Estimation of net primary productivity in China using remote sensing data

It is significant to estimate terrestrial net primary productivity (NPP) accurately not only for global change research, but also for natural resources management to achieve sustainable development. Remote sensing data can describe spatial distribution of plant resources better. So, in this paper an NPP model based on remote sensing data and climate data is developed. And 1km resolution AVHRR NDVI data are used to estimate the spatial distribution and seasonal change of NPP in China. The results show that NPP estimated using remote sensing data are more close to truth. Total annual NPP in China is 2.645X109 tC. The spatial distribution of NPP in China is mainly affected by precipitation and has the trend of decreasing from southeast to northwest.

Estimation of Net Primary Productivity of Terrestrial Vegetation in China by Remote Sensing

Among the many approaches for studying the net primary productivity (NPP), a new method by using remote sensing was introduced in this paper. With spectral information source (the visible band, near infrared band and thermal infrared band) of NOAA-AVHRR, we can get the relative index and parameters, which can be used for estimating NPP of terrestrial vegetation. By means of remote sensing, the estimation of biomass and NPP is mainly based on the models of light energy utilization. In other words, the biomass and NPP can be calculated from the relation among NPP, absorbed photosynthetical active radiation (APAR) and the rate (epsilon) of transformation of APAR to organic matter, thus: NPP = ( FPAR x PAR) x [epsilon * x sigma (T) x sigma (E) x sigma (S) x (1 - Y-m) x (1 - Y-g)]. Based upon remote sensing ( RS) and geographic information system (GIS), the NPP of terrestrial vegetation in China in every ten days was calculated, and the annual NPP was integrated. The result showed that the total NPP of terrestrial vegetation in China was 6.13 x 10(9) t C . a(-1) in 1990 and the maximum NPP was 1 812.9 g C/m(2). According to this result, the spatio-temporal distribution of NPP was analyzed. Comparing to the statistical models, the RS model, using area object other than point one, can better reflect the distribution of NPP, and match the geographic distribution of vegetation in China.

Dynamic change of net primary productivity and fractional vegetation cover in the Yellow River Basin using multi-temporal AVHRR NDVI Data

An exponential relationship between net primary productivity (NPP) and integrated NDVI has been found in this paper. Based on the relationship and using multi-temporal 8 km resolution NOAA AVHRR-NDVI data, the spatial distribution and dynamic change of NPP and fractional vegetation cover in the Yellow River Basin from 1982 to 1999 are analyzed. Finally, the effect of rainfall on NDVI is examined. Results show that mean NPP and fractional vegetation cover have an inclining trend for the whole basin, and rainfall in flood season influences vegetation cover most.

Improving remote sensing-based net primary production estimation in the grazed land with defoliation formulation model

Remote sensing(RS) technologies provide robust techniques for quantifying net primary productivity(NPP) which is a key component of ecosystem production management. Applying RS, the confounding effects of carbon consumed by livestock grazing were neglected by previous studies, which created uncertainties and underestimation of NPP for the grazed lands. The grasslands in Xinjiang were selected as a case study to improve the RS based NPP estimation. A defoliation formulation model(DFM) based on RS is developed to evaluate the extent of underestimated NPP between 1982 and 2011. The estimates were then used to examine the spatiotemporal patterns of the calculated NPP. Results show that average annual underestimated NPP was 55.74 gC·m^(-2)yr^(-1) over the time period understudied, accounting for 29.06% of the total NPP for the Xinjiang grasslands. The spatial distribution of underestimated NPP is related to both grazing intensity and time. Data for the Xinjiang grasslands show that the average annual NPP was 179.41 gC·m^(-2)yr^(-1), the annual NPP with an increasing trend was observed at a rate of 1.04 gC·m^(-2)yr^(-1) between 1982 and 2011. The spatial distribution of NPP reveals distinct variations from high to low encompassing the geolocations of the Tianshan Mountains, northern and southern Xinjiang Province and corresponding with mid-mountain meadow, typical grassland, desert grassland, alpine meadow, and saline meadow grassland types. This study contributes to improving RS-based NPP estimations for grazed land and provides a more accurate data to support the scientific management of fragile grassland ecosystems in Xinjiang.

