Applicability of MODIS and VIIRS vegetation index products in estimating grassland biomass in Gannan

ZHANG Zhongxue; FENG Qisheng; WANG Ruijing; JIN Zheren; LIANG Tiangang

doi:10.11829/j.issn.1001-0629.2022-0377

Pratacultural Science > 2023 > 40(9): 2199-2212. > DOI: 10.11829/j.issn.1001-0629.2022-0377

ZHANG Z X, FENG Q S, WANG R J, JIN Z R, LIANG T G. Applicability of MODIS and VIIRS vegetation index products in estimating grassland biomass in Gannan. Pratacultural Science, 2023, 40(9): 2199-2212. DOI: 10.11829/j.issn.1001-0629.2022-0377

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Applicability of MODIS and VIIRS vegetation index products in estimating grassland biomass in Gannan

College of Pastoral Agriculture Science and Technology, Lanzhou University, Lanzhou, 730020, Gansu, China

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Corresponding author:
FENG Qisheng　E-mail: fengqsh@lzu.edu.cn
Received Date: May 04, 2022
Accepted Date: September 06, 2022
Available Online: May 10, 2023

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Abstract

Abstract

At present, the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor is in the extended service stage. The First Visible Infrared Imager Radiometer Suite (VIIRS) sensor is a further development of the MODIS sensor. With Gannan region as an example, the Enhanced Vegetation Index (EVI), provided by four vegetation cover products of MODIS and two vegetation cover products of VIIRS, were selected in this study. The EVI and Normalized Difference Vegetation Index (NDVI) were used to analyze the difference between MODIS and VIIRS in grassland above-ground biomass estimation. The main conclusions are as follows: 1) Compared with VIIRS, the accuracy of the MODIS-based grassland above-ground biomass inversion model in the Gannan region was higher, and MODIS MOD13A1 is more suitable for the estimation of grassland above-ground biomass in the Gannan region. 2) The areas with higher aboveground biomass (> 3000 kg·ha⁻¹) were mainly distributed in Maqu, Luqu, and Hezuo; while the areas with lower aboveground biomass (< 1500 kg·ha⁻¹) were mainly distributed in Xiahe and Dibu. The aboveground biomass of grassland in the western region was higher than that of the non-grassland in the eastern region. 3) The areas with large biomass increase were mainly distributed in the north of Xiahe, the northwest of Maqu, and the middle of Luqu; while the areas with large biomass decrease were mainly distributed in the southeast of Maqu and the south of Luqu.
- above-ground biomass,
- comparison of vegetation index products,
- Gannan,
- inversion,
- spatial distribution

