Please wait a minute...
img

官方微信

遥感技术与应用  2022, Vol. 37 Issue (5): 1267-1276    DOI: 10.11873/j.issn.1004-0323.2022.5.1267
遥感应用     
基于植被指数的若尔盖高原湿地光合有效辐射吸收比例估算研究
袁艺溶(),王继燕(),杨嘉葳,熊俊楠
西南石油大学 土木工程与测绘学院,测绘遥感地理信息防灾应急研究中心,四川 成都 610500
Research on FPAR Estimation of Wetland in Zoige Plateau based on Vegetation Index
Yirong Yuan(),Jiyan Wang(),Jiawei Yang,Junnan Xiong
School of Civil Engineering and Geomatics,Southwest Petroleum University,Surveying and Mapping Remote Sensing Geographic Information Disaster Prevention Emergency Research Center,Chengdu 610500,China
 全文: PDF(4676 KB)   HTML
摘要:

植被光合有效辐射吸收比例(FPAR)是湿地生态系统碳收支和气候变化的关键参量,直接反映湿地植被生长发育状况。基于植被指数的经验统计方法简单高效,被广泛运用于草原、森林及作物等植被FPAR的模拟,却较少用于湿地,缺乏不同植被指数对湿地FPAR估算适应性的系统研究。研究对比了14种常见的植被指数,选出最优植被指数用于反演若尔盖高原湿地生长季FPAR。结果表明:常见的植被指数中,MSAVI指数动态考虑了土壤信息,能较好地适应湿地植被FPAR的估算,误差和R2均优于其他植被指数。若尔盖高原湿地生长季FPAR取值在0.22—0.80之间,整体分布较为均匀,泥炭湿地、湿草甸及沼泽湿地平均FPAR分别为0.46、0.63和0.58;生长季期间若尔盖高原不同类型湿地FPAR随时间呈现先增加后降低趋势。

关键词: 若尔盖高原湿地光合有效辐射吸收比例植被指数时空分布    
Abstract:

The Fraction of absorbed Photosynthetically Active Radiation (FPAR) is a key parameter for carbon balance and climate change in wetland ecosystems, which directly reflects the growth and development of wetland vegetation. The empirical statistical method based on vegetation indexes is simple and efficient, and which has been widely used in the simulation of FPAR of grassland, forest and crop vegetation, but it is rarely used in wetlands. There is a lack of systematic research on the adaptability of different vegetation indexes to wetland FPAR estimation. In this paper, 14 common vegetation indexes are compared, and the optimal vegetation index is selected to invert the FPAR of the wetland in the Zoige Plateau during the growing season. The results indicate that the MSAVI index dynamically considers soil information, and can better adapt to the estimation of wetland vegetation FPAR among the common vegetation indexes, and its error and R2 are better than other vegetation indexes. The FPAR value of the Zoige Plateau wetland in the growing season is between 0.22 and 0.8, and the overall distribution is relatively uniform. The average FPAR of peat wetland, wet meadow and marsh wetland are 0.46, 0.63 and 0.58 respectively. During the growing season, the FPAR of different types of wetlands on the Zoige Plateau showed a trend of first increasing and then decreasing with time.

Key words: Zoige Plateau    Wetland    FPAR    Vegetation index    Temporal and spatial distribution
收稿日期: 2021-08-20 出版日期: 2022-12-13
ZTFLH:  P422.2  
基金资助: 国家自然科学基金项目(41701428);四川省应用基础研究项目(2022NSFSC1179);西南石油大学科研“启航计划”项目(2017QHZ026);西南石油大学测绘遥感青年科技创新团队(2017CXTD09)
通讯作者: 王继燕     E-mail: yuanyirong09@163.com;wangjiyan@swpu.edu.cn
作者简介: 袁艺溶(1998-),女,四川乐山人,硕士研究生,主要从事生态遥感研究。E?mail: yuanyirong09@163.com
服务  
把本文推荐给朋友
加入引用管理器
E-mail Alert
RSS
作者相关文章  
袁艺溶
王继燕
杨嘉葳
熊俊楠

引用本文:

袁艺溶,王继燕,杨嘉葳,熊俊楠. 基于植被指数的若尔盖高原湿地光合有效辐射吸收比例估算研究[J]. 遥感技术与应用, 2022, 37(5): 1267-1276.