Spatio-temporal distribution of net primary productivity along the northeast China transect and its response to climatic change

An improved Carnegie Ames Stanford Approach model （CASA model） was used to estimate the net primary productivity （NPP） of the Northeast China Transect （NECT） every month from 1982 to 2000. The spatial-temporal distribution of NPP along NECT and its response to climatic change were also analyzed. Results showed that the change tendency of NPP spatial distribution in NECT is quite similar to that of precipitation and their spatial correlation coefficient is up to 0.84 （P 〈 0.01）. The inter-annual variation of NPP in NECT is mainly affected by the change of the aestival NPP every year, which accounts for 67.6% of the inter-annual increase in NPP and their spatial correlation coefficient is 0.95 （P 〈 0.01）. The NPP in NECT is mainly cumulated between May and September, which accounts for 89.8% of the annual NPP. The NPP in summer （June to August） accounts for 65.9% of the annual NPP and is the lowest in winter. Recent climate changes have enhanced plant growth in NECT. The mean NPP increased 14.3% from 1980s to 1990s. The inter-annual linear trend of NPP is 4.6 gC·m^-2·a^-1, and the relative trend is 1.17%, which owns mainly to the increasing temperature.

Assessing the uncertainties of phytoplankton absorption-based model estimates of marine net primary productivity

Satellite-derived phytoplankton pigment absorption （aph） has been used as a key predictor of phytoplankton photosynthetic efficiency to estimate global ocean net primary production （NPP）. In this study, an aph-based NPP model （AbPM） with four input parameters including the photosynthetically available radiation （PAR）, diffuse attenuation at 490 nm （Ka（490））, euphotic zone depth （Zeu） and the phytoplankton pigment absorption coefficient （aph） is compared with the chlorophyll-based model and carbon-based model. It is found that the AbPM has significant advantages on the ocean NPP estimation compared with the chlorophyll-based model and carbon- based model. For example, AbPM greatly outperformed the other two models at most monitoring sites and had the best accuracy, including the smallest values of RMSD and bias for the NPP estimate, and the best correlation between the observations and the modeled NPPs. In order to ensure the robustness of the model, the uncertainty in NPP estimates of the AbPM was assessed using a Monte Carlo simulation. At first, the frequency histograms of simple difference （fi）, and logarithmic difference （~LOG） between model estimates and in situ data confirm that the two input parameters （Zeu and PAR） approximate the Normal Distribution, and another two input parameters （aph and Ka（490）） approximate the logarithmic Normal Distribution. Second, the uncertainty in NPP estimates in the AbPM was assessed by using the Monte Carlo simulation. Here both the PB （percentage bias）, defined as the ratio of ANPP to the retrieved NPP, and the CV （coefficient of variation）, defined as the ratio of the standard deviation to the mean are used to indicate the uncertainty in the NPP brought by input parameter to AbPM model. The uncertainty related to magnitude is denoted by PB and the uncertainty related to scatter range is denoted by CV. Our investigations demonstrate that PB of NPP uncertainty brought by all parameters with an annual mean of 5.5% covered a range of -5%-15% for the global ocean. The PB uncertainty of AbPM model was mainly caused by aph; the PB of NPP uncertainty brought by aph had an annual mean of 4.1% for the global ocean. The CV brought by all the parameters with an annual mean of 105% covered a range of 98%-134% for global ocean. For the coastal zone of Antarctica with higher productivity, the PB and CV of NPP uncertainty brought by all parameters had annual means of 7.1% and 121%, respectively, which are significantly larger than those obtained in the global ocean. This study suggests that the NPPs estimated by AbPM model are more accurate than others, but the magnitude and scatter range of NPP errors brought by input parameter to AbPM model could not be neglected, especially in the coastal area with high productivity. So the improving accuracy of satellite retrieval of input parameters should be necessary. The investigation also confirmed that the SST related correction is effective for improving the model accuracy in low temperature condition.

Spatio-temporal Pattern of Ecosystem Pressure in Countries Along the Belt and Road: Combining Remote Sensing Data and Statistical Data

Building a Green Silk Road by integrating the Sustainable Development Goals(SDGs) is one of the Belt and Road Initiative(BRI) visions, but the BRI faces enormous challenge that is the conflict between economic development and ecological sustainability.Understanding the current scale and trend of the impact of human activities on the ecosystem is the preliminary work to ensure that human activities do not exceed the ecological carrying capacity under the BRI. This study evaluated the ecosystem pressure in countries along the Belt and Road(B&R) from 2000–2017 based on the supply-consumption balance relationship of ecological resources. Net primary productivity(NPP) is taken as the measure of ecological resources, and the supply level and consumption intensity of ecological resources is estimated based on remote sensing data and statistical data, respectively. Results show that thirteen countries with overconsumed ecological resources concentrated in the West Asia/Middle East. Although the intensity of the ecological resource consumption correlated with ecological resource endowments, the ecosystem pressure was determined by social development dependence on the ecological resources at the same ecological resource endowments level. Nearly 80% of countries along the B&R suffered from significantly increased(P < 0.05) ecosystem pressure during 2000–2017, since most of the countries along the B&R were developing countries,and their economic development was highly dependent on ecological resources. Some West Asia/Middle East countries successfully mitigated the ecosystem pressure by importing feed for livestock. Likewise, the Southeast Asian islands benefitted from the import of agricultural products. The results highlight that the BRI should reduce the dependence of social development demands on local ecological resources by international trade for ensuring the increasing ecosystem pressure trend within the ecological carrying capacity.

