Please wait a minute...
 
Remote Sensing for Natural Resources    2024, Vol. 36 Issue (4) : 92-106     DOI: 10.6046/zrzyyg.2023308
|
Comprehensive evaluation of ESA CCI soil moisture data in eastern China
LING Xiaolu1,2(), CHEN Chaorong3, GUO Weidong4, QIN Kai1,2, ZHANG Jinlong5
1. School of Environment and Spatial Informatics, China University of Mining and Technology, Xuzhou 221116, China
2. Jiangsu Key Laboratory of Coal-based Greenhouse Gas Control and Utilization, China University of Mining and Technology, Xuzhou 221008, Jiangsu, China
3. School of Geographical Sciences, Qinghai Normal University, Xining 810008, China
4. School of Atmospheric Sciences, Nanjing University, Nanjing 210023, China
5. Jiangsu CYP Light Heat Electronics Industrial Technology Research Insitute, Ltd., Xuzhou 221116, China
Download: PDF(10199 KB)   HTML
Export: BibTeX | EndNote | Reference Manager | ProCite | RefWorks    
Abstract  

Soil moisture products based on remote sensing are crucial for investigating climatic change and hydrological effects on a regional scale. However, there is a lack of verification and application of long-term soil moisture datasets in China due to factors such as inconsistent observation standards and instrument upgrades. Using the agro-meteorological dataset from the China Meteorological Administration and soil moisture data from the International Soil Moisture Network (ISMN), this study constructed a monthly dataset of soil moisture in eastern China covering the period from 1981 to 2013. Accordingly, this study analyzed and compared the performance of four microwave remote sensing-based soil moisture products developed by the European Space Agency’s Climate Change Initiative (ESA CCI): active, passive, combined, and combined adjusted products. The results indicate that active and passive products underestimated and overestimated soil moisture in eastern China, respectively. The maximum deviations from active products were found in the northern and northwestern regions, with relative deviations of -30.9% and -29.6%, respectively. In contrast, the passive products showed relative deviations of 39.1% and 26.5%, respectively for soil moisture in northeastern and northwestern regions. The combined products mitigated the underestimation of the active products and the overestimation of the passive product in these regions, reducing the relative deviations to 24.3% and 3.7%, respectively. Regarding the variation characteristics of regional monthly average soil moisture, both the active and combined products performed best for soil moisture in the Yangtze-Huaihe (YH) region, with the highest correlation coefficient of 0.66. The passive and combined products yielded correlation coefficients of 0.44 and 0.47, respectively for soil moisture in the northeastern region and performed poorly for soil moisture in the northern and northwestern regions. The analysis of the variance sources of the remote sensing-based products indicates that the active products enjoyed more advantages in describing the evolutionary characteristics of soil moisture, the passive products demonstrated greater accuracy, and the combined products yielded the highest accuracy overall. Additionally, this study investigated the impacts of changes in the integrated satellite equipment of CCI on product performance. The results indicate that the active products exhibited consistent performance for soil moisture in the northeastern and northwestern regions in different periods. However, passive sensors still exhibited gaps in reproducing the variations in soil moisture. The combined products exhibited better overall variance than both active and passive products. However, these products yielded comparable correlation coefficients with the active products for soil moisture in the northeastern and northwestern regions. The combined products presented no notable improvement after correction, proving that it is feasible to conduct long-term research using the combined products of CCI. The results of this study contribute to a deeper understanding of the error structures and characteristics of various satellite product datasets, providing evidence for researchers to select appropriate data products and conduct research on long time series.
Keywords soil moisture      satellite remote sensing      instrument replacement      long time series      ESA CCI      eastern China      break-adjusted COMBINED product      comprehensive evaluation     
ZTFLH:  TP79  
Issue Date: 23 December 2024
Service
E-mail this article
E-mail Alert
RSS
Articles by authors
Xiaolu LING
Chaorong CHEN
Weidong GUO
Kai QIN
Jinlong ZHANG
Cite this article:   
Xiaolu LING,Chaorong CHEN,Weidong GUO, et al. Comprehensive evaluation of ESA CCI soil moisture data in eastern China[J]. Remote Sensing for Natural Resources, 2024, 36(4): 92-106.