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References

[1]	WANG R J, FENG Q S, JIN Z R, LIANG T G. The restoration potential of the grasslands on the Tibetan Plateau. Remote Sensing, 2022, 14(1).
[2]	张雨欣, 黄健熙, 金云翔, 王洁, 赵圆圆, 冯权泷, 马钦. 草地地上生物量估算模型研究进展. 草地学报, 2022, 30(4): 850-858. ZHANG Y X, HUANG J X, JIN Y X, WANG J, ZHAO Y Y, FENG Q L, MA Q. Estimation of grasslands aboveground biomass: A review. Acta Agrestia Sinica, 2022, 30(4): 850-858.
[3]	XU D W, WANG C, CHEN J, SHEN M G, SHEN B B, YAN R R, LI Z W, KARNIELI A, CHEN J Q, YAN Y C, WANG X, CHEN B R, YIN D M, XIN X P. The superiority of the normalized difference phenology index (NDPI) for estimating grassland aboveground fresh biomass. Remote Sensing of Environment, 2021, 264:112578.
[4]	WANG X Y, HE X C, PRICE M, HE Q Y, ZHANG P, RAN J H, WU Y J. Epigeic arthropod community changes in response to livestock-caused alpine grassland degradation on the eastern Qinghai-Tibetan Plateau. Global Ecology and Conservation, 2022, 35
[5]	孟令奎, 李晓香, 张文. 植被覆盖区VIIRS与MODIS遥感指数的相关性. 江苏农业学报, 2018, 34(3): 570-577. doi: 10.3969/j.issn.1000-4440.2018.03.013 MENG L K, LI X X, ZHANG W. The relationship between VIIRS and MODIS remote sensing index in vegetation coverage area. Jiangsu Journal of Agricultural Sciences, 2018, 34(3): 570-577. doi: 10.3969/j.issn.1000-4440.2018.03.013
[6]	BREWSTER R, HOOD B, HAMILTON E, BURNS R, STATES D, SILVA S. Suomi-npp: Recent experiences, Advances in the Astronautical Sciences, 2019, 151: 839-850.
[7]	LI Y H, XIONG X X, MCLNTIRE J, WU A S. Comparison of the MODIS and VIIRS thermal emissive band radiometric calibration, 2020, 58(7): 4852-4859.
[8]	姜梦蝶, 陈林, 何玉青, 胡秀清, 刘明奇, 张鹏. 利用NPP/VIIRS微光数据反演夜间气溶胶光学厚度. 遥感学报, 2022, 26(3): 493-504. doi: 10.11834/j.issn.1007-4619.2022.3.ygxb202203006 JIANG M D, CHEN L, HE Y Q, HU X Q, LIU M Q, ZHANG P. Nighttime aerosol optical depth retrievals from VIIRS day/night band data. National Remote Sensing Bulletin, 2022, 26(3): 493-504. doi: 10.11834/j.issn.1007-4619.2022.3.ygxb202203006
[9]	SMALL C, ELVIDGE C D, BAUGH K. Mapping urban structure and spatial connectivity with VIIRS and OLS night light imagery. Joint Urban Remote Sensing Event, 2013,24: 230-233.
[10]	WANG M J, WANG Y J, TENG F, LI S C, LIN Y H, CAI H F. Estimation and analysis of PM2.5 concentrations with NPP-VIIRS nighttime light images: A case study in the Chang-Zhu-Tan urban agglomeration of China. International Journal of Environmental Research and Public Health, 2022, 19(7): 46-47.
[11]	LI F, WANG Q L, HU W J, LIU J, ZHANG X B. Rapid assessment of disaster damage and economic resilience in relation to the flooding in Zhengzhou, China in 2021. Remote Sensing Letters, 2022, 13(7): 651-662.
[12]	LI Y, XIONG X, MCINTIRE J, WU A. Comparison of the MODIS and VIIRS Thermal Emissive Band Radiometric Calibration. IEEE Transactions on Geoscience and Remote Sensing, 2020, 58(7): 4852-4859. doi: 10.1109/TGRS.2020.2968037
[13]	LIU S, XIE Z H, GAO J Q, ZENG Y J, LIU B, LI R C. Response of carbon and water cycles to climate change in the high-frigid ecotone: A case study of Gannan zone. Plateau Meteorology, 2018, 37(5): 1177-1187.
[14]	刘旻霞, 张国娟, 李亮, 穆若兰, 徐璐, 于瑞新. 甘南高寒草甸海拔梯度上功能多样性与生态系统多功能的关系. 应用生态学报, 2022, 33(5): 1291-1299. doi: 10.13287/j.1001-9332.202205.003 LIU M X, ZHANG G J, LI L, MU R L, XU L, YU R X. Relationship between functional diversity and ecosystem multifunctionality of alpine meadow along an altitude gradient in Gannan, China. Chinese Journal of Applied Ecology, 2022, 33(5): 1291-1299. doi: 10.13287/j.1001-9332.202205.003
