Spatiotemporal changes in vegetation net primary productivity in the arid region of Northwest China, 2001 to 2012

doi:10.1007/s11707-017-0621-8

Front. Earth Sci.

2018, Vol. 12

Issue (1) : 108-124 https://doi.org/10.1007/s11707-017-0621-8

RESEARCH ARTICLE

Spatiotemporal changes in vegetation net primary productivity in the arid region of Northwest China, 2001 to 2012

Zhen LI, Jinghu PAN(

)

College of Geographic and Environmental Science, Northwest Normal University, Lanzhou 730070, China

Download: PDF(1631 KB) HTML
Export: BibTeX | EndNote | Reference Manager | ProCite | RefWorks

Abstract

Net primary productivity (NPP) is recognized as an important index of ecosystem conditions and a key variable of the terrestrial carbon cycle. It also represents the comprehensive effects of climate change and anthropogenic activity on terrestrial vegetation. In this study, the temporal-spatial pattern of NPP for the period 2001–2012 was analyzed using a remote sensing-based carbon model (i.e., the Carnegie-Ames-Stanford Approach, CASA) in addition to other methods, such as linear trend analysis, standard deviation, and the Hurst index. Temporally, NPP showed a significant increasing trend for the arid region of Northwest China (ARNC), with an annual increase of 2.327 g C. Maximum and minimum productivity values appeared in July and December, respectively. Spatially, the NPP was relatively stable in the temperate and warm-temperate desert regions of Northwest China, while temporally, it showed an increasing trend. However, some attention should be given to the northwestern warm-temperate desert region, where there is severe continuous degradation and only a slight improvement trend.

Keywords NPP CASA model remote sensing arid region of Northwest China (ARNC)

Corresponding Author(s): Jinghu PAN

Just Accepted Date: 16 March 2017 Online First Date: 07 April 2017 Issue Date: 23 January 2018

Cite this article:

Zhen LI,Jinghu PAN. Spatiotemporal changes in vegetation net primary productivity in the arid region of Northwest China, 2001 to 2012[J]. Front. Earth Sci., 2018, 12(1): 108-124.

URL:

https://academic.hep.com.cn/fesci/EN/10.1007/s11707-017-0621-8
https://academic.hep.com.cn/fesci/EN/Y2018/V12/I1/108

Fig.1 Comprehensive physical representation of the ARNC. Note: There are three subregions: I, the Xinjiang mountain grassland-coniferous forest region; II, the northwestern warm-temperate desert region; and III, the Inner Mongolia temperate steppe region.

Fig.2 Flow diagram summarizing the methodology for the Carnegie-Ames-Stanford Approach (CASA) model estimates based on vegetation cover, NDVI , DEM, and Climate parameter. SOL = total solar radiation; T_ε₁ and T_ε₂ = temperature stress coefficients; W_ε = the moisture stress coefficient; APAR = photosynthetically active radiation; and ε = light-use efficiency. Trend analysis (slope) can detect and analyze the temporal tendency of each grid in the long time series. Standard deviation (σ) can reflect the fluctuation. The Hurst analysis (R/S) can evaluate the persistence or non-persistence of a time series of the NPP.

Tab.1 List of methods used and their functions and references

Fig.3 Comparison of simulated and observed NPP.

Fig.4 Spatial distribution of mean annual NPP of the ARNC from 2001 to 2012.

Fig.5 Spatial distribution of mean annual NDVI of the ARNC from 2001 to 2012.

Fig.6 Interannual variations in total NPP over the ARNC from 2001 to 2012.

Fig.7 Distribution of the linear trend of annual NPP changes in the ARNC from 2001 to 2012.

Fig.8 Standard deviation of NPP changes in the Northwest arid region from 2001 to 2012.

Fig.9 Hurst index of NPP changes in the northwest arid region from 2001 to 2012.

Fig.10 Spatial change characteristics of NPP based on the trends and the Hurst index. Note: nine distribution types are present.