URL:  
https://www.gtzyyg.com/EN/10.6046/zrzyyg.2023308     OR     https://www.gtzyyg.com/EN/Y2024/V36/I4/92
Fig.1  Spatial distribution of soil moisture observation stations and four research regions, as well as land surface types
Fig.2  Spatial distribution of soil moisture during growing seasons (from April to September) for the period of 1992 to 2013
Fig.3  Spatial distribution of the bias of soil moisture during growing seasons (from April to September) for the period of 1992 to 2013
Fig.4-1  Spatial distribution of μbRMSE of soil moisture during growing seasons (from April to September) for the period of 1992 to 2013
Fig.4-2  Spatial distribution of μbRMSE of soil moisture during growing seasons (from April to September) for the period of 1992 to 2013
Fig.5  Taylor diagram of remotely sensed products and observations for the period of 1992 to 2013
区域 产品 Bias rBias RMSD rRMSD μbRMSD R
东北 主动产品 -0.066 -21.4% 0.103 42.7% 0.079 0.41
被动产品 0.056 39.1% 0.128 52.8% 0.115 0.16
融合产品 0.022 24.3% 0.095 41.4% 0.092 0.15
校正后融合 0.022 24.3% 0.095 41.4% 0.092 0.15
华北 主动产品 -0.085 -30.9% 0.112 45.5% 0.072 0.38
被动产品 0.002 7.5% 0.094 41.1% 0.094 0.27
融合产品 0.024 20.3% 0.085 37.6% 0.081 0.22
校正后融合 0.030 22.8% 0.085 37.9% 0.080 0.23
江淮 主动产品 -0.084 -29.6% 0.113 45.1% 0.076 0.41
被动产品 -0.007 3.6% 0.101 41.8% 0.100 0.23
融合产品 0.039 26.4% 0.097 41.0% 0.089 0.16
校正后融合 0.046 29.8% 0.099 41.9% 0.087 0.15
西北 主动产品 -0.075 -25.8% 0.106 43.1% 0.076 0.26
被动产品 0.041 26.5% 0.145 61.3% 0.139 0.07
融合产品 -0.009 3.7% 0.087 36.1% 0.087 0.15
校正后融合 -0.005 5.6% 0.086 35.8% 0.086 0.15
Tab.1  Correlation coefficients, biases, and RMSDs of the five data sets for growing seasons from 1981 to 2013
Fig.6  Time series of soil moisture in four regions from 1981 to 2013
Fig.7  The decomposition of three terms to the mean square errors (MSEs) for the four satellite products from 1981 to 2013
产品
类型		 时间段 主动传感器 被动传感器
主
动
产
品		 1992年01月—1997年05月 ERS1/2 (AMI-WS) N/A
1997年05月—2003年02月 ERS2 (AMI-WS) N/A
2003年02月—2006年12月 ERS1/2 (AMI-WS) N/A
2007年01月—2012年11月 Metop-A ASCAT N/A
2012年11月—2013年12月 Metop-A ASCAT, Metop-B ASCAT N/A
被
动
产
品		 1992年01月—1997年12月 N/A SSM/I (a/d)
1998年01月—2002年06月 N/A SSM/I (a/d), TMI (a/d)
2002年07月—2007年09月 N/A AMSR-E (a/d), TMI (a/d)
2007年10月—2010年01月 N/A AMSR-E (a/d), Windsat (a/d), TMI (a/d)
2010年01月—2011年05月 N/A AMSR-E (a/d), WindSat (a/d), SMOS (a/d), TMI (a/d)
2011年06月—2011年09月 N/A AMSR-E (a/d), WindSat (a/d), SMOS (a/d), TMI (a/d), FY3B (a/d)
2011年10月—2012年06月 N/A WindSat (a/d), SMOS (a/d), TMI (a/d), FY-3B (a/d)
2012年07月—2013年09月 N/A SMOS (a/d), AMSR2 (a/d), TMI (a/d), FY-3B (a/d)
2013年10月—2013年12月 N/A SMOS (a/d), AMSR2 (a/d), TMI (a/d), FY-3B (a/d), FY-3C (a/d)
融
合
产
品		 1992年01月—1997年12月 AMI-WS SSM/I (a/d)
1998年01月—2002年06月 AMI-WS SSM/I (a/d), TMI (a/d)
2002年07月—2006年12月 AMI-WS AMSR-E (a/d), TMI (a/d)
2007年01月—2007年09月 Metop-A ASCAT AMSR-E (a/d), TMI (a/d)
2007年10月—2010年01月 Metop-A ASCAT AMSR-E (a/d), Windsat (a/d), TMI (a/d)
2010年01月—2011年05月 Metop-A ASCAT AMSR-E (a/d), WindSat (a/d), SMOS (a/d), TMI (a/d)
2011年06月—2011年09月 Metop-A ASCAT AMSR-E (a/d), WindSat (a/d), SMOS (a/d), TMI (a/d), FY3B (a/d)
