基于MODIS GPP产品的冬小麦保险费率厘定方法研究

农作物保险是国内外减少灾害造成的种植户经济损失,保障农民基本生产收入的重要手段。国内传统的农作物保险费率是基于行政单元的统计数据厘定的,忽略了行政单元内部灾害的空间风险差异,因此如何获得行政单元内部农户级农作物纯保险费率,成为精细化农作物保险的关键问题。本文针对农户级的冬小麦纯保险费率,以河南省周口市为实验区,利用2005—2015年MODIS MOD17 A2 GPP总初级生产力数据产品生成2005—2015年冬小麦生长季的GPP数据,同时利用Landsat5/7/8 TM/ETM/OLI数据计算2005—2015年公里级的冬小麦种植面积比。通过Bühlmann-Straub模型和经验费率法厘定得到2016年实验区基于格网单元的冬小麦纯保险费率。研究表明：遥感数据可以为农作物保险空间精细费率厘定提供数据保障,利用遥感数据可以得到公里级格网单元的冬小麦纯保险费率。将利用遥感数据得到的农作物纯保险费率用于农作物保险中,提高了农作物保险的空间精细水平,可以进行基于地块的空间差异化农户投保,有利于政府针对不同农户制定合理的农作物保险政策,保险公司合理的收取保费。     

基于遥感指数的中国南亚热带常绿林光合作用季节动态变化研究（英文）

准确监测中国南亚热带常绿林生态系统光合作用动态变化对全球陆地生态系统碳吸收估计及其对气候变化的响应至关重要。涡动协方差技术一直被认为是评估生态系统碳通量最直接的方法,虽然具有较高的时间分辨率,但在空间上有其自身的局限性。近10年,光谱观测和卫星遥感技术在植被生产力监测方面的应用大大提高了对碳通量的时空评估能力。本研究基于长时间序列光谱观测数据,提取叶绿素荧光指数(FRI)和光化学植被指数(PRI),进而评价两个生理遥感指数跟踪亚热带常绿林光合作用季节动态变化的能力。结果表明,传统NDVI指数受光照条件影响较大(R^2=0.88,p<0.001),并呈现出饱和现象,而FRI和PRI指数则能较好地跟踪植物光和功能季节性变化,且在季节尺度上两者受光照条件的影响相对较弱(FRI指数R^2=0.13;PRI指数R^2=0.51);相比PRI指数与光能利用效率(LUE)在午间具有较强的相关性,FRI指数与GPP的相关性则在早上优于午间时段;而这两种相关关系均在植被衰退季优于植被生长季。另外,通过考虑光合有效辐射因子,基于FRI指数监测GPP的能力得到显著提高,R 2从0.22提高到0.69,呈显著正相关关系(p<0.001);同时,在植被衰退季也呈现出更强的相关性(R^2=0.79,p<0.001)。研究成果表明,FRI和PRI两个生理遥感指数能够准确地监测亚热带常绿林光合作用季节动态变化,建议把其引入碳循环模型中以改进区域碳收支估计。     

Remote Sensing Indices to Measure the Seasonal Dynamics of Photosynthesis in a Southern China Subtropical Evergreen Forest

The accurate measurement of the dynamics of photosynthesis in China’s subtropical evergreen forest ecosystems is an important contribution to carbon (C) sink estimates in global terrestrial ecosystems and their responses to climate change. Eddy covariance has historically been the only direct method to assess C flux of whole ecosystems with high temporal resolution, but it suffers from limited spatial resolution. During the last decade, continuous global monitoring of plant primary productivity from spectroradiometer sensors on flux towers and satellites has extended the temporal and spatial coverage of C flux observations. In this study, we evaluated the performance of two physiological remote sensing indices, fluorescence reflectance index (FRI) and photochemical reflectance index (PRI), to measure the seasonal variations of photosynthesis in a subtropical evergreen forest ecosystem using continuous canopy spectral and flux measurements in the Dinghushan Nature Reserve in southern China. The more commonly used NDVI has been shown to be saturated and mainly affected by illumination (R²=0.88, p < 0.001), but FRI and PRI could better track the seasonal dynamics of plant photosynthetic functioning by comparison and are less affected by illumination (R²=0.13 and R²=0.51, respectively) at the seasonal scale. FRI correlated better with daily gross primary production (GPP) in the morning hours than in the afternoon hours, in contrast to PRI which correlated better with light-use efficiency (LUE) in the afternoon hours. Both FRI and PRI could show greater correlations with GPP and LUE respectively in the senescence season than in the recovery-growth season. When incident PAR was taken into account, the relationship between GPP and FRI was improved and the correlation coefficient increased from 0.22 to 0.69 (p < 0.001). The strength of the correlation increased significantly in the senescence season (R ²=0.79, p < 0.001). Our results demonstrate the application of FRI and PRI as physiological indices for the accurate measurement of the seasonal dynamics of plant community photosynthesis in a subtropical evergreen forest, and suggest these indices may be applied to carbon cycle models to improve the estimation of regional carbon budgets.     