Yirong Yuan,Jiyan Wang,Jiawei Yang,Junnan Xiong. Research on FPAR Estimation of Wetland in Zoige Plateau based on Vegetation Index. Remote Sensing Technology and Application, 2022, 37(5): 1267-1276.

链接本文:

http://www.rsta.ac.cn/CN/10.11873/j.issn.1004-0323.2022.5.1267        http://www.rsta.ac.cn/CN/Y2022/V37/I5/1267

图1  若尔盖高原概况
植被指数英文缩写(全称)公式
差值植被指数DVI (Difference Vegetation Index)ρNIR-ρR
比值植被指数RVI (Ratio Vegetation Index)ρNIR/ρR
归一化植被指数NDVI (Normalized Difference Vegetation Index)(ρNIR-ρR)/(ρNIR+ρR)
增强型植被指数EVI (Enhanced Vegetation Index)G*(ρNIR-ρR)(ρNIR+C1*ρR-C2*ρB+L)
修正植被指数MVI (Modified Vegetation Index)ρNIR-ρRρNIR+ρR+0.5
改进的归一化植被指数MNDVI (Modified Normalized Difference Vegetation Index)c*ρNIR-ρRc*ρNIR+ρR
土壤调整指数SAVI (Soil Adjust Vegetation Index)ρNIR-ρR*(1+L1)(ρNIR+ρR+L1)
土壤调整简单比SASR (Soil Adjust Simple Ratio)ρNIR+L/2ρR+L/2
优化土壤调整指数OSAVI (Optimized Soil Adjust Vegetation Index)ρNIR-ρRρNIR+ρR+0.16
修正土壤调整植被指数MSAVI (Modified Soil Adjust Vegetation Index)(2ρNIR+1-(2ρNIR+1)2-8(ρNIR-ρR))2
简单比值水体指数SRWI (Simple Ratio Water Index)ρNIR/ρMIR
归一化水体指数NDWI (Normalized Difference Water Index)(ρG-ρNIR)/(ρNIR+ρG)
改进的归一化水体指数MNDWI (Modified Normalized Difference Water Index)(ρG-ρMIR)/(ρG+ρMIR)
归一化水汽指数NDMI (Normalized Difference Moisture Index)(ρNIR-ρMIR)/(ρNIR+ρMIR)
表1  常见植被指数
植被指数估算模型R2RMSEMAD/‰RMD/‰
DVIFPAR= 0.00002VIDVI+0.3980.6100.0504.237.51
RVIFPAR=0.2146VIRVI+0.1610.6780.0454.968.65
NDVIFPAR=0.8911VINDVI+0.2980.7640.0374.037.04
EVIFPAR=0.6597VIEVI+0.35750.6970.0435.138.95
MVIFPAR=1.5667VIMVI-0.83430.7830.0363.866.74
MNDVIFPAR=0.8179VIMNDVI+0.5940.7430.0394.357.60
SASRFPAR=0.2146VISASR+0.1610.6780.0454.958.64
SAVIFPAR=0.5941VISAVI+0.2980.7630.0374.057.08
OSAVIFPAR=0.8911VIOSAVI+0.2950.7610.0374.087.12
MSAVIFPAR=0.7134VIMSAVI+0.24010.8040.0333.556.21
SRWIFPAR=0.3878VISRWI+0.0940.2930.0696.6911.64
NDWIFPAR=-0.903VINDWI+0.30650.7570.0384.688.17
MNDWIFPAR=-1.0044VIMNDWI+0.3740.5720.0515.789.61
NDMIFPAR=1.0152VINDMI+0.46840.3130.0686.3311.02
表2  基于常见植被指数的FPAR模型
图2  不同类型湿地FPAR模拟值与真实值关系
图3  若尔盖高原湿地生长季平均FPAR审图号:GS(2020)3183(注:上部和右部插图为FPAR平均值随经纬度变化,下同)
图4  不同类型湿地生长季平均FPAR像元统计
图5  若尔盖高原湿地6—9月平均FPAR审图号:GS(2020)3183
图6  不同类型湿地生长季平均FPAR变化曲线图中阴影表示平均FPAR标准差
1 Peng Wenhong, Mou Changcheng, Chang Yihui,et al. Carbon storage of forest-swamp wetland ecosystem in permafrost regions of Northeast Cold Temperate Zone[J]. Acta Pedologica Sinica, 2020, 57(6):1526-1538.