Estimation of ocean primary productivity and its spatio-temporal variation mechanism for East China Sea based on VGPM model

According to calculation results of ocean chlorophyll concentration based on SeaWiFS data by SeaBAM model and synchronous ship-measured data, this research set up an improved model for CaseⅠand CaseⅡwater bodies respectively. The monthly chlorophyll distribution in the East China Sea in 1998 was obtained from this improved model on calculation results of SeaBAM. The euphotic depth distribution in 1998 in the East China Sea is calculated by using remote sensing data of K 490 from SeaWiFS according to the relation between the euphotic depth and the oceanic diffuse attenuation coefficient. With data of ocean chlorophyll concentration, euphotic depth, ocean surface photosynthetic available radiation (PAR), daily photoperiod and optimal rate of daily carbon fixation within a water column, the monthly and annual primary productivity spatio-temporal distributions in the East China Sea in 1998 were obtained based on VGPM model. Based on analysis of those distributions, the conclusion can be drawn that there is a clear bimodality character of primary productivity in the monthly distribution in the East China Sea. In detail, the monthly distribution of primary productivity stays the lowest level in winter and rises rapidly to the peak in spring. It gets down a little in summer, and gets up a little in autumn. The daily average of primary productivity in the whole East China Sea is 560.03 mg/m 2 /d, which is far higher than the average of subtropical ocean areas. The annual average of primary productivity is 236.95 g/m 2 /a. The research on the seasonal variety mechanism of primary productivity shows that several factors that affect the spatio-temporal distribution may include the chlorophyll concentration distribution, temperature condition, the Yangtze River diluted water variety, the euphotic depth, ocean current variety, etc. But the main influencing factors may be different in each local sea area.

Assessing the Response of Seasonal Variation of Net Primary Productivity to Climate Using Remote Sensing Data and Geographic Information System Techniques in Xinjiang

Net primary productivity （NPP） is a key component of energy and matter transformation in the terrestrial ecosystem, and the responses of NPP to global change locally and regionally have been one of the most important aspects in climatevegetation relationship studies. In order to isolate causal climatic factors, it is very important to assess the response of seasonal variation of NPP to climate. In this paper, NPP in Xinjiang was estimated by NOAA/AVHRR Normalized Difference Vegetation Index （NDVI） data and geographic information system （GIS） techniques. The impact of climatic factors （air temperature, precipitation and sunshine percentage） on seasonal variations of NPP was studied by time lag and serial correlation ageing analysis. The results showed that the NPP for different land cover types have a similar correlation with any one of the three climatic factors, and precipitation is the major climatic factor influencing the seasonal variation of NPP in Xinjiang. It was found that the positive correlation at 0lag appeared between NPP and precipitation and the serial correlation ageing was 0 d in most areas of Xinjiang, which indicated that the response of NPP to precipitation was immediate. However, NPP of different land cover types showed significant positive correlation at 2 month lag with air temperature, and the impact of which could persist 1 month as a whole. No correlation was found between NPP and sunshine percentage.

Satellite remote sensing of ultraviolet irradiance on the ocean surface

Ultraviolet （UV） radiation has a significant influence on marine biological processes and primary productivity; however, the existing ocean color satellite sensors seldom contain UV bands. A look-up table of wavelength- integrated UV irradiance （280-400 nm） on the sea surface is established using the coupled ocean atmosphere radiative transfer （COART） model. On the basis of the look-up table, the distributions of the UV irradiance at middle and low latitudes are inversed by using the satellite-derived atmospheric products from the Aqua satellite, including aerosol optical thickness at 550 nm, ozone content, liquid water path, and the total precipitable water. The validation results show that the mean relative difference of the 10 d rolling averaged UV irradiance between the satellite retrieval and field observations is 8.20% at the time of satellite passing and 13.95% for the daily dose of UV. The monthly-averaged UV irradiance and daily dose of UV retrieved by satellite data show a good correlation with the in situ data, with mean relative differences of 6.87% and 8.43%, respectively. The sensitivity analysis of satellite inputs is conducted. The liquid water path representing the condition of cloud has the highest effect on the retrieval of the UV irradiance, while ozone and aerosol have relatively lesser effect. The influence of the total precipitable water is not significant. On the basis of the satellite-derived UV irradiance on the sea surface, a preliminary simple estimation of ultraviolet radiation＇s effects on the global marine primary productivity is presented, and the results reveal that ultraviolet radiation has a non-negligible effect on the estimation of the marine primary productivity.

