi-disciplinary products is anticipated.
基金Under the auspices of National Basic Research Program of China (No. 2010CB951304-5)National Natural Science Foundation of China (No. 41101545,41030743)
文摘In this study,the Surface Energy Balance Algorithms for Land(SEBAL) model and Moderate Resolution Imaging Spectroradiometer(MODIS) products from Terra satellite were combined with meteorological data to estimate evapotranspiration(ET) over the Sanjiang Plain,Northeast China.Land cover/land use was classified by using a recursive partitioning and regression tree with MODIS Normalized Difference Vegetation Index(NDVI) time series data,which were reconstructed based on the Savitzky-Golay filtering approach.The MODIS product Quality Assessment Science Data Sets(QA-SDS) was analyzed and all scenes with valid data covering more than 75% of the Sanjiang Plain were selected for the SEBAL modeling.This provided 12 overpasses during 184-day growing season from May 1st to October 31st,2006.Daily ET estimated by the SEBAL model was misestimaed at the range of-11.29% to 27.57% compared with that measured by Eddy Covariance system(10.52% on average).The validation results show that seasonal ET from the SEBAL model is comparable to that from ground observation within 8.86% of deviation.Our results reveal that the time series daily ET of different land cover/use increases from vegetation on-going until June or July and then decreases as vegetation senesced.Seasonal ET is lower in dry farmland(average(Ave):491 mm) and paddy field(Ave:522 mm) and increases in wetlands to more than 586 mm.As expected,higher seasonal ET values are observed for the Xingkai Lake in the southeastern part of the Sanjiang Plain(Ave:823 mm),broadleaf forest(Ave:666 mm) and mixed wood(Ave:622 mm) in the southern/western Sanjiang Plain.The ET estimation with SEBAL using MODIS products can provide decision support for operational water management issues.
文摘Satellite-derived surface temperature data is increasingly required to supplement the limited weather stations for the assessment of temperature trend over the data-sparse Antarctic Ice Sheet. To accomplish this, it is essential to assess the relationship and difference between satellite-based land-surface temperature (LST) retrieval and air temperature observation. In this study, we made a comparison between monthly averaged LST from Moderate Resolution Imaging Spectroradiometer (MODIS) and the corresponding air temperature at the nominal heights of 1 m and 2 m from automatic weather stations (AWSs) over the Lambert Glacier basin, East Antarctica. This comparison reveals a statistically significant correlation between the two types of temperature measurements with correlation coefficient (R) above 0.6. Also, the time difference between satellite overpass and air temperature observation is not critical for the R values. Although MODIS LST evidently deviates from air temperature (Mean difference fluctuates from 2.87°C to 8.08°C) probably due to the temperature inversion effect, heterogeneity in surface emissivity, representative of AWS measurements and satellite self limitation. MODIS LST measurements have a great potential for the accurate evaluation or monitoring of regional air temperature over Antarctica, and thus better improve current reconstruction of spatial and temporal reconstruction variability in Antarctic temperature.
文摘The accuracy of the cloud-aerosol lidar with orthogonal polarization (CALIOP), moderate resolution imaging spectroradiometer (MODIS), Multi-Angle Implementation of Atmospheric Correction (MAIAC), and Geostationary Operational Environmental Satellite (GOES) aerosol optical depth (AOD) products for the Arctic north of 59.75°N was examined by means of 35 aerosol robotic network (AERONET) AOD sites. The assessment for June to October 2006 to 2020 showed MAIAC AOD agreed the best with AERONET AOD;CALIOP AOD differed the strongest from the AERONET AOD. Cross-correlations of CALIOP AOD along the satellite path indicated that AOD-values 40 km up-and-down the path often failed to represent the AERONET AOD-values within ±30 min of the overpass in this region dominated by easterly winds. Typically, CALIOP AOD was lower than AERONET AOD and MAIAC AOD at the sites, especially, at sites with mean AOD below 0.1. Generally, MODIS AOD values exceeded those of MAIAC. Comparison of CALIOP, MAIAC, and MODIS products resampled on a 0.25° × 0.25° grid revealed differences among the products caused by their temporal and spatial resolution, sample habit and size. Typically, the MODIS AOD-product showed the most details in AOD distribution. Despite differences in AOD-values, all products provided similar temporal evolution of elevated and lower AOD.
文摘该文采用M OD IS N DV I时序数据对东北区土地覆盖分类进行研究,以验证M OD IS区域土地覆盖制图的可靠性。通过试验发现经过Sav izky-G o lay滤波处理能有效去除云、缺失数据及异常值的影响,使得N DV I时序曲线能更好的反映植被季相变化特征,分类结果表明N DV I时序数列能较好的区分植被与非植被、草本(一年生)与木本(多年生)覆盖类型。但研究区内一年一熟的农作物与高盖度草地、落叶针叶林与落叶阔叶林具有相似的物候特征,混分现象比较严重。该研究通过添加地表温度(land surface tem perature,LST)数据解决这一问题,利用所得温度/植被指数TV I对研究区进行土地覆盖分类。所得结果用363个野外调查样区进行验证,N DV I及TV I时序数据的分类精度分别为62.26%与71.63%。结果表明TV I比N DV I对土地覆盖类型中的植被类型识别更有效。