Tab.2 Typical vegetation NPP average contrasts (g C·m⁻²·yr⁻¹)

1	Almorox J, Hontoria C (2004). Global solar estimation using sunshine duration in Spain. Energy Convers Manage, 45(9–10): 1529–1535 https://doi.org/10.1016/j.enconman.2003.08.022
2	Angström A (1924). Solar and terrestrial radiation. Report to the international commission for solar research on actinometric investigations of solar and atmospheric radiation. Quart J Roy Met Soc, 50(210): 121–126 https://doi.org/10.1002/qj.49705021008
3	Calvão T, Palmeirim J M (2004). Mapping mediterranean scrub with satellite imagery: biomass estimation and spectral behaviour. Int J Remote Sens, 25(16): 3113–3126 https://doi.org/10.1080/01431160310001654978
4	Cao M, Prince S D, Small J, Goetz S J (2004). Remotely sensed interannual variations and trends in terrestrial net primary productivity 1981–2000. Ecosyst, 7(3): 233–242 https://doi.org/10.1007/s10021-003-0189-x
5	Chen F J, Shen Y J, Li Q, Guo Y, Xu L M (2011). Spatio-temporal variation analysis of ecological systems NPP in China in past 30 years. Sci Geogr Sin, 31(11): 1409–1414 (in Chinese)
6	Esser R, Fineschi S, Dobrzycka D, Habbal S R, Edgar R J, Raymond J C, Kohl J L, Guhathakurta M (1999). Plasma properties in coronal holes derived from measurements of minor ion spectral lines and polarized white light intensity. Astrophys J, 510(1): 63–67 https://doi.org/10.1086/311786
7	Eswaran H, Lal R, Reich P F (2001). Land degradation: an overview. In: Bridges E M, Hannam I D, Oldeman L R, Pening de Vries F W T, Scherr S J , Sompatpanit S, eds. Responses to Land Degradation. Proceedings of the 2nd International Conference on Land Degradation and Desertification, Khon Kaen, Thailand. New Delhi: Oxford Press, 20–35
8	Fang J Y, Piao S L, Field C B, Pan Y D, Guo Q H, Zhou L M, Peng C H, Tao S (2003). Increasing net primary production in China from 1982 to 1999. Front Ecol Environ, 1(6): 293–297 https://doi.org/10.1890/1540-9295(2003)001[0294:INPPIC]2.0.CO;2
9	Field C B, Randerson J T, Malmström C M (1995). Global net primary production: combining ecology and remote sensing. Remote Sens Environ, 51(1): 74–88 https://doi.org/10.1016/0034-4257(94)00066-V
10	Guo Z X, Wang Z M, Zhang B, Liu D W, Yang G, Song K S, Li F (2008). Analysis of temporal-spatial characteristics and factors influencing vegetation NPP in northeast China from 2000 to 2006. Resources Science, 30(8): 1226–1235 (in Chinese)
11	Haxeltine A, Prentice I C (1996). BIOME3: an equilibrium terrestrial biosphere model based on ecophysiological constraints, resource availability and competition among plant functional types. Global Biogeochem Cycles, 10(4): 693–709 https://doi.org/10.1029/96GB02344
12	He Y, Dong W J, Guo X Y, Cao L J, Feng D (2006). Terrestrial NPP variation in the region of the South-North Water Diversion Project (East Route). Adv Clim Change Res, 2(5): 246–249 (in Chinese)
13	Hicke J A, Asner G P, Randerson J T, Tucker C, Los S, Birdsey R, Jenkins J C, Field C, Holland E (2002b). Setellite-derived increases in net primary productivity across North America, 1982–1998. Geophys Res Lett, 29(10): 69-1–69-4
14	Hicke J A, Asner G P, Randerson J T, Tuker C, Los S, Birdsey R, Jenkins J C, Field C (2002a). Trends in North American net primary productivity derived from satellite observations, 1982–1998. Glob Biogeochem Cy, 16(2): 2-1–2-14