Modeling Gross Primary Production by Integrating Satellite Data and Coordinated Flux Measurements in Arid and Semi-Arid China

Assessing large-scale patterns of gross primary production (GPP) in arid and semi-arid (ASA) areas is important for both scientific and practical purposes.Remote sensing-based models,which integrate satellite data with input from ground-based meteorological measurements and vegetation characteristics,improve spatially extended estimates of vegetation productivity with high accuracy.In this study,the authors simulated GPP in ASA areas by integrating moderate resolution imaging spectral radiometer (MODIS) data with eddy covariance and meteorological measurements at the flux tower sites using the Vegetation Photosynthesis Model (VPM),which is a remote sensing-based model for analyzing the spatial pattern of GPP in different land cover types.The field data were collected by coordinating observations at nine stations in 2008.The results indicate that in the region during the growing season GPP was highest in cropland sites,second highest in woodland sites,and lowest in grassland sites.VPM captured the temporal and spatial characteristics of GPP for different land covers in ASA areas.Further,Enhanced Vegetation Index (EVI) had a strong liner relationship with GPP in densely vegetated areas,while the Normalized Difference Vegetation Index (NDVI) had a strong liner relationship with GPP over less dense vegetation.This study demonstrates the potential of satellite-driven models for scaling-up GPP,which is a key component for studying the carbon cycle at regional and global scales.

Spatial scaling of net primary productivity model based on remote sensing

Spatial scaling for net primary productivity （NPP） refers to the transferring process of establishing quantitative correlation between simulated NPP derived from data at different spatial resolutions. How to transfer NPP at one scale by the algorithm with smaller error to at another is the urgent problem. Nonlinearity and effects from land cover type are two main problems in NPP scaling. In this paper, the contextural approach based on mixed pixels and support vector machine （SVM） algorithm are used to make the scaling model from the fine resolution （TM） to the coarse resolution （MODIS）. Spatial scaling from NPP retrieved from fine resolution data to NPP derived from coarse resolution images is performed, and the correction of scale effect to NPP retrieved from coarse resolution data of MODIS is accomplished. The result shows that the correlation between Rj_coereted of the correction factor for scale effect and 1-Fmiddle dessity grassland estimated by SVM regression model is higher （R2=0.81）. Before the correction for scale effect, the correlation between NPPMODIS and NPPTM is lower （R2=0.69; RMSE=3.47）, while the correlation between NPPTM and corrected NPPMODIS_corrected is higher （R2=0.84; RMSE= 1.87）. Therefore, NPP corrected for scale effect has been greatly improved in both correlation and error.

Influence of mesoscale eddies on primary production in the South China Sea during spring inter-monsoon period

Mesoscale eddies have been suggested to have an impact on biological carbon fixation in the South China Sea （SCS）. However, their overall contribution to primary production during the spring inter-monsoon pe riod is still unknown. Based on large-scale biological and environmental in situ observations and synchro nous remote sensing data, the distribution patterns of phytoplankton biomass and the primary production, and the role of mesoscale eddies in regulating primary production in different eddy-controlled waters were investigated. The results suggested that the surface chlorophyll a concentrations and water column inte grated primary production （IPP） are significantly higher in cyclonic eddies and lower in the anticyclonic eddies as compared to that in non-eddy waters. Although eddies could affect various environmental factors, such as nutrients, temperature and light availability, nutrient supply is suggested to be the most important one through which mesoscale eddies regulated the distribution patterns of phytoplankton biomass and pri mary production. The estimated IPP in cyclonic and anticyclonic eddies are about 29.5% higher and 16.6% lower than the total average in the whole study area, respectively, indicating that the promotion effect of mesoscale cold eddies on the primary production was much stronger than the inhibition effect of the warm eddies per unit area. Overall, mesoscale eddies are crucial physical processes that affect the biological car bon fixation and the distribution pattern of primary production in the SCS open sea, especially during the spring inter-monsoon period.