1 彭文宏, 牟长城, 常怡慧, 等. 东北寒温带永久冻土区森林沼泽湿地生态系统碳储量[J]. 土壤学报, 2020, 57(6):1526-1538.
2 Wang Bowei, Mou Changcheng, Wang Biao.Carbon storage of primitive coniferous forest wwamp wetland ecosystem in Chang-bai Mountain[J]. Acta Ecologica Sinica,2019,39(9):3344-3354.
2 王伯炜, 牟长城, 王彪. 长白山原始针叶林沼泽湿地生态系统碳储量[J]. 生态学报,2019,39(9):3344-3354.
3 Wang Jiyan, Li Ainong, Jin Huaan. Sensitivity analysis of the denitrification and decomposition model for simulating regional carbon budget at the wetland-grassland area on the Zoige Plateau, China[J]. Journal of Mountain Science, 2016, 13(7): 1200-1216.DOI: 10.1007/s11629-015-3520-z .
doi: 10.1007/s11629-015-3520-z
4 Dong Taifeng, Wu Bingfang, Meng Jihua, et al. Sensitivity analysis of retrieving fraction of Absorbed Photosynthetically Active Radiation (FPAR) Using Remote Sensing Data[J]. Acta Ecologica Sinica, 2016, 36(1): 1-7.
5 Gao Liming, Zhang Lele, Chen Kelong, et al. Photosynthetic active radiation characteristics of Alpine Wetland in Qinghai Lake Basin[J]. Arid Zone Research, 2018, 35(1): 50-56.
5 高黎明, 张乐乐, 陈克龙, 等. 青海湖流域高寒湿地光合有效辐射特征[J]. 干旱区研究, 2018, 35(1): 50-56.
6 Jiao Xuemin, Zhang Helin, Xu Fubao, et al. Analysis of the temporal and spatial changes of FPAR on the Qinghai-Tibet plateau from 1982 to 2015[J]. Remote Sensing Technology and Application, 2020, 35(4): 950-961.
6 焦雪敏, 张赫林, 徐富宝, 等. 青藏高原1982~2015年FPAR时空变化分析[J]. 遥感技术与应用, 2020, 35(4): 950-961.
7 Zhang Jincheng, Zhou Wenzuo.Analysis on the spatio-temporal changes of the photosynthetically active radiation absorption Ratio of vegetation in Qinba Mountains from 2006 to 2015[J]. Chinese Journal of Ecology, 2019, 5(38): 1453-1463.
7 章金城, 周文佐. 2006~2015年秦巴山区植被光合有效辐射吸收比例的时空变化分析[J]. 生态学杂志, 2019, 5(38): 1453-1463.
8 Madani N, Kimball JS, Affleck DLR, et al. Improving ecosystem productivity modeling through spatially explicit estimation of optimal light use efficiency[J]. Journal of Geophysical Research: Biogeosciences, 2014, 119(9): 1755-1769. DOI: 10.1002/2014JG002709 .
doi: 10.1002/2014JG002709
9 Tian Dingfang, Fan Wenjie, Ren Huazhong. Research progress of vegetation photosynthetically active radiation absorption ratio by remote sensing[J]. Journal of Remote Sensing, 2020, 24(11): 1307-1324.
9 田定方, 范闻捷, 任华忠. 植被光合有效辐射吸收比率遥感研究进展[J]. 遥感学报, 2020, 24(11): 1307-1324.
10 Liang S Z, Sui X Y, Hou X H,et al.Simulation and analysis on green fraction of absorbed photosynthetically active radiation of deciduous broadleaved forest canopy through remote sensing model[J]. Acta Ecologica Sinica,2017,37(10): 3415-3424.DOI: 10.5846/stxb201603080405 .
doi: 10.5846/stxb201603080405
11 Ge Meixiang, Zhao Jun, Zhong Bo, et al. Comparison of FY-3/VIRR, MERSI and EOS/MODIS vegetation index and analysis of the difference[J]. Remote Sensing Technology and Application,2017,32(2):262-273.