Integration of ground and satellite data to model Mediterranean forest processes

The current work presents the testing of a modeling strategy that has been recently developed to simulate the gross and net carbon fluxes of Mediterranean forest ecosystems. The strategy is based on the use of a NDVI-driven parametric model, C-Fix, and of a biogeochemical model, BIOME-BGC, whose outputs are combined to simulate the behavior of forest ecosystems at different development stages. The performances of the modeling strategy are evaluated in three Italian study sites (San Rossore, Lecceto and Pianosa), where carbon fluxes are being measured through the eddy correlation technique. These sites are characterized by variable Mediterranean climates and are covered by different types of forest vegetation (pine wood, Holm oak forest and Macchia, respectively). The results of the tests indicate that the modeling strategy is generally capable of reproducing monthly GPP and NEE patterns in all three study sites. The highest accuracy is obtained in the most mature, homogenous pine wood of San Rossore, while the worst results are found in the Lecceto forest, where there are the most heterogeneous terrain, soil and vegetation conditions. The main error sources are identified in the inaccurate definition of the model inputs, particularly those regulating the site water budgets, which exert a strong control on forest productivity during the Mediterranean summer dry season. In general, the incorporation of NDVI-derived fAPAR estimates corrects for most of these errors and renders the forest flux simulations more stable and accurate.     

A GPP assimilation model for the southeastern Tibetan Plateau based on CO2 eddy covariance flux tower and remote sensing data

The gross primary production (GPP) at individual CO₂ eddy covariance flux tower sites (GPP_Tower) in Dali (DL), Wenjiang (WJ) and Linzhi (LZ) around the southeastern Tibetan Plateau were determined by the net ecosystem exchange of CO₂ (NEE) and ecosystem respiration (R_e). The satellite remote sensing-VPM model estimates of GPP values (GPP_MODIS) used the satellite-derived 8-day surface reflectance product (MOD09A1), including satellite-derived enhanced vegetation index (EVI) and land surface water index (LSWI). In this paper, we assembled a subset of flux tower data at these three sites to calibrate and test satellite-VPM model estimated GPP_MODIS, and introduced the satellite data and site-level environmental factors to develop four new assimilation models. The new assimilation models’ estimates of GPP values were compared with GPP_MODIS and GPP_Tower, and the final optimum model among the four assimilation models was determined and used to calibrate GPP_MODIS. The results showed that GPP_MODIS had similar temporal variations to the GPP_Tower, but GPP_MODlS were commonly higher in absolute magnitude than GPP_Tower with relative error (RE) about 58.85%. While, the assimilation models’ estimates of GPP values (GPP_MODEL) were much more closer to GPP_Tower with RE approximately 6.98%, indicating that the capacity of the simulation in the new assimilation model was greatly improved, the R² and root mean square error (RMSE) of the new assimilation model were 0.57–4.90% higher and 0.74–2.47 g C m⁻² s⁻¹ lower than those of the GPP_MODIS, respectively. The assimilation model was used to predicted GPP dynamics around the Tibetan Plateau and showed a reliable result compared with other researches. This study demonstrated the potential of the new assimilation model for estimating GPP around the Tibetan Plateau and the performances of site-level biophysical parameters in related to satellite-VPM model GPP.     