Decoupling between Plant Productivity and Growing Season Length under a Warming Climate in Canada’s Arctic

Given the short duration of growing season in the Arctic, a strong correlation between plant productivity and growing season length (GSL) is conventionally assumed. Will this assumption hold true under a warming climate? In this study, we addressed the question by investigating the relationship between net primary productivity of leaves (NPPleaf) and GSL for various tundra ecosystems. We quantified NPPleaf and GSL using long-term satellite data and field measurements. Our results indicated that the relationship was not significant (i.e., decoupled) for 44% to 64% of tundra classes in the southern Canadian Arctic, but significant for all classes in the northern Canadian Arctic. To better understand the causes of the decoupling, we further decomposed the relationship into two components: the correspondence of interannual variations and the agreement of long- term trends. We found that the longer the mean GSL for a tundra class, the poorer the correspondence between their interannual variations. Soil moisture limitation further decoupled the relationship by deteriorating the agreement of long-term trends. Consequently, the decoupling between NPPleaf and GSL would be more likely to occur under a warming climate if the tundra class had a mean GSL > 116 (or 123) days with a dry (or moist) soil moisture regime.

Cropland Dynamics and Their Influence on the Productivity in Northern Shaanxi,China,for the Past 20 Years:Based on Remotely Sensed Data

In this paper,we mainly focused our research on northern Shaanxi district,which is a pilot area of the Grain for Green Project.We compared the spatial distribution patterns of croplands and their productivity for the past 20 years（from the end of the 1980 s to 2010）.Cropland dynamics for the past 20 years were interpreted from medium- and high-resolution remote sensing images（Landsat TM/ETM＋）.In addition,using the GLO-PEM and AGRO-VPM models with a medium resolution and long time series remote sensing dataset（AVHRR/MODIS）,net primary productivity（NPP） and its relationship with cropland were estimated.Finally,the effect of cropland change on productivity was analyzed.The results show that during the first decade of the research period,cropland area and productivity in northern Shaanxi experienced a small boost,while in the latter decade,both cropland area and NPP were significantly reduced.The main cause of the increase in cropland was the reclamation of large area of grassland and unutilized land to meet the food demands of the local population as well as to compensate for the occupation of urban constructions.While the main cause of the decrease in cropland was the implementation of the Grain for Green Project.In addition,urbanization was also a key factor.Overall,during the past 20 years,the total area of cropland in northern Shaanxi decreased by 42.56%,and cropland NPP dropped by 41.90%.This study is of great importance for the assessment of regional cropland security,food security and scientific planning of regional land use.

中亚地区2001-2020年250 m及2020年30 m分辨率植被生长季NDVI数据集

中亚地区是北半球最大的干旱和半干旱区,其生态环境十分脆弱,对全球气候变化的响应较为敏感。由于该区域的特殊地理位置,维护该区域生态系统的稳定对全球经济社会发展至关重要。植被具有重要的生态环境指示作用,其时空分布格局和变化趋势是评估区域生态状况的重要指标。归一化植被指数(Normalized Difference Vegetation Index,NDVI)作为研究植被最常用的遥感指数之一,能够表征植被的时空变化特征。本数据集利用MODIS13Q1产品生成了中亚地区2001–2020年长时间序列空间分辨率为250 m的生长季均值NDVI数据,并使用基于规则的分段回归Cubist算法,结合Landsat数据,融合得到了能够更好表征地物细节的30 m空间分辨率的2020年生长季均值NDVI数据。同时,本数据集从数据源的质控,模型训练优化,以及模型独立验证三个方面对数据产品进行质量控制,以确保数据的精度和可靠性。本数据集的生成为中亚地区植被动态变化和空间格局的分析提供了有力的数据支持。

基于时空数据融合的塔吉克斯坦中高时空分辨率NDVI数据集(2010-2020)

归一化植被指数(Normalized Difference Vegetation Index,NDVI)是研究植被最常用的遥感指数之一。NDVI长时间序列数据对于植被变化研究有着重要的意义。然而由于传感器的限制,遥感数据的时间分辨率与空间分辨率不能兼顾,因此在目前广泛使用的NDVI数据产品中,高时空分辨率的数据还较为缺乏。本产品基于Cubist模型对MODIS数据与Landsat及哨兵等遥感数据进行时空数据融合,得到了塔吉克斯坦2010–2020年中高时空分辨率Landsat-MODIS融合数据,以及2020年中高时空分辨率Sentinel-MODIS融合数据。为保证数据的准确性和可靠性,本数据集从数据源的质控,模型训练优化,以及模型独立验证三个方面对数据产品进行质量控制,且取得了较好的验证效果。本数据集可反映塔吉克斯坦2010–2020年NDVI时空变化情况,可为该地区植被变化分析、生态环境监测等提供长时间序列数据支撑。