[15]	TUCKER C J. Maximum normalized difference vegetation index images for sub-Saharan africa for 1983－1985. International Journal of Remote Sensing, 1986, 7(11): 1383-1384.
[16]	HUETE A. A comparison of vegetation indices over a global set of TM images for EOS-MODIS. Remote Sensing of Environment, 1997, 59(3): 440-451.
[17]	BREIMAN L. Random forests. Machine Learning, 2001, 45(1): 5-32. doi: 10.1023/A:1010933404324
[18]	陈海洋, 孟令奎, 周元. 基于随机森林的遥感影像雪冰云信息检测方法. 测绘地理信息, 2022, 47(2): 105-110. doi: 10.14188/j.2095-6045.2019193 CHEN H Y, MENG L K, ZHOU Y. Ice snow and cloud detection in remote sensing images based on random forest. Journal of Geomatics, 2022, 47(2): 105-110. doi: 10.14188/j.2095-6045.2019193
[19]	HEDIGER S, MICHEL L, NAF J. On the use of random forest for two-sample testing. Computational Statistics & Data Analysis, 2022, 170.
[20]	LIU M, LANG R L, CAO Y B. Number of trees in random forest. Computer Engineering and Applications, 2015, 51(5): 126-131.
[21]	高宏元, 侯蒙京, 葛静, 包旭莹, 李元春, 刘洁, 冯琦胜, 梁天刚, 贺金生, 钱大文. 基于随机森林的高寒草地地上生物量高光谱估算. 草地学报, 2021, 29(8): 1757-1768. GAO H Y, HOU M J, GE J, BAO X Y, LI Y C, LIU J, FENG Q S, LIANG T G, HE J S, QIAN D W. Hyperspectraal estimation of aboveground biomass of alpine grassland based on random forest algorithm. Acta Agrestia Sinica, 2021, 29(8): 1757-1768.
[22]	赵玮, 温小霓. 应用统计学教程. 西安: 电子科技大学出版社, 2003. ZHAO W, WEN X N. A Course in Applied Statistics. Xi'an: University of Electronic Technology of China Press, 2003.
[23]	ZHAO Y, WANG J W, CHEN L P, FU Y Y, ZHU H C, FENG H K, XU X G, LI Z H. An entirely new approach based on remote sensing data to calculate the nitrogen nutrition index of winter wheat. Journal of Integrative Agriculture, 2021, 20(9): 2535-2551. doi: 10.1016/S2095-3119(20)63379-2
[24]	GANG F, WEI S, SHAO W L, JING Z, CHENG Q Y, ZHEN X, S. Modeling aboveground biomass using MODIS images and climatic data in grasslands on the Tibetan plateau. Journal of Resources and Ecology, 2018, 218: 162-173.
[25]	王佃来, 宿爱霞, 刘文萍. 几种植被覆盖变化趋势分析方法对比研究. 安徽农业科学, 2019, 47(5): 10-14. doi: 10.3969/j.issn.0517-6611.2019.05.003 WANG D L, SU A X, LIU W P. Comparison study on several trend analytical methods of vegetation cover changes. Journal of Anhui Agricultural Sciences, 2019, 47(5): 10-14. doi: 10.3969/j.issn.0517-6611.2019.05.003
[26]	陆荫, 杨淑霞, 李晓红. 甘南州高寒天然草地生长状况遥感监测. 草业科学, 2021, 38(1): 32-43. doi: 10.11829/j.issn.1001-0629.2020-0092 LU Y, YANG S X, LI X H. Monitoring of grassland herbage accumulation by using remote sensing in Gannan Prefecture. Pratacultural Science, 2021, 38(1): 32-43. doi: 10.11829/j.issn.1001-0629.2020-0092
[27]	于惠, 吴玉锋, 金毅, 张峰. 基于MODIS SWIR数据的干旱区草地地上生物量反演及时空变化研究. 遥感技术与应用, 2017, 32(3): 524-530. YU H, WU Y F, JIN Y, ZHANG F. A approach for monitoring the aboveground biomass of grassland in arid areas based on MODIS SWIR bands. Remote Sensing Technology and Application, 2017, 32(3): 524-530.
[28]	MENG B P, GE J, LIANG T G, YANG S X, GAO J L, FENG Q S, CUI X, HUANG X D, XIE H J. Evaluation of remote sensing inversion error for the above-ground biomass of alpine meadow grassland based on multi-source satellite data. Remote Sensing, 2017, 9(4): 372.
[29]	ZHANG X Z, LI M, WU J S, HE Y T, NIU B. Alpine grassland aboveground biomass and theoretical livestock carrying capacity on the Tibetan plateau. Journal of Resources and Ecology, 2022, 13(1): 129-141.

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