15	Holben B N (1986). Characteristics of maximum-value composite images from temporal AVHRR data. Int J Remote Sens, 7(11): 1417–1434 https://doi.org/10.1080/01431168608948945
16	Hurst H E (1951). Long term storage capacity of reservoirs. Trans Am Soc Civ Eng, 116(12): 776–808
17	Jiang R Z, Li X Q, Zhu Y G, Zhang G Z (2011). Spatial-temporal variation of NPP and NDVI correlation in wetland of Yellow River Delta based on MODIS data. Acta Ecol Sin, 31(22): 6708–6716 (in Chinese)
18	Knorr W, Heimann M (1995). Impact of drought stress and other factors on seasonal land biosphere CO2 exchange studied through an atmospheric tracer transport model. Tellus B Chem Phys Meterol, 47(4): 471–489 https://doi.org/10.1034/j.1600-0889.47.issue4.7.x
19	Li E Z, Tan K, Du P J, Jiang D E (2013a). Net primary productivity of vegetation estimation and correlation analysis based on multi-temporal remote sensing data in Xuzhou. Remote Sensing Technology and Application, 28(4): 689–696 (in Chinese)
20	Li J, Cui Y P, Liu J Y, Shi W J, Qin Y C (2013b). Estimation and analysis of net primary productivity by integrating MODIS remote sensing data with a light use efficiency model. Ecol Modell, 252(1): 3–10 https://doi.org/10.1016/j.ecolmodel.2012.11.026
21	Li J, You S C, Huang J F (2006). Spatial interpolation method and spatial distribution characteristics of monthly mean temperature in China during 1961–2000. Ecol Environ, 15(1): 109–114 (in Chinese)
22	Lin H L (2009). A new model of grassland net primary productivity (NPP) based on the integrated orderly classification system of grassland. The sixth international conference on fuzzy systems and knowledge discovery. Tianjin, China, 52–56
23	Liu C M, Chen Y N, Xu Z X (2010). Eco-hydrology and sustainable development in the arid regions of China. Hydrol Processes, 24(2): 127–128
24	Liu C Y, Dong X F, Liu Y Y (2015). Changes of NPP and their relationship to climate factors based on the transformation of different scales in Gansu, China. Catena, 125: 190–199 https://doi.org/10.1016/j.catena.2014.10.027
25	Liu J Y, Liu M L, Zhuang D F, Zhang Z X, Deng X Z (2003). Study on spatial pattern of land-use change in China during 1995–2000. Sci China Ser D, 46(4): 373–384
26	Liu Y A, Huang B, Yi C G, Cheng T, Yu J, Qu L A (2013). Simulation by remote sensing and analysis of net primary productivity of vegetation based on topographical correction. Trans Chin Soc of Agric Eng, 29: 130–141 (in Chinese)
27	Malmström C M, Thompson M V, Juday G P, Los S O, Randerson J T, Field C B (1997). Interannual variation in global-scale net primary production: testing model estimates. Global Biogeochem Cycles, 11(3): 367–392 https://doi.org/10.1029/97GB01419
28	Mao D H, Luo L, Wang Z M, Zhang C H, Ren C Y (2015). Variations in net primary productivity and its relationships with warming climate in the permafrost zone of the Tibetan Plateau. J Geogr Sci, 25(8): 967–977 https://doi.org/10.1007/s11442-015-1213-8
29	Matsushita B, Tamura M (2002). Integrating remotely sensed data with an ecosystem model to estimate net primary productivity in East Asia. Remote Sens Environ, 81(1): 58–66 https://doi.org/10.1016/S0034-4257(01)00331-5