2011年10月—2012年06月 Metop-A ASCAT WindSat (a/d), SMOS (a/d), TMI (a/d), FY-3B (a/d)
2012年07月—2012年10月 Metop-A ASCAT SMOS (a/d), AMSR2 (a/d), TMI (a/d), FY-3B (a/d)
2012年11月—2013年09月 Metop-A ASCAT, Metop-B ASCAT SMOS (a/d), AMSR2 (a/d), TMI (a/d), FY-3B (a/d)
2013年10月—2013年12月 Metop-A ASCAT, Metop-B ASCAT SMOS (a/d), AMSR2 (a/d), TMI (a/d), FY-3B (a/d), FY-3C (a/d)
Tab.2  Time segments and list of sensors for ESA CCI SM products
Fig.8  The decomposition of three terms to the mean square errors (MSEs) for the four satellite products from 1992 to 2013 over different regions during different time segments
Fig.9  The decomposition of three terms to the mean square errors (MSEs) for the COMBINED and ADJUSTED COMBINED products from 1992 to 2013 over different regions
Fig.10  Relative bias of monthly soil moisture for ACTIVE, PASSIVE, COMBINED, and ADJUSTED COMBINED products in different numerical ranges
[1] Dirmeyer P A. Using a global soil wetness dataset to improve seasonal climate simulation[J]. Journal of Climate, 2000, 13(16):2900-2922.
[2] Koster R D, Dirmeyer P A, Guo Z, et al. Regions of strong coupling between soil moisture and precipitation[J]. Science, 2004, 305(5687):1138-1140.
pmid: 15326351
[3] McColl K A, Wang W, Peng B, et al. Global characterization of surface soil moisture drydowns[J]. Geophysical Research Letters, 2017, 44(8):3682-3690.
[4] 郭维栋, 马柱国, 王会军. 土壤湿度——一个跨季度降水预测中的重要因子及其应用探讨[J]. 气候与环境研究, 2007, 12(1):20-28.
[4] Guo W D, Ma Z G, Wang H J. Soil moisture—An important factor of seasonal precipitation prediction and its application[J]. Climatic and Environmental Research, 2007, 12(1):20-28.
[5] Komma J, Blöschl G, Reszler C. Soil moisture updating by Ensemble Kalman Filtering in real-time flood forecasting[J]. Journal of Hydrology, 2008, 357(3-4):228-242.
[6] Seneviratne S I, Corti T, Davin E L, et al. Investigating soil moisture-climate interactions in a changing climate:A review[J]. Earth-Science Reviews, 2010, 99(3-4):125-161.
[7] 张文君, 周天军, 智海. 土壤湿度影响中国夏季气候的数值试验[J]. 气象学报, 2012, 70(1):78-90.
[7] Zhang W J, Zhou T J, Zhi H. A numerical experiment on the effect of soil moisture to summer climate over China[J]. Acta Meteorologica Sinica, 2012, 70(1):78-90.
[8] Dorigo W A, Wagner W, Hohensinn R, et al. The international soil moisture network:A data hosting facility for global in situ soil moisture measurements[J]. Hydrology and Earth System Sciences, 2011, 15(5):1675-1698.
[9] Dorigo W, Himmelbauer I, Aberer D, et al. The international soil moisture network:Serving earth system science for over a decade[J]. Hydrology and Earth System Sciences, 2021, 25(11):5749-5804.
[10] Lawrence D M, Oleson K W, Flanner M G, et al. Parameterization improvements and functional and structural advances in version 4 of the Community Land Model[J]. Journal of Advances in Modeling Earth Systems, 2011, 3:M03001.
[11] Xue Y, Sellers P J, Kinter J L, et al. A simplified biosphere model for global climate studies[J]. Journal of Climate, 1991, 4(3):345-364.
[12] Kalnay E, Kanamitsu M, Kistler R, et al. The NCEP/NCAR 40-year reanalysis project[J]. Bulletin of the American Meteorological Society, 1996, 77(3):437-472.