Linking satellite-based spring phenology to temperate deciduous broadleaf forest photosynthesis activity

Satellite-based remote sensed phenology has been widely used to assess global climate change. However, it is constrained by uncertain linkages with photosynthesis activity. Two dynamic threshold methods were employed to retrieve spring phenology metrics from four Moderate Resolution Imaging Spectroradiometer (MODIS) products, including fraction of Absorbed Photosynthetically Active Radiation (fAPAR), Leaf Area Index (LAI), Normalized Difference Vegetation Index (NDVI), and Enhanced Vegetation Index (EVI) for three temperate deciduous broadleaf forests in North America between 2001 and 2009. These MODIS-based spring phenology metrics were subsequently linked to the photosynthetic curves (daily gross primary productivity, GPP) measured by an eddy covariance flux tower. The 20% dynamic threshold spring onset metrics from MODIS products were closer to the photosynthesis onset metrics at the date of 2% GPP increase for NDVI and fAPAR, and closer to the date of 5% and 10% increase of GPP for EVI and LAI, respectively. The 50% dynamic threshold onset metrics were closer to the photosynthesis onset metrics at the date of 10% GPP increase for NDVI, and closer to the date of 20% GPP increase for fAPAR, LAI and EVI, respectively. These results can improve our knowledge on the photosynthesis activity status of remotely sensed spring phenology metrics.     

陆地总初级生产力遥感估算精度分析

准确估算陆地总初级生产力GPP(Gross Primary Productivity)数值对碳循环过程模拟有重要影响。本文介绍了多种基于植被指数以及基于光能利用率的遥感GPP算法,综述了不同算法在其研究区域的估算精度;并分析了MODIS/GPP以及BESS/GPP两种遥感GPP产品在不同植被类型的估算精度。通过对比全球碳通量站网络GPP数据表明,MODIS/GPP产品在全球估算结果具显著相关性(R2=0.59)及中等标准误差(RMSE=2.86 g C/m2/day),估算精度较高的植被类型有落叶阔叶林,草地等;估算精度较低类型包括常绿阔叶林,稀树草原等。本文对GPP产品中存在的不确定性进行分析,通过综述前人研究中发现的遥感估算GPP方法中存在的问题,指出可能的提高卫星遥感GPP产品估算精度的方法及发展趋势。     

Links between Western Pacific Subtropical High and vegetation growth in China

There is a lack of simple ways to predict the vegetation responses to the East Asian Monsoon (EAM) variability in China due to the complexity of the monsoon system. In this study, we found the variation of the Western Pacific Subtropical High (WPSH), which is one of the major components of the EAM, has a profound influence on the vegetation growth in China. When the WPSH is located more to the west of its climate average, the eastern and northwestern parts experience increased yearly-averaged normalized difference vegetation index (NDVI) and gross primary productivity (GPP) by 0.3%–2.2%, and 0.2%–2.2%, respectively. In contrast, when the WPSH is located more to the east of its climate average, the above areas experience decreased yearly-averaged NDVI and GPP by 0.4% to 1.6%, and 1.3% to 4.5%, respectively. The WPSH serves as a major circulation index to predict the response of vegetation to monsoon.     

基于涡度相关通量数据的植被最大光能利用率反演研究

准确估计和预测陆地生态系统碳循环时空变化是预测气候变化的基础,也是目前全球变化研究中最为重要的前沿领域之一。最大光能利用率（εmax）是遥感估算陆地生态系统初级生产力的关键参数之一,本研究基于CASA（Carnegie-Ames-Stanford Approach）模型,采用马尔科夫链-蒙特卡罗（Markov Chain Monte Carlo,简称MCMC）方法,利用中国陆地生态系统通量观测研究网络（ChinaFLUX）8个野外台站的涡度相关通量观测数据对εmax进行反演,得到εmax的最优估计值及其不确定性,并利用优化后的εmax对2003~2008年各生态系统总初级生产力（Gross Primary Productivity,简称GPP）及其不确定性进行了模拟。结果表明：8个生态系统εmax后验估计结果均呈近似正态分布,森林、农田和草地生态系统εmax分别为0.737±0.026~0.850±0.035gC/MJ·PAR,1.056±0.090gC/MJ·PAR和0.199±0.068~0.469±0.043gC/MJ·PAR;εmax估计的不确定性将导致内蒙、当雄和海北草地生态系统GPP年总量的模拟值产生9.17%~14.20%的误差,长白山、鼎湖山、千烟洲和西双版纳4个森林生态系统GPP年总量的误差为3.52%~7.79%,禹城农田生态系统GPP年总量的误差为8.52%。对εmax进行优化后,GPP年总量模拟值的相对误差显著降低,有效改善了原模型对内蒙草地生态系统GPP年总量的高估和除当雄草地生态系统外其他6个生态系统GPP年总量的低估。