30	Moleele N, Ringrose S, Arnberg W, Lunden B, Vanderpost C (2001). Assessment of vegetation indexes useful for browse (forage) prediction in semi-arid rangelands. Int J Remote Sens, 22(5): 741–756 https://doi.org/10.1080/01431160051060147
31	Mu S J, Chen Y Z, Li J L, Ju W M, Odeh I O A, Zou X L (2013a). Grassland dynamics in response to climate change and human activities in Inner Mongolia, China between 1985 and 2009. Rangeland J, 35(3): 315–329 https://doi.org/10.1071/RJ12042
32	Mu S J, Li J L, Zhou W, Yang H F, Zhang C B, Ju W M (2013c). Spatial-temporal distribution of net primary productivity and its relationship with climate factors in Inner Mongolia from 2001 to 2010. Acta Ecol Sin, 33(12): 3752–3764 (in Chinese) https://doi.org/10.5846/stxb201205030638
33	Mu S J, Zhou S X, Chen Y Z, Li J L, Ju W M, Odeh I O A (2013b). Assessing the impact of restoration-induced land conversion and management alternatives on net primary productivity in Inner Mongolian grassland, China. Global Planet Change, 108(3): 29–41 https://doi.org/10.1016/j.gloplacha.2013.06.007
34	Nemani R R, Keeling C D, Hashimoto H, Jolly W M, Piper S C, Tucker C J, Myneni R B, Running S W (2003). Climate-driven increases in global terrestrial net primary production from 1982 to 1999. Science, 300(5625): 1560–1563 https://doi.org/10.1126/science.1082750
35	Niklaus M, Eisfelder C, Tum M, Günther K P (2012). A remote sensing model based land degradation index for the arid and semi-arid regions of southern Africa. IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2012. 22e27 July 2012, Munich, Germany
36	Parton W J, Scurlock J M O, Ojıma D S, Gilmanov T G, Scholes R J, Schimel D S, Kirchner T, Menaut J C, Seastedt T, Garcia Moya E, Kamnalrut A, Kinyamario J I (1993). Observations and modeling of biomass and soil organic matter dynamics for the grassland biome worldwide. Global Biogeochem Cycles, 7(4): 785–809 https://doi.org/10.1029/93GB02042
37	Pei F S, Li X, Liu X P, Lao C H (2013). Assessing the impacts of droughts on net primary productivity in China. J Environ Manage, 114(2): 362–371 https://doi.org/10.1016/j.jenvman.2012.10.031
38	Piao S L, Ciais P, Huang Y, Shen Z H, Peng S S, Li J S, Zhou L P, Liu H Y, Ma Y C, Ding Y H, Friedlingstein P, Liu C Z, Tan K, Yu Y Q, Zhang T Y, Fang J Y (2010). The impacts of climate change on water resources and agricultural in China. Nature, 467(7311): 43–51 https://doi.org/10.1038/nature09364
39	Piao S L, Fang J Y, Ciais P, Peylin P, Huang Y, Sitch S, Wang T (2009). The carbon balance of terrestrial ecosystems in China. Nature, 458(7241): 1009–1013 https://doi.org/10.1038/nature07944
40	Piao S L, Fang J Y, He J S (2006). Variations in vegetation net primary production in the Qinghai-Xizang plateau, China, from 1982 to 1999. Clim Change, 74(1–3): 253–267 https://doi.org/10.1007/s10584-005-6339-8
41	Piao S L, Fang J Y, Zhou L M, Zhu B, Tan K, Tao S (2005). Changes in vegetation net primary productivity from 1982 to 1999 in China. Global Biogeochem Cycles, 19(2): 1605–1622 https://doi.org/10.1029/2004GB002274
42	Potter C, Klooster S, Myneni R, Genovese V, Tan P N, Kumar V (2003). Continental-scale comparisons of terrestrial carbon sinks estimated from satellitedata and ecosystem modeling 1982–1998. Global Planet Change, 39(3–4): 201–213 https://doi.org/10.1016/j.gloplacha.2003.07.001