[13] 师春香, 谢正辉, 钱辉, 等. 基于卫星遥感资料的中国区域土壤湿度EnKF数据同化[J]. 中国科学:地球科学, 2011, 41(3):375-385.
[13] Shi C X, Xie Z H, Qian H, et al. China land soil moisture EnKF data assimilation based on satellite remote sensing data[J]. Scientia Sinica Terrae, 2011, 41(3):375-385.
[14] Kanamitsu M, Ebisuzaki W, Woollen J, et al. NCEP-DOE AMIP-II reanalysis (R-2)[J]. Bulletin of the American Meteorological Society, 2002, 83(11):1631-1643.
[15] Mitchell K E, Lohmann D, Houser P R, et al. The multi-institution North American Land Data Assimilation System (NLDAS):Utilizing multiple GCIP products and partners in a continental distributed hydrological modeling system[J]. Journal of Geophysical Research:Atmospheres, 2004, 109:D07S90.
[16] Rodell M, Houser P R, Jambor U, et al. The global land data assimilation system[J]. Bulletin of the American Meteorological Society, 2004, 85(3):381-394.
[17] Dee D P, Uppala S M, Simmons A J, et al. The ERA-Interim reanalysis:configuration and performance of the data assimilation system[J]. Quarterly Journal of the Royal Meteorological Society, 2011, 137(656):553-597.
[18] Xia Y, Mitchell K, Ek M, et al. Continental-scale water and energy flux analysis and validation for North American Land Data Assimilation System Project phase 2 (NLDAS-2):2.Validation of model-simulated streamflow[J]. Journal of Geophysical Research:Atmospheres, 2012, 117:D03109.
[19] 陈书林, 刘元波, 温作民. 卫星遥感反演土壤水分研究综述[J]. 地球科学进展, 2012, 27(11):1192-1203.
doi: 10.11867/j.issn.1001-8166.2012.11.1192
[19] Chen S L, Liu Y B, Wen Z M. Satellite retrieval of soil moisture:An overview[J]. Advances in Earth Science, 2012, 27(11):1192-1203.
[20] Liu W, Wang J, Xu F, et al. Validation of four satellite-derived soil moisture products using ground-based in situ observations over northern China[J]. Remote Sensing, 2022, 14(6):1419.
[21] 王逸男, 安如, 尤加俊, 等. CCI土壤湿度产品在中国区的降尺度与融合研究[J]. 河海大学学报(自然科学版), 2018, 46(3):196-202.
[21] Wang Y N, An R, You J J, et al. Downscaling and merging of the CCI soil moisture product over China[J]. Journal of Hohai University (Natural Sciences), 2018, 46(3):196-202.
[22] 姚晓磊, 鱼京善, 李占杰, 等. CCI遥感土壤水在东北粮食主产区表征干旱的准确性评估[J]. 北京师范大学学报(自然科学版), 2019, 55(2):233-239.
[22] Yao X L, Yu J S, Li Z J, et al. Accuracy evaluation of the ESA CCI remote sensing soil moisture for revealing drought in Northeast China[J]. Journal of Beijing Normal University (Natural Science), 2019, 55(2):233-239.
[23] 李昂, 陆其峰, 杨晓峰, 等. AMSR-E卫星反演土壤湿度与ECWMF、NECP再分析土壤湿度比较分析[J]. 遥感技术与应用, 2013, 28(4):666-673.
doi: 10.11873/j.issn.1004-0323.2013.4.666
[23] Li A, Lu Q F, Yang X F, et al. AMSR-E soil moisture compared with ECWMF and NECP soil moisture[J]. Remote Sensing Technology and Application, 2013, 28(4):666-673.
[24] 李明星, 马柱国, 牛国跃. 中国区域土壤湿度变化的时空特征模拟研究[J]. 科学通报, 2011, 56(16):1288-1300.
[24] Li M X, Ma Z G, Niu G Y. Modeling spatial and temporal variations in soil moisture in China[J]. Chinese Science Bulletin, 2011, 56(16):1288-1300.
[25] Ling X, Huang Y, Guo W, et al. Comprehensive evaluation of sate-llite-based and reanalysis soil moisture products using in situ observations over China[J]. Hydrology and Earth System Sciences, 2021, 25(7):4209-4229.
[26] 刘丽伟, 魏栋, 王小巍, 等. 多种土壤湿度资料在中国地区的对比分析[J]. 干旱气象, 2019, 37(1):40-47.