11 葛美香,赵军,仲波,等. FY-3/VIRR及MERSI与EOS/MODIS植被指数比较与差异原因分析[J]. 遥感技术与应用,2017,32(2):262-273.
12 Wang Baolin, Yang Yong, Zheng Shuhua, et al. Estimation of photosynthetic effective absorption ratio in typical grassland based on vegetation index[J].Acta Prataculturae Sinica,2016, 24(3):689-692.
12 王保林, 杨勇, 郑淑华, 等. 基于植被指数的典型草原光合有效吸收比例估算研究[J]. 草业学报, 2016, 24(3): 689-692.
13 Liang Shouzhen, Ma Wandong, Wang Meng, et al. The relationship between canopy green FPAR and vegetation index and its sensitivity to aerosol[J]. Surveying and Spatial Information Technology,2018,41(12):11-14.
13 梁守真,马万栋,王猛,等. 冠层绿色FPAR与植被指数关系及其对气溶胶的敏感性分析[J]. 测绘与空间地理信息,2018,41(12):11-14.
14 Chen Xueyang, Meng Jihua, Wu Bingfang, et al. Summer corn FPAR remote sensing monitoring model based on HJ-1 CCD[J]. Transactions of the Chinese Society of Agricultural Engineering, 2010, 26(S1): 241-245.
14 陈雪洋, 蒙继华, 吴炳方,等.基于HJ-1 CCD的夏玉米FPAR遥感监测模型[J]. 农业工程学报, 2010, 26(): 241-245.
15 Zhao L, Liu Z G, Xu S, et al. Retrieving the diurnal FPAR of a maize canopy from the Jointing Stage to the Tasseling Stage with Vegetation Indexes under different water stresses and light conditions[J]. Sensors,2018,18(11):3965.DOI: 10.3390/s18113965 .
doi: 10.3390/s18113965
16 Tan C W, Wang D L, Zhou J,et al.Remote asses sing Fraction of Photosynthetically Active Radiation (FPAR) for wheat canopies based on hyperspectral vegetation indexes[J]. Frontiers in Plant Science,2018,766(9):1-9.DOI: 10.3389/fpls. 2018. 00776 .
doi: 10.3389/fpls. 2018. 00776
17 He Jia, Guo Yan, Zhang Yan, et al. Dynamic estimation of summer maize FPAR by remote sensing based on GF-1 data[J]. Transactions of the Chinese Society of Agricultural Engineering, 2022, 53(4): 164-172.
17 贺佳, 郭燕, 张彦, 等. 基于GF-1数据的夏玉米FPAR遥感动态估算[J]. 农业工程学报, 2022, 53(4): 164-172.
18 Li Qing, Wang Hongtao, Liu Wen, et al. Estimating the potential of Alpine grassland vegetation net primary productivity based on HJ-1 satellite remote sensing data: Taking Ruoergai grassland as an example[J]. China Desert,2013,33(4):1250-1255.
18 李庆,王洪涛,刘文,等.以HJ-1卫星遥感数据估算高寒草地植被净第一性生产力的潜力评估——以若尔盖草地为例[J].中国沙漠,2013,33(4):1250-1255.
19 Guo Bin, Wang Shan, Wang Mingtian. Spatiotemporal changes of net primary productivity in Zoige Grassland Wetland from 1999 to 2015[J]. Chinese Journal of Applied Ecology, 2020, 31(2): 424-432.
19 郭斌, 王珊, 王明田. 1999~2015年若尔盖草原湿地净初级生产力时空变化[J]. 应用生态学报, 2020, 31(2): 424-432.
20 Bian J H, Li A N, Deng W. Estimation and Analysis of net primary productivity of Ruoergai Wetland in China for the recent 10 years based on remote sensing[J]. Procedia Environmental Sciences,2010,2:288-301. DOI:10.1016/j.proenv. 2010.10.035 .
doi: 10.1016/j.proenv. 2010.10.035
21 Li Meng, Hu Rong, Pu Yulin, et al. Soil nitrogen mineralization characteristics and temperature effects of different degraded alpine Marsh Wetlands in Zoige[J]. Acta Grasslanda, 2021, 29(5): 1025-1033.