多源潜在蒸散发产品在雅鲁藏布江流域的适用性评估与融合

基于2001—2018年监测站点观测的蒸发皿数据,分别在站点尺度和流域尺度上对GLEAM、MOD16A2、GLDAS_Noah和ERA5共4种遥感潜在蒸散发产品进行评估,选出适应性较好的3种遥感产品,运用Triple Collocation方法进行融合,并分析其时空变化特征。结果表明:在站点尺度上,ERA5遥感产品相关性较为显著(相关系数CC=0.72),精确度最高(相对偏差Bias=-22.48%,均方根误差RMSE=39.24 mm/月),更适用于雅鲁藏布江流域,MOD16A2和GLDAS_Noah次之;MOD16A2、GLDAS_Noah和ERA5分别占融合数据PET_(TC)的31.12%、30.64%和38.24%,对比PET_(TC)与3种遥感产品,PET_(TC)融合数据在流域内精度有所提高;融合数据PET_(TC)的潜在蒸散发峰值出现在2009年,雅鲁藏布江流域多年平均潜在蒸散发呈现从中部向上、下游逐渐减小的趋势,在流域东南角出现潜在蒸散发量最大值。在雅鲁藏布江流域内获得更精准的潜在蒸散发并揭示其变化规律,可为研究流域水资源的供需平衡和生态系统的稳定性提供数据支撑。

新疆天山北坡城市群土地覆被数据产品精度评价

土地覆被产品为全球各种地球系统科学应用提供了重要的地表覆被信息,如CLCD30(30 m)、GlobeLand30(30 m,简称Globe30)、GLC_FCS30(30 m)、FROM-GLC10(10 m)、Esri_Land_Cover_2020(10 m,简称Esri10),以及ESAWorldCover2020(10 m,简称ESA10),然而其在局地的精度和适用性如何尚不明确。基于Sentinel-2影像,通过样本精度评价和类别混淆评价对上述6套土地覆被数据产品在天山北坡城市群的精度及误差情况进行了研究和分析,并探讨了误差成因和不同数据产品的适用性。结果表明:(1)6套数据产品中,除Esri10外,其余5套数据产品的类型构成、面积占比相对一致。(2)GLC-FCS30、Globe30、CLCD30、FROM-GLC10、ESA10、Esri10的总体精度分别为0.8080、0.8147、0.7880、0.8531、0.8047、0.4725。(3)从产品适用性来看,GLC_FCS30适用于对耕地、裸地的分析,CLCD30适用于对林地、裸地的分析,FROM-GLC10适用于对草地、水体、冰/雪以及建筑的分析,ESA10适用于对耕地和草地的分析,Esri10适用于对林地、冰/雪以及建筑的分析,Globe30在各类别精度评价结果上更均衡。(4)类别混淆主要是耕地、林地以及草地之间及与其他类别的相互混淆,尤其是在土地覆被复杂的地区,如城市边缘区域。

水利遥感数据规模化加工处理平台框架研究

以卫星遥感为代表的地表信息空天获取手段是支撑新时期智慧水利建设的重要途径。当前,遥感数据获取能力与处理服务能力之间出现了失衡,大量的对地观测卫星遥感数据被获取,而能够支撑水利业务的遥感数据产品即时服务能力和动态更新频次仍然存在瓶颈,“卫星数据量大、处理流程复杂、产品服务不足”的现状长期存在,制约着水利行业遥感应用的质量和效率。本文通过调研当前国内外遥感大数据加工处理进展,分析了面向智慧水利建设的水利遥感数据处理平台需求,设计了一种水利遥感数据规模化加工处理平台的框架,用于海量卫星遥感数据产品全链路自动化处理,以提升水利遥感数据产品处理质量和服务效率。该平台可以实现超过30种多源高分遥感数据全流程处理,通过动态分配GPU和CPU计算缓存,实现卫星遥感数据正射校正、融合、镶嵌、质检以及水利遥感专题信息提取等流程规模化、并行化和自动化处理,生产满足数字孪生流域建设的卫星遥感专题产品,支撑新时期智慧水利建设的遥感深度应用。