[26] Liu L W, Wei D, Wang X W, et al. Multi-data intercomparison of soil moisture over China[J]. Journal of Arid Meteorology, 2019, 37(1):40-47.
[27] 朱智, 师春香, 张涛, 等. 四套再分析土壤湿度资料在中国区域的适用性分析[J]. 高原气象, 2018, 37(1):240-252.
doi: 10.7522/j.issn.1000-0534.2017.00033
[27] Zhu Z, Shi C X, Zhang T, et al. Applicability analysis of four soil moisture reanalysis datasets in China[J]. Plateau Meteorology, 2018, 37(1):240-252.
doi: 10.7522/j.issn.1000-0534.2017.00033
[28] Albergel C, de Rosnay P, Gruhier C, et al. Evaluation of remotely sensed and modelled soil moisture products using global ground-based in situ observations[J]. Remote Sensing of Environment, 2012, 118:215-226.
[29] Chen Y Y, Yang K, Qin J, et al. Evaluation of SMAP,SMOS,and AMSR2 soil moisture retrievals against observations from two networks on the Tibetan Plateau[J]. Journal of Geophysical Research:Atmospheres, 2017, 122(11):5780-5792.
[30] An R, Zhang L, Wang Z, et al. Validation of the ESA CCI soil moisture product in China[J]. International Journal of Applied Earth Observation and Geoinformation, 2016, 48:28-36.
[31] González-Zamora Á, Sánchez N, Pablos M, et al. CCI soil moisture assessment with SMOS soil moisture and in situ data under different environmental conditions and spatial scales in Spain[J]. Remote Sensing of Environment, 2019, 225:469-482.
[32] Dorigo W, Wagner W, Albergel C, et al. ESA CCI Soil Moisture for improved earth system understanding:State-of-the art and future directions[J]. Remote Sensing of Environment, 2017, 203:185-215.
[33] Gruber A, Scanlon T, van der Schalie R, et al. Evolution of the ESA CCI soil moisture climate data records and their underlying merging methodology[J]. Earth System Science Data, 2019, 11(2):717-739.
[34] Friedl M, Sulla-Menashe D. MCD12C1 MODIS/Terra+Aqua Land Cover Type Yearly L3 Global 0.05Deg CMG V006 [Data set].NASA EOSDIS Land Processes Distributed Active Archive Center.Accessed 2023-01-19 from https://doi.org/10.5067/MODIS/MCD12C1.006.
[35] Gupta H V, Kling H, Yilmaz K K, et al. Decomposition of the mean squared error and NSE performance criteria:Implications for improving hydrological modelling[J]. Journal of Hydrology, 2009, 377(1/2):80-91.
[36] Dorigo W A, Scipal K, Parinussa R M, et al. Error characterisation of global active and passive microwave soil moisture datasets[J]. Hydrology and Earth System Sciences, 2010, 14(12):2605-2616.
[37] Conrad V, Pollak L W. Methods in climatology[J]. Quarterly Journal of the Royal Meteorology Society, 1951, 77(332):328-330.
[38] Peterson T C, Easterling D R, Karl T R, et al. Homogeneity adjustments of in situ atmospheric climate data:A review[J]. International Journal of Climatology, 1998, 18(13):1493-1517.
[39] Preimesberger W, Scanlon T, Su C H, et al. Homogenization of structural breaks in the global ESA CCI soil moisture multisatellite climate data record[J]. IEEE Transactions on Geoscience and Remote Sensing, 2021, 59(4):2845-2862.
doi: 10.1109/TGRS.2020.3012896
[40] Earth Observation Data Center for Water Resources Monitoring EODC GmbH, TU Wien, VanderSat, CESBIO and ETH Zürich. ESA Climate Change Initiative Plus-Soil Moisture,Algorithm Theoretical Baseline Document (ATBD) Supporting Product Version 07.1,D2.1 Version 3[Z]. 2022:14-15.
[41] Ulaby F T, Long D G, Blackwell W J, et al. Microwave radar and radiometric remote sensing[M]. University of Michigan Press, 2014:569-570.
[42] Albergel C, Rüdiger C, Pellarin T, et al. From near-surface to root-zone soil moisture using an exponential filter:An assessment of the method based on in situ observations and model simulations[J]. Hydrology and Earth System Sciences, 2008, 12(6):1323-1337.
