Mapping rice cropping systems using Landsat-derived Renormalized Index of Normalized Difference Vegetation Index (RNDVI) in the Poyang Lake Region, China

doi:10.1007/s11707-016-0545-8

Front. Earth Sci.

2016, Vol. 10

Issue (2) : 303-314 https://doi.org/10.1007/s11707-016-0545-8

RESEARCH ARTICLE

Mapping rice cropping systems using Landsat-derived Renormalized Index of Normalized Difference Vegetation Index (RNDVI) in the Poyang Lake Region, China

Peng LI^1,², Luguang JIANG¹, Zhiming FENG¹(

), Sage SHELDON³, Xiangming XIAO³

¹. Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
². Key Laboratory of Poyang Lake Wetland and Watershed Research, Ministry of Education, Jiangxi Normal University, Nanchang 330022, China
³. Department of Botany and Microbiology, Center for Spatial Analysis, University of Oklahoma, Norman, OK 73019, USA

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Abstract

Mapping rice cropping systems with optical imagery in multiple cropping regions is challenging due to cloud contamination and data availability; development of a phenology-based algorithm with a reduced data demand is essential. In this study, the Landsat-derived Renormalized Index of Normalized Difference Vegetation Index (RNDVI) was proposed based on two temporal windows in which the NDVI values of single and early (or late) rice display inverse changes, and then applied to discriminate rice cropping systems. The Poyang Lake Region (PLR), characterized by a typical cropping system of single cropping rice (SCR, or single rice) and double cropping rice (DCR, including early rice and late rice), was selected as a testing area. The results showed that NDVI data derived from Landsat time-series at eight to sixteen days captures the temporal development of paddy rice. There are two key phenological stages during the overlapping growth period in which the NDVI values of SCR and DCR change inversely, namely the ripening phase of early rice and the growing phase of single rice as well as the ripening stage of single rice and the growing stage of late rice. NDVI derived from scenes in two temporal windows, specifically early August and early October, was used to construct the RNDVI for discriminating rice cropping systems in the polder area of the PLR, China. Comparison with ground truth data indicates high classification accuracy. The RNDVI approach highlights the inverse variations of NDVI values due to the difference of rice growth between two temporal windows. This makes the discrimination of rice cropping systems straightforward as it only needs to distinguish whether the candidate rice type is in the period of growth (RNDVI<0) or senescence (RNDVI>0).

Keywords Normalized Difference Vegetation Index (NDVI) Renormalized Index of NDVI (RNDVI) rice cropping systems phenology temporal windows Poyang Lake Region (PLR)

Corresponding Author(s): Zhiming FENG

Just Accepted Date: 08 January 2016 Online First Date: 19 February 2016 Issue Date: 05 April 2016

Cite this article:

Peng LI,Luguang JIANG,Zhiming FENG, et al. Mapping rice cropping systems using Landsat-derived Renormalized Index of Normalized Difference Vegetation Index (RNDVI) in the Poyang Lake Region, China[J]. Front. Earth Sci., 2016, 10(2): 303-314.

URL:

https://academic.hep.com.cn/fesci/EN/10.1007/s11707-016-0545-8
https://academic.hep.com.cn/fesci/EN/Y2016/V10/I2/303