43	Potter C, Klooster S, Steinbach M, Tan P N, Kumar V, Shekhar S, Carvalho C R D (2004). Understanding global teleconnections of climate to regional model estimates of Amazon ecosystem carbon fluxes. Glob Change Biol, 10(5): 693–703 https://doi.org/10.1111/j.1529-8817.2003.00752.x
44	Potter C S, Randerson J T, Field C B, Matson P A, Vitousek P A, Mooney H A, Klooster S A (1993). Terrestrial ecosystem production: a process model based on global satellite and surface data. Global Biogeochem Cycles, 7(4): 811–841 https://doi.org/10.1029/93GB02725
45	Prescott J A (1940). Evaporation from a water surface in relation to solar radiation. Trans R Soc S Aust, 64: 114–125
46	Prince S D (1991). A model of regional primary production for use with coarse resolution satellite data. Int J Remote Sens, 12(6): 1313–1330 https://doi.org/10.1080/01431169108929728
47	Prince S D, Goward S N (1995). Global primary production: a remote sensing approach. J Biogeogr, 22(4/5): 815–835 https://doi.org/10.2307/2845983
48	Rayner P J, Scholze M, Knorr W, Kaminski T, Giering R, Widmann H (2005). Two decades of terrestrial carbon fluxes from a carbon cycle data assimilation system (CCDAS). Global Biogeochem Cycles, 19(2): 202–214 https://doi.org/10.1029/2004GB002254
49	Reeves M C, Moreno A L, Bagne K E, Running S W (2014). Estimating climate change effects on net primary production of rangelands in the United States. Clim Change, 126(3–4): 429–442 https://doi.org/10.1007/s10584-014-1235-8
50	Ren Z Y, Liu Y X (2013). Contrast in vegetation net primary productivity estimation models and ecological effect value evaluation in Northwest China. Chin. J Eco-Agric, 21(4): 494–502 (in Chinese)
51	Ruimy A, Dedieu G, Saugier B (1996). TURC: a diagnostic model of continental gross primary productivity and net primary productivity. Global Biogeochem Cycles, 10(2): 269–285 https://doi.org/10.1029/96GB00349
52	Running S W, Nemani R R, Heinsch F A, Zhao M S, Reeves M, Hashimoto H (2004). A continuous satellite-derived measure of global terrestrial primary production. Bioscience, 54(6): 547–560 https://doi.org/10.1641/0006-3568(2004)054[0547:ACSMOG]2.0.CO;2
53	Running S W, Thornton P E, Nemani R, Glassy J M (2000). Global terrestrial gross and net primary productivity from the earth observing system. In: Sala O E, Jackson R B, Mooney H A, Howarth R W, eds. Methods in Ecosystem Science. New York: Springer, 44–57
54	Sellers P J, Randall D A, Collatz G J, Berry J A, Field C B, Dazlich D A, Zhang C, Collelo G D, Bounoua L (1996). A revised land surface parameterization (SiB2) for atmospheric GCMs. Part 1: model formulation. J Clim, 9(4): 676–705 https://doi.org/10.1175/1520-0442(1996)009<0676:ARLSPF>2.0.CO;2
55	Silvestri S, Marani M, Settle J, Benvenuto F, Marani A (2002). Salt marsh vegetation radiometry: data analysis and scaling. Remote Sens Environ, 80(3): 473–482 https://doi.org/10.1016/S0034-4257(01)00325-X
56	Stow D A V, Petersen A, Hope A, Engstrom R, Coulter L L (2007). Greenness trends of Arctic tundra vegetation in the 1990s: Comparison of two NDVI datasets from NOAA AVHRR system. Int J Remote Sens, 28(21): 4807–4822 https://doi.org/10.1080/01431160701264284
57	Sun Q L, Feng X F, Liu M X, Xiao X (2015). Estimation and analysis of net primary productivity in Wuling mountainous area based on remote sensing. Journal of Natural Resources, 347: 83–95 (in Chinese)
