Cropland intensity extraction combined using optical and SAR time-series in cloudy and rainy areas of southern China

doi:10.11947/j.AGCS.2024.20230497

Abstract

Abstract:

Timely and accurate acquisition of spatiotemporal distribution information regarding cropping intensity holds significant reference value for adjusting agricultural production layout and making grain production decisions. Current research on cropping intensity extraction primarily relies on optical data and phenological knowledge. However, critical phenological parameters for multi-season cropping cropland are often missing in cloudy and rainy regions of the South China, and confusable vegetation with phenological characteristics similar to crops is difficult to cull, and the salt-and-pepper noise is obvious in the pixel-level results. This paper introduces a novel method for extracting cropping intensity by integrating optical phenological parameters, SAR temporal features, and superpixel optimization based on time-series optical and SAR data. Initially, optical NDVI and LSWI temporal curves are utilized to acquire the number and duration of growth periods. Subsequently, SAR temporal features are employed to identify early-season signals of transplanting and irrigation. Finally, spatial contextual information is utilized for superpixel optimization of the cropping intensity extraction results. The effectiveness of the proposed method is validated using time-series Sentinel-1/2 data from Honghu city in 2020—2021, yielding an overall accuracy of 92.02% and a Kappa coefficient of 0.84. Results indicate that incorporating growth period duration effectively mitigates the influence of mixed vegetation, SAR temporal features accurately classify double-season rice, and superpixel optimization enhances the accuracy and completeness of planting intensity results. This method proves capable of accurately capturing cropping intensity distribution in regions with cloudy and rainy complex cropping pattern.

Key words: time-series optical, time-series SAR, cropping intensity, phenological parameters

CLC Number:

P237

Qihao CHEN, Guangchao LI, Wenjing CAO, Xiuguo LIU. Cropland intensity extraction combined using optical and SAR time-series in cloudy and rainy areas of southern China[J]. Acta Geodaetica et Cartographica Sinica, 2024, 53(12): 2361-2374.