1	S M Adler-Golden, M W Matthew, L S Bernstein, R Y Levine, A Berk, S C Richtsmeier, P K Acharya, G P Anderson, G Felde, J Gardner, M L Hoke, L S Jeong, B Pukall, A J Ratkowski, H K Burke (1999). Atmospheric correction for short-wave spectral imagery based on MODTRAN 4. Proc. SPIE 3753. Proc. SPIE 3753, Imaging Spectrometry V, 61: 61–69 https://doi.org/10.1117/12.366315
2	W G M Bastiaanssen, D J Molden, I W Makin (2000). Remote sensing for irrigated agriculture: examples from research and possible applications. Agric Water Manage, 46(2): 137–155 https://doi.org/10.1016/S0378-3774(00)00080-9
3	B Bouman, T P Tuong (2001). Field water management to save water and increase its productivity in irrigated lowland rice. Agric Water Manage, 49(1): 11–30 https://doi.org/10.1016/S0378-3774(00)00128-1
4	A Bouvet, T Le Toan, N Lam-Dao (2009). Monitoring of the rice cropping system in the Mekong delta using ENVISAT/ASAR dual polarization data. IEEE Transactions on Geoscience and Remote Sensing, 47(2): 517–526 https://doi.org/10.1109/TGRS.2008.2007963
5	J Chen (2007). Rapid urbanization in China: A real challenge to soil protection and food security. Catena, 69(1): 1–15 https://doi.org/10.1016/j.catena.2006.04.019
6	J Chen, J Huang, J Hu (2011). Mapping rice planting areas in southern China using the China Environment Satellite data. Math Comput Model, 54(3‒4): 1037–1043 https://doi.org/10.1016/j.mcm.2010.11.033
7	A Gusso, J R Ducati (2012). Algorithm for soybean classification using medium resolution satellite images. Remote Sens, 4(10): 3127–3142 https://doi.org/10.3390/rs4103127
8	M C Hansen, T R Loveland (2012). A review of large area monitoring of land cover change using Landsat data. Remote Sens Environ, 122(Landsat Legacy Special Issue): 66–74
9	Jiangxi Province Department of Water Resources (1999). Levee Atlas of Jiangxi Province. Nanchang: Jiangxi Province Department of Water Resources
10	T Le Toan, F Ribbes, L F Wang, N Floury, K H Ding, J A Kong, M Fujita, T Kurosu (1997). Rice crop mapping and monitoring using ERS-1 data based on experiment and modeling results. IEEE Transactions on Geoscience and Remote Sensing, 35(1): 41–56 https://doi.org/10.1109/36.551933
11	C S Li, S Frolking, X M Xiao, B Moore, S Boles, J J Qiu, Y Huang, W Salas, R Sass (2005). Modeling impacts of farming management alternatives on CO2, CH4, and N2O emissions: a case study for water management of rice agriculture of China. Global Biogeochem Cy, 19(GB30103): B3010, 10–1029
12	P Li (2012). Trade-off between Grain Production and Flood Regulation Functions in the Poyang Lake Region, China. Dissertation for Ph.D degree. Beijing: Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, 166
13	P Li, Z Feng, L Jiang, Y Liu, X Xiao (2012). Changes in rice cropping systems in the Poyang Lake Region, China during 2004‒2010. J Geogr Sci, 22(4): 653–668 https://doi.org/10.1007/s11442-012-0954-x
14	S C Liew, S P Kam, T P Tuong, P Chen, V Q Minh, H Lim (1998). Application of multitemporal ERS-2 synthetic aperture radar in delineating rice cropping systems in the Mekong River Delta, Vietnam. IEEE Transactions on Geoscience and Remote Sensing, 36(5): 1412–1420 https://doi.org/10.1109/36.718845
15	J Y Liu, M L Liu, H Q Tian, D F Zhuang, Z X Zhang, W Zhang, X M Tang, X Z Deng (2005). Spatial and temporal patterns of China's cropland during 1990‒2000: an analysis based on Landsat TM data. Remote Sens Environ, 98(4): 442–456 https://doi.org/10.1016/j.rse.2005.08.012
16	J A Martínez-Casasnovas, A Martín-Montero, M A Casterad (2005). Mapping multi-year cropping patterns in small irrigation districts from time-series analysis of Landsat TM images. Eur J Agron, 23(2): 159–169 https://doi.org/10.1016/j.eja.2004.11.004
17	R B Myneni, C D Keeling, C J Tucker, G Asrar, R R Nemani (1997). Increased plant growth in the northern high latitudes from 1981 to 1991. Nature, 386(6626): 698–702 https://doi.org/10.1038/386698a0
18	NASA Goddard Space Flight Center (2011). Landsat 7 Science Data Users Handbook.