58	Tang C J, Fu X Y, Jiang D, Fu J Y, Zhang X Y, Zhou S (2014). Simulating spatiotemporal dynamics of Sichuan grassland net primary productivity using the CASA model and in situ observations. Sci World J, 2014: 956963 https://doi.org/10.1155/2014/956963
59	UN (1994). Elaboration of an International Convention to Combat Desertification in Countries Experiencing Serious Drought and/or Desertification, Particularly in Africa. UN General Assembly. A/AC.241/27. Available online at: (accessed 17.03.10)
60	Verstraete M M (1986). Defining desertification: a review. Clim Change, 9(1–2): 5–18 https://doi.org/10.1007/BF00140520
61	Wang H, Li X B, Long H L, Gai Y Q, Wei D D (2009). Monitoring the effects of land use and cover changes on net primary production: a case study in China’s Yongding River basin. For Ecol Manage, 258(12): 2654–2665 https://doi.org/10.1016/j.foreco.2009.09.028
62	Wang P J, Xie D H, Zhou Y Y, E Y H, Zhu Q J (2014). Estimation of net primary productivity using a process-based model in Gansu Province, Northwest China. Environ Earth Sci, 71(2): 647–658 https://doi.org/10.1007/s12665-013-2462-4
63	Wang X C, Wang S D, Zhang H B (2013). Spatiotemporal pattern of vegetation net primary productivity in Henan Province of China based on MOD17A3. Chinese J Ecol, 32(10): 2797–2805 (in Chinese)
64	Woodward F I, Smith T M, Emanuel W R (1995). A global land primary productivity and phytogeography model. Global Biogeochem Cycles, 9(4): 471–490 https://doi.org/10.1029/95GB02432
65	Xie B N, Qin Z F, Wang Y, Chang Q R (2014). Spatial and temporal variation in terrestrial net primary productivity on Chinese Loess Plateau and its influential factors. Transactions of the Chinese Society of Agricultural Engineering, 30(11): 244–253 (in Chinese)
66	Xu J H (2010). Geographic Modeling Method. Beijing: Science Press (in Chinese)
67	Yu D Y, Shao H B, Shi P J, Zhu W Q, Pan Y Z (2009). How does the conversion of land cover to urban use affect net primary productivity? A case study in Shenzhen city, China. Agric Meteorol, 149(11): 2054–2060 https://doi.org/10.1016/j.agrformet.2009.07.012
68	Yu D Y, Shi P J, Han G Y, Zhu W Q, Du S Q, Xun B (2011). Forest ecosystem restoration due to a national conservation plan in China. Ecol Eng, 37(9): 1387–1397 https://doi.org/10.1016/j.ecoleng.2011.03.011
69	Zhao G S, Wang J B, Fan W Y, Ying T Y (2011). Vegetation net primary productivity in Northeast China in 2000–2008: simulation and seasonal change. Chinese J Appl Ecol, 22(3): 621–630 (in Chinese)
70	Zhao S Q (1983). A new scheme for comprehensive physical regionalization in China. Acta Geogr Sin, 38(1): 1–10 (in Chinese)
71	Zheng S H, Zhao M L, Shan D, Pan L R, Han G D (2005). Range condition and its evaluation. Grassland of China, 27(2): 72–76 (in Chinese)
72	Zhou W, Gang C C, Zhou L, Chen Y Z, Li J L, Ju W M, Odeh I (2014). Dynamic of grassland vegetation degradation and its quantitative assessment in the northwest China. Acta Oecol, 55(2): 86–96 https://doi.org/10.1016/j.actao.2013.12.006
73	Zhu W Q, Pan Y Z, Long Z H, Chen Y H, Li J, Hu H B (2005). Estimating net primary productivity of terrestrial vegetation based on GIS and RS: a case study in Inner Mongolia, China. J Remote Sens, 9(3): 300–307 (in Chinese)