[1] YANG Chenfei, WU Tianjun, WANG Changpeng, YANG Lijuan, LUO Jiancheng, ZHANG Xin. Construction and application of a comprehensive drought index based on Copula function on a kilometer scale: A case study of Chongqing, China[J]. Remote Sensing for Natural Resources, 2024, 36(3): 117-127.
[2] ZHANG Chungui, PENG Jida. Air freshness monitoring technology based on meteorology and remote sensing[J]. Remote Sensing for Natural Resources, 2024, 36(3): 163-173.
[3] LUO Wei, LI Xiuhua, QIN Huojuan, ZHANG Muqing, WANG Zeping, JIANG Zhuhui. Identification and yield prediction of sugarcane in the south-central part of Guangxi Zhuang Autonomous Region, China based on multi-source satellite-based remote sensing images[J]. Remote Sensing for Natural Resources, 2024, 36(3): 248-258.
[4] CHEN Yan, YUAN Jing, TANG Chunhua, SUN Chao, TANG Xiao, WANG Mingyou. A remote sensing methodology for predicting geothermal resources in the Wugongshan uplift zone[J]. Remote Sensing for Natural Resources, 2024, 36(2): 27-38.
[5] JIANG Ruirui, GAN Fuping, GUO Yi, YAN Bokun. Progress in research on the joint inversion for soil moisture using multi-source satellite remote sensing data[J]. Remote Sensing for Natural Resources, 2024, 36(1): 1-13.
[6] ZHOU Xiaojia. Application of remote sensing monitoring in abandoned arable land in a hilly region[J]. Remote Sensing for Natural Resources, 2024, 36(1): 235-241.
[7] GUO Yi, GAN Fuping, YAN Bokun, BAI Juan, XING Naichen, LIU Qi. Spatio-temporal distribution and influencing factors of soil moisture content in Henan Province during 1948—2021[J]. Remote Sensing for Natural Resources, 2023, 35(3): 241-252.
[8] GUO Xiaomeng, FANG Xiuqin, YANG Lulu, CAO Yu. Artificial neural network-based estimation of root zone soil moisture in the western Liaohe river basin[J]. Remote Sensing for Natural Resources, 2023, 35(2): 193-201.
[9] LI Kailin, LIAO Kuo, DANG Haofei. Recent progress in chromaticity remote sensing of inland and nearshore water bodies[J]. Remote Sensing for Natural Resources, 2023, 35(1): 15-26.
[10] QI Zhao, TAN Bingxiang, CAO Xiaoming, YU Hang, SHEN Mingtan. Spatial-temporal dynamics of ecological carrying capacity of the northeastern margin of the Ulan Buh Desert[J]. Remote Sensing for Natural Resources, 2023, 35(1): 222-230.
[11] LI Zhu, FAN Hongdong, GAO Yantao, XU Yaozong. DS-InSAR-based monitoring and analysis of a long time series of surface deformation in the fire area of the Wuda coal field[J]. Remote Sensing for Natural Resources, 2022, 34(3): 138-145.
[12] ZHANG Shu, ZHOU Zhongfa, WANG Lingyu, CHEN Quan, LUO Jiancheng, ZHAO Xin. Inversion of moisture in surface soil of farmland in karst mountainous areas using multi-temporal SAR images[J]. Remote Sensing for Natural Resources, 2022, 34(3): 154-163.
[13] ZUO Lu, SUN Leigang, LU Junjing, XU Quanhong, LIU Jianfeng, MA Xiaoqian. MODIS-based comprehensive assessment and spatial-temporal change monitoring of ecological quality in Beijing-Tianjin-Hebei region[J]. Remote Sensing for Natural Resources, 2022, 34(2): 203-214.
[14] GAO Qi, WANG Yuzhen, FENG Chunhui, MA Ziqiang, LIU Weiyang, PENG Jie, JI Yanzhen. Remote sensing inversion of desert soil moisture based on improved spectral indices[J]. Remote Sensing for Natural Resources, 2022, 34(1): 142-150.
[15] SONG Qi, FENG Chunhui, MA Ziqiang, WANG Nan, JI Wenjun, PENG Jie. Simulation of land use change in oasis of arid areas based on Landsat images from 1990 to 2019[J]. Remote Sensing for Natural Resources, 2022, 34(1): 198-209.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
京ICP备05055290号-2
Copyright © 2017 Remote Sensing for Natural Resources
Support by Beijing Magtech