19	National Bureau of Statistics of China (2010). China Statistical Yearbook.Beijing: China Statistics Press
20	S Panigrahy, S S Ray, K R Manjunath, P S Pandey, S K Sharma, A Sood, M Yadav, P C Gupta, N Kundu, J S Parihar (2011). A spatial database of cropping system and its characteristics to aid climate change impact assessment studies. Journal of the Indian Society of Remote Sensing, 39(3): 355–364 https://doi.org/10.1007/s12524-011-0093-3
21	D Peng, A R Huete, J Huang, F Wang, H Sun (2011). Detection and estimation of mixed paddy rice cropping patterns with MODIS data. Int J Appl Earth Obs Geoinf, 13(1): 13–23 https://doi.org/10.1016/j.jag.2010.06.001
22	T Sakamoto, P Van Cao, N Van Nguyen, A Kotera, M Yokozawa (2009 a). Agro-ecological interpretation of rice cropping systems in flood-prone areas using MODIS imagery. Photogramm Eng Remote Sensing, 75(4): 413–424 https://doi.org/10.14358/PERS.75.4.413
23	T Sakamoto, N Van Nguyen, H Ohno, N Ishitsuka, M Yokozawa (2006). Spatio-temporal distribution of rice phenology and cropping systems in the Mekong Delta with special reference to the seasonal water flow of the Mekong and Bassac rivers. Remote Sens Environ, 100(1): 1–16 https://doi.org/10.1016/j.rse.2005.09.007
24	T Sakamoto, P C Van, A Kotera, K N Duy, M Yokozawa (2009 b). Detection of yearly change in farming systems in the Vietnamese Mekong Delta from MODIS time-series imagery. Jarq-Jpn Agr Res Q, 43(3): 173–185 https://doi.org/10.6090/jarq.43.173
25	T Sakamoto, M Yokozawa, H Toritani, M Shibayama, N Ishitsuka, H Ohno (2005). A crop phenology detection method using time-series MODIS data. Remote Sens Environ, 96(3‒4): 366–374 https://doi.org/10.1016/j.rse.2005.03.008
26	D Shankman, Q L Liang (2003). Landscape changes and increasing flood frequency in China’s Poyang Lake Region. Prof Geogr, 55(4): 434–445 https://doi.org/10.1111/0033-0124.5504003
27	P S Thenkabail (2003). Biophysical and yield information for precision farming from near-real-time and historical Landsat TM images. Int J Remote Sens, 24(14): 2879–2904 https://doi.org/10.1080/01431160710155974
28	P S Thenkabail, M Schull, H Turral (2005). Ganges and Indus river basin land use/land cover (LULC) and irrigated area mapping using continuous streams of MODIS data. Remote Sens Environ, 95(3): 317–341 https://doi.org/10.1016/j.rse.2004.12.018
29	C J Tucker (1979). Red and photographic infrared linear combinations for monitoring vegetation. Remote Sens Environ, 8(2): 127–150 https://doi.org/10.1016/0034-4257(79)90013-0
30	T G Van Niel, T R McVicar (2004). Determining temporal windows for crop discrimination with remote sensing: a case study in south-eastern Australia. Comput Electron Agric, 45(1‒3): 91–108 https://doi.org/10.1016/j.compag.2004.06.003
31	B D Wardlow, S L Egbert, J H Kastens (2007). Analysis of time-series MODIS 250 m vegetation index data for crop classification in the US Central Great Plains. Remote Sens Environ, 108(3): 290–310 https://doi.org/10.1016/j.rse.2006.11.021
32	X Xiao, S Boles, S Frolking, C Li, J Y Babu, W Salas, B Moore III (2006). Mapping paddy rice agriculture in South and Southeast Asia using multi-temporal MODIS images. Remote Sens Environ, 100(1): 95–113 https://doi.org/10.1016/j.rse.2005.10.004
33	X Xiao, S Boles, J Liu, D Zhuang, S Frolking, C Li, W Salas, B Moore III (2005). Mapping paddy rice agriculture in southern China using multi-temporal MODIS images. Remote Sens Environ, 95(4): 480–492 https://doi.org/10.1016/j.rse.2004.12.009
34	W Xiong, D Conway, E D Lin, I Holman (2009). Potential impacts of climate change and climate variability on China’s rice yield and production. Clim Res, 40(1): 23–35 https://doi.org/10.3354/cr00802
35	M W Zhang, Q B Zhou, Z X Chen, J Liu, Y Zhou, C F Cai (2008). Crop discrimination in Northern China with double cropping systems using Fourier analysis of time-series MODIS data. International Journal of Applied Earth Observation and Geoinformation, 10(4): 476–485 https://doi.org/10.1016/j.jag.2007.11.002

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