[1]	Conghui LI, Lili LIN, Zhenbang HAO, Christopher J. POST, Zhanghao CHEN, Jian LIU, Kunyong YU. Developing a USLE cover and management factor (C) for forested regions of southern China[J]. Front. Earth Sci., 2020, 14(3): 660-672.
[2]	Emre ÇOLAK, Filiz SUNAR. Spatial pattern analysis of post-fire damages in the Menderes District of Turkey[J]. Front. Earth Sci., 2020, 14(2): 446-461.
[3]	Jianhong LIU, Clement ATZBERGER, Xin HUANG, Kejian SHEN, Yongmei LIU, Lei WANG. *Modeling grass yields in Qinghai Province, China, based on MODIS NDVI* data—an empirical comparison**[J]. Front. Earth Sci., 2020, 14(2): 413-429.
[4]	Xia LEI, Jiayi PAN, Adam DEVLIN. An ultraviolet to visible scheme to estimate chromophoric dissolved organic matter absorption in a Case-2 water from remote sensing reflectance[J]. Front. Earth Sci., 2020, 14(2): 384-400.
[5]	Tong LI, Huadong GUO, Li ZHANG, Chenwei NIE, Jingjuan LIAO, Guang LIU. Simulation of Moon-based Earth observation optical image processing methods for global change study[J]. Front. Earth Sci., 2020, 14(1): 236-250.
[6]	Jianwen WANG, Da ZHANG, Ying NAN, Zhifeng LIU, Dekang QI. Spatial patterns of net primary productivity and its driving forces: a multi-scale analysis in the transnational area of the Tumen River[J]. Front. Earth Sci., 2020, 14(1): 124-139.
[7]	Kaiguo FAN, Huaguo ZHANG, Jianjun LIANG, Peng CHEN, Bojian XU, Ming ZHANG. Analysis of ship wake features and extraction of ship motion parameters from SAR images in the Yellow Sea[J]. Front. Earth Sci., 2019, 13(3): 588-595.
[8]	Ying XIONG, Fen PENG, Bin ZOU. Spatiotemporal influences of land use/cover changes on the heat island effect in rapid urbanization area[J]. Front. Earth Sci., 2019, 13(3): 614-627.
[9]	Shichao CUI, Kefa ZHOU, Rufu DING, Guo JIANG. Estimation of copper concentration of rocks using hyperspectral technology[J]. Front. Earth Sci., 2019, 13(3): 563-574.
[10]	Qianfeng WANG, Jingyu ZENG, Song LENG, Bingxiong FAN, Jia TANG, Cong JIANG, Yi HUANG, Qing ZHANG, Yanping QU, Wulin WANG, Wei SHUI. The effects of air temperature and precipitation on the net primary productivity in China during the early 21st century[J]. Front. Earth Sci., 2018, 12(4): 818-833.
[11]	Igor Appel. Uncertainty in satellite remote sensing of snow fraction for water resources management[J]. Front. Earth Sci., 2018, 12(4): 711-727.
[12]	Donal O’Leary III, Dorothy Hall, Michael Medler, Aquila Flower. Quantifying the early snowmelt event of 2015 in the Cascade Mountains, USA by developing and validating MODIS-based snowmelt timing maps[J]. Front. Earth Sci., 2018, 12(4): 693-710.
[13]	Mohamed Abd Salam EL-VILALY, Kamel DIDAN, Stuart E. MARSH, Willem J.D. VAN LEEUWEN, Michael A. CRIMMINS, Armando Barreto MUNOZ. Vegetation productivity responses to drought on tribal lands in the four corners region of the Southwest USA[J]. Front. Earth Sci., 2018, 12(1): 37-51.
[14]	Duanyang XU, Alin SONG, Xiao SONG. Assessing the effect of desertification controlling projects and policies in northern Shaanxi Province, China by integrating remote sensing and farmer investigation data[J]. Front. Earth Sci., 2017, 11(4): 689-701.
[15]	Sizhang TANG,Chaomin SHEN,Guixu ZHANG. Adaptive regularized scheme for remote sensing image fusion[J]. Front. Earth Sci., 2016, 10(2): 236-244.

Viewed

Full text

Abstract

Cited

Shared

Discussed