21 李梦, 胡容, 蒲玉琳, 等. 若尔盖不同退化程度高寒沼泽湿地土壤氮矿化特征及温度效应[J]. 草地学报, 2021, 29(5): 1025-1033.
22 Zhai Xing, Wang Jiyan, Yu Bing, et al. Remote sensing monitoring of grazing intensity in Zoige Plateau based on above-ground Net Primary Productivity and above-ground biomass[J]. Pragmatic Science, 2021, 38(3): 544-553.
22 翟星, 王继燕, 于冰, 等. 基于地上净初级生产力与地上生物量的若尔盖高原放牧强度遥感监测[J]. 草业科学, 2021, 38(3): 544-553.
23 Liu Lijuan, Liu Xinwei, Ju Peijun,et al. Development and carbon dynamics of peatland in Zoige Plateau since 15 000 years[J]. Acta Ecologica Sinica,2018,38(18):6493-6501.
23 刘利娟, 刘欣蔚, 鞠佩君, 等. 15000年以来若尔盖高原泥炭地发育及其碳动态[J]. 生态学报,2018,38(18):6493-6501.
24 Zhang Zhengjian, Li Ainong, Bian Jinhu, et al. Estimation of aboveground biomass of Zoige Grassland based on UAV image visible light vegetation index[J]. Remote Sensing Technology and Application,2016,31(1):51-62.
24 张正健,李爱农,边金虎,等. 基于无人机影像可见光植被指数的若尔盖草地地上生物量估算研究[J]. 遥感技术与应用,2016,31(1): 51-62.
25 Fei Yi, Wang Jiyan, Wang Zegen. Quantitative analysis of land desertification and its Causes in Zoige Plateau[J]. Arid Land Resources and Environment,2019,33(8):146-152.
25 费怡, 王继燕, 王泽根. 若尔盖高原土地沙化及其成因定量分析[J]. 干旱区资源与环境,2019,33(8):146-152.
26 Yuan Yue, Zhang Liang, Cui Linlin. Temporal and spatial changes of water conservation function of Zoige Plateau ecosystem[J]. Chinese Journal of Ecology,2020,39(8):2713-2723.
26 苑跃,张亮,崔林林.若尔盖高原生态系统水源涵养功能时空变化特征[J]. 生态学杂志,2020,39(8):2713-2723.
27 Yin Xiaojun, Zhu Honghui, Gao Jerry, et al. NPP simulation of farming and pastoral areas based on the fusion of Landsat and MODIS data[J]. Journal of Agricultural Machinery, 2020, 51(8): 163-170.
27 尹小君, 祝宏辉, GAO Jerry 等. 基于Landsat和MODIS数据融合的农牧区NPP模拟[J]. 农业机械学报, 2020, 51(8): 163-170.
28 Chen Minghua, Chai Peng, Chen Wenxiang, et al. Comparative study on estimation of vegetation coverage with different vegetation indices[J]. Subtropical Soil and Water Conservation, 2016, 28(1): 52-56.
28 陈明华, 柴鹏, 陈文祥, 等. 不同植被指数估算植被覆盖度的比较研究[J]. 亚热带水土保持, 2016, 28(1): 52-56.
29 Chen Baolin, Zhang Bincai, Wu Jing, et al. Application of historical average method to pixel cloud compensation in MODIS Image: Taking Gansu Province as an example[J]. Remote Sensing for Land and Resources, 2021, 33(2): 85-92.
29 陈宝林, 张斌才, 吴静, 等. 历史平均值法用于MODIS影像像元云补偿 ——以甘肃省为例[J]. 国土资源遥感, 2021, 33(2): 85-92.
30 Niu Yaxiao, Zhang Liyuan, Han Wenting, et al. Winter wheat coverage extraction method based on UAV remote sensing and vegetation index[J]. Transactions of the Chinese Society of Agricultural Machinery,2018,49(4):212-221.
30 牛亚晓,张立元, 韩文霆, 等. 基于无人机遥感与植被指数的冬小麦覆盖度提取方法[J]. 农业机械学报,2018,49(4):212-221.
31 Li Zhiwei, Lu Hanyou, Hu Xuyue. Water balance calculation of typical peat wetland in Zoige Plateau[J]. Advances in Water Science, 2018, 29(5):655-666.
31 李志威, 鲁瀚友, 胡旭跃. 若尔盖高原典型泥炭湿地水量平衡计算[J]. 水科学进展, 2018, 29(5): 655-666.
32 Liu Yuqing, Yan Feng, Chen Junhan. Remote sensing estimation of biomass in arsenic area based on Landsat-8 OLI data[J]. Research of Soil and Water Conservation, 2021, 28(2): 135-140,148.
32 刘雨晴, 闫峰, 陈俊翰. 基于Landsat-8 OLI数据的砒砂岩区生物量遥感估算[J]. 水土保持研究, 2021, 28(2): 135-140,148.
33 Jiang Jingang, Li Ainong, Bian Jinhu, et al. Research on wetland changes in Zoige County from 1974 to 2007[J]. Wetland Science,2012,10(3):318-326.
33 蒋锦刚,李爱农,边金虎,等. 1974~2007年若尔盖县湿地变化研究[J]. 湿地科学,2012, 10(3): 318-326.
34 Cristiano P M, Posse G, Bella C M D, et al. Uncertainties in FPAR estimation of grass canopies under different stress situations and differences in architecture[J]. International Journal of Remote Sensing,2010,31(15):4095-4109. DOI: 10.1080/01431160903229192 .
doi: 10.1080/01431160903229192
35 Wang Y T, Yan G J, Xie D H, et al. Generating long time series of high spatiotemporal resolution FPAR images in the remote sensing trend surface framework[J]. IEEE Transactions on Geoscience and Remote Sensing,2022,60(1):1-15.DOI: 10.1109/TGRS.2021.3067913 .
doi: 10.1109/TGRS.2021.3067913
36 Arturo S A, Lain S, Paul D G, et al. Calibration of Co-Located identical PAR sensors using wireless sensor networks and characterization of the in situ FPAR variability in a tropical dry forest[J]. Remote Sensing,2022,14(12):2752. DOI: 10.3390/rs14122752 .
doi: 10.3390/rs14122752
37 Dong J W, Xiao X M, Wagle P, et al. Comparison of four EVI-based models for estimating gross primary production of maize and soybean croplands and tallgrass prairie under severe drought[J]. Remote Sensing of Environment, 2015, 2(162): 154-168. DOI: 10.1016/j.rse.2015.02.022
doi: 10.1016/j.rse.2015.02.022
38 Majasalmi T, Rautiainen M, Stenberg P. Modeled and measured FPAR in a boreal forest: Validation and application of a new model[J]. Agricultural and Forest Meteorology, 2016, 189(204): 46-47. DOI: 10.1016/j.agrformet.2014.01.015 .
doi: 10.1016/j.agrformet.2014.01.015
39 Luke A B, Courtney M, Harry M, et al. Evaluation of global leaf area index and fraction of absorbed photosynthetically active radiation products over North America using copernicus ground based observations for validation data[J].Remote Sensing of Environment, 2020, 247(1): 1-22.DOI: 10.1016/j.rse.2020.111935 .
doi: 10.1016/j.rse.2020.111935
40 Chen B X, Zhang X Z, Sun Y F, et al. Alpine grassland FPAR change over the Northern Tibetan Plateau from 2002 to 2011[J]. Advances in Climate Change Research, 2017, 8(2): 108-116.DOI: 10.1016/j.accre.2017.05.008
doi: 10.1016/j.accre.2017.05.008
41 Zhou Zijuan, Su Peixi, Shi Rui, et al. Light use efficiency of plants and the environmental impact factors in different Alpine ecosystems[J]. Chinese Journal of Ecology,2017,36(6):1570-1577.
41 周紫鹃, 苏培玺, 侍瑞, 等. 不同高寒生态系统植物光能利用效率及其环境影响[J]. 生态学杂志, 2017, 36(6):1570-1577.
42 Liu Yanxia, Liu Yangyang, Chen Shiwei, et al. Photosynthetic and physiological characteristics of plants along the slope gradient of Alpine meadow on the eastern edge of the Qinghai-Tibet Plateau[J]. Soil and Crops, 2015, 4(3): 104-112.
42 刘晏霞, 刘洋洋, 陈世伟, 等. 青藏高原东缘高寒草甸坡向梯度上植物光合生理特征研究[J]. 土壤与作物, 2015, 4(3): 104-112.
43 Deng Chenhui, Bai Hongying, Gao Shan, et al. Temporal and spatial changes of vegetation coverage in Qinling Mountains and its dual response to climate change and human activities[J]. Journal of Natural Resources, 2018, 33(3): 425-438.
43 邓晨晖, 白红英, 高山,等. 秦岭植被覆盖时空变化及其对气候变化与人类活动的双重响应[J]. 自然资源学报,2018,33(3):425-438.
44 Rao Junfeng, Zhang Xianfeng, Lian Jingfang. The influence of the uncertainty in the retrieval of atmospheric precipitation from satellite remote sensing on the simulation of total solar radiation[J]. Journal of Natural Resources,2016,31(4):639-648.
44 饶俊峰, 张显峰, 练静芳. 大气可降水量卫星遥感反演不确定性对太阳总辐射模拟的影响[J]. 自然资源学报, 2016,31(4):639-648.
[1] 郭擎,朱丽娅,李安,顾铃燕. 基于NDVI变化检测的滑坡遥感精细识别[J]. 遥感技术与应用, 2022, 37(1): 17-23.
[2] 王茜,宋开山,毛德华,焉恒琦,谭晓宇,王宗明. 1980~2018朝鲜半岛西海岸滨海湿地演化分析[J]. 遥感技术与应用, 2022, 37(1): 108-116.
[3] 赵天玮,朱文彬,裴亮,宝康妮. 三江源蒸散发遥感估算及其时空分布特征研究[J]. 遥感技术与应用, 2022, 37(1): 137-147.
[4] 谭月,杨倩,贾明明,席志成,王宗明,毛德华. 辽河口国家级自然保护区湿地时空演变遥感评估[J]. 遥感技术与应用, 2022, 37(1): 218-230.
[5] 李淑贞,徐大伟,范凯凯,陈金强,佟旭泽,辛晓平,王旭. 基于无人机与卫星遥感的草原地上生物量反演研究[J]. 遥感技术与应用, 2022, 37(1): 272-278.
[6] 葛强,沈文举,李冉,李莘莘,蔡坤,左宪禹,乔保军,张云舟. 2001~2018年我国热异常点时空分布特征研究[J]. 遥感技术与应用, 2022, 37(1): 73-84.
[7] 白雪洁,王旭峰,柳晓惠,周旭强. 黑河流域湿地、农田、草地生态系统碳通量变化特征及驱动因子分析[J]. 遥感技术与应用, 2022, 37(1): 94-107.
[8] 马锦典,江洪. SEVI指数消除4种十米级空间分辨率卫星影像地形阴影影响的效果评价[J]. 遥感技术与应用, 2021, 36(5): 1100-1110.
[9] 吴川虎,陶于祥,罗小波. 基于Google Earth Engine的重庆市植被指数长时间序列S-G滤波方法的改进与实现[J]. 遥感技术与应用, 2021, 36(5): 1189-1198.
[10] 路春燕,雷依凡,苏颖,黄雨菲,刘明月,贾明明. 基于面向对象—深度学习的闽东南低海拔海岸带地区湿地动态遥感分析[J]. 遥感技术与应用, 2021, 36(4): 713-727.
[11] 李晓东,闫守刚,宋开山. 遥感监测东北地区典型湖泊湿地变化的方法研究[J]. 遥感技术与应用, 2021, 36(4): 728-741.
[12] 罗玲,毛德华,张柏,王宗明,杨桄. 芦苇湿地植被NPP估算方法探索与应用[J]. 遥感技术与应用, 2021, 36(4): 742-750.
[13] 陈康明,朱旭东. 基于Google Earth Engine的南方滨海盐沼植被时空演变特征分析[J]. 遥感技术与应用, 2021, 36(4): 751-759.
[14] 姚杰鹏,杨磊库,陈探,宋春桥. 基于Sentinel-1,2和Landsat 8时序影像的鄱阳湖湿地连续变化监测研究[J]. 遥感技术与应用, 2021, 36(4): 760-776.
[15] 梁爽,宫兆宁,赵文吉,关鸿亮,梁亚囡,陆丽,赵雪. 基于多季相Sentinel-2影像的白洋淀湿地信息提取[J]. 遥感技术与应用, 2021, 36(4): 777-790.