Add to citation manager EndNote|Reference Manager|ProCite|BibTeX|RefWorks

URL: http://xb.chinasmp.com/EN/10.11947/j.AGCS.2024.20230497

http://xb.chinasmp.com/EN/Y2024/V53/I12/2361

Figures/Tables 16

Fig. 1

Fig. 2

Fig. 3

Fig. 4

Fig. 5

Tab. 1

Fig. 6

Tab. 2

Fig. 7

Tab. 3

Fig. 8

Fig. 9

Fig. 10

Fig. 11

Fig. 12

Fig. 13

References 36

[1]	陈秧分, 王介勇, 张凤荣, 等. 全球化与粮食安全新格局[J]. 自然资源学报, 2021, 36(6): 1362-1380.
	CHEN Yangfen, WANG Jieyong, ZHANG Fengrong, et al. New patterns of globalization and food security[J]. Journal of Natural Resources, 2021, 36(6): 1362-1380.
[2]	刘巽浩, 陈阜, 吴尧. 多熟种植：中国农业的中流砥柱[J]. 作物杂志, 2015(6): 1-9.
	LIU Xunhao, CHEN Fu, WU Yao. Multiple cropping: the principal part of China's agriculture[J]. Crops, 2015(6): 1-9.
[3]	LIU Luo, XIAO Xiangming, QIN Yuanwei, et al. Mapping cropping intensity in China using time series Landsat and Sentinel-2 images and Google Earth Engine[J]. Remote Sensing of Environment, 2020, 239: 111624.
[4]	PAN Li, XIA Haoming, YANG Jia, et al. Mapping cropping intensity in Huaihe basin using phenology algorithm, all Sentinel-2 and Landsat images in Google Earth Engine[J]. International Journal of Applied Earth Observation and Geoinformation, 2021, 102: 102376.
[5]	QIU Bingwen, HU Xiang, YANG Peng, et al. A robust approach for large-scale cropping intensity mapping in smallholder farms from vegetation, brownness indices and SAR time series[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2023, 203: 328-344.
[6]	GRAY J, FRIEDL M, FROLKING S, et al. Mapping Asian cropping intensity with MODIS[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2014, 7(8): 3373-3379.
[7]	ESTEL S, KUEMMERLE T, LEVERS C, et al. Mapping cropland-use intensity across Europe using MODIS NDVI time series[J]. Environmental Research Letters, 2016, 11(2): 024015.
[8]	HAO Pengyu, TANG Huajun, CHEN Zhongxin, et al. High resolution crop intensity mapping using harmonized Landsat-8 and Sentinel-2 data[J]. Journal of Integrative Agriculture, 2019, 18(12): 2883-2897.
[9]	JIANG Min, XIN Liangjie, LI Xiubin, et al. Decreasing rice cropping intensity in Southern China from 1990 to 2015[J]. Remote Sensing, 2018, 11(1): 35.
[10]	LI Le, ZHAO Yaolong, FU Yingchun, et al. High resolution mapping of cropping cycles by fusion of Landsat and MODIS data[J]. Remote Sensing, 2017, 9(12): 1232.
[11]	GUO Yan, XIA Haoming, PAN Li, et al. Mapping the northern limit of double cropping using a phenology-based algorithm and Google Earth Engine[J]. Remote Sensing, 2022, 14(4): 1004.
[12]	TAO Jianbin, WANG Yun, QIU Bingwen, et al. Exploring cropping intensity dynamics by integrating crop phenology information using Bayesian networks[J]. Computers and Electronics in Agriculture, 2022, 193: 106667.
[13]	HU Jie, CHEN Yunping, CAI Zhiwen, et al. Mapping diverse paddy rice cropping patterns in South China using harmonized Landsat and Sentinel-2 data[J]. Remote Sensing, 2023, 15(4): 1034.
[14]	LIU Yuan, YU Qiangyi, ZHOU Qingbo, et al. Mapping the complex crop rotation systems in Southern China considering cropping intensity, crop diversity, and their seasonal dynamics[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2022, 15: 9584-9598.
[15]	XIANG Mingtao, YU Qiangyi, WU Wenbin. From multiple cropping index to multiple cropping frequency: observing cropland use intensity at a finer scale[J]. Ecological Indicators, 2019, 101: 892-903.
[16]	ZHANG Miao, WU Bingfang, ZENG Hongwei, et al. GCI30: a global dataset of 30 m cropping intensity using multisource remote sensing imagery[J]. Earth System Science Data, 2021, 13(10): 4799-4817.
[17]	WANG Yanyan, FANG Shenghui, ZHAO Lingli, et al. Parcel-based summer maize mapping and phenology estimation combined using Sentinel-2 and time series Sentinel-1 data[J]. International Journal of Applied Earth Observation and Geoinformation, 2022, 108: 102720.
[18]	SCHLUND M, ERASMI S. Sentinel-1 time series data for monitoring the phenology of winter wheat[J]. Remote Sensing of Environment, 2020, 246: 111814.
[19]	YANG Huijin, PAN Bin, LI Ning, et al. A systematic method for spatio-temporal phenology estimation of paddy rice using time series Sentinel-1 images[J]. Remote Sensing of Environment, 2021, 259: 112394.
[20]	D'ANDRIMONT R, TAYMANS M, LEMOINE G, et al. Detecting flowering phenology in oil seed rape parcels with Sentinel-1 and-2 time series[J]. Remote Sensing of Environment, 2020, 239: 111660.
[21]	MERCIER A, BETBEDER J, BAUDRY J, et al. Evaluation of Sentinel-1 & 2 time series for predicting wheat and rapeseed phenological stages[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2020, 163: 231-256.
[22]	XU Shuai, ZHU Xiaolin, CHEN Jin, et al. A robust index to extract paddy fields in cloudy regions from SAR time series[J]. Remote Sensing of Environment, 2023, 285: 113374.
[23]	ZHANG Xi, SHEN Ruoque, ZHU Xiaolin, et al. Sample-free automated mapping of double-season rice in China using Sentinel-1 SAR imagery[J]. Frontiers in Environmental Science, 2023, 11: 1207882.
[24]	YAN Huimin, XIAO Xiangming, HUANG Heqing, et al. Multiple cropping intensity in China derived from agro-meteorological observations and MODIS data[J]. Chinese Geographical Science, 2014, 24: 205-219.
[25]	GAO Feng, ANDERSON M C, ZHANG Xiaoyang, et al. Toward mapping crop progress at field scales through fusion of Landsat and MODIS imagery[J]. Remote Sensing of Environment, 2017, 188: 9-25.
[26]	YU Qiangyi, SHI Yun, TANG Huajun, et al. eFarm: a tool for better observing agricultural land systems[J]. Sensors, 2017, 17(3): 453.
[27]	刘巍, 吴志峰, 骆剑承, 等. 深度学习支持下的丘陵山区耕地高分辨率遥感信息分区分层提取方法[J]. 测绘学报, 2021, 50(1): 105-116. DOI:. doi: 10.11947/j.AGCS.2021.20190448
	LIU Wei, WU Zhifeng, LUO Jiancheng, et al. A divided and stratified extraction method of high-resolution remote sensing information for cropland in hilly and mountainous areas based on deep learning[J]. Acta Geodaetica et Cartographica Sinica, 2021, 50(1): 105-116. DOI:. doi: 10.11947/j.AGCS.2021.20190448
[28]	YU Juanjuan, HE Xiufeng, XU Jia, et al. An edge-assisted, object-oriented random forest approach for refined extraction of tea plantations using multi-temporal Sentinel-2 and high-resolution Gaofen-2 imagery[J]. Journal of Geodesy and Geoinformation Science, 2023, 6(1): 31-46.
[29]	GONG Peng, LIU Han, ZHANG Meinan, et al. Stable classification with limited sample: transferring a 30 m resolution sample set collected in 2015 to mapping 10 m resolution global land cover in 2017[J]. Science Bulletin, 2019, 64(6): 370-373.
[30]	FRANTZ D, HAßE , UHL A, et al. Improvement of the Fmask algorithm for Sentinel-2 images: separating clouds from bright surfaces based on parallax effects[J]. Remote Sensing of Environment, 2018, 215: 471-481.
[31]	SOBRINO J A, RAISSOUNI N, LI Zhaoliang. A comparative study of land surface emissivity retrieval from NOAA data[J]. Remote Sensing of Environment, 2001, 75(2): 256-266.
[32]	DONG Jinwei, XIAO Xiangming, KOU Weili, et al. Tracking the dynamics of paddy rice planting area in 1986—2010 through time series Landsat images and phenology-based algorithms[J]. Remote Sensing of Environment, 2015, 160: 99-113.
[33]	CHEN Bangqian, XIAO Xiangming, YE Huichun, et al. Mapping forest and their spatial-temporal changes from 2007 to 2015 in tropical Hainan Island by integrating ALOS/ALOS-2 L-band SAR and Landsat optical images[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2018, 11(3): 852-867.
[34]	MERONI M, D'ANDRIMONT R, VRIELING A, et al. Comparing land surface phenology of major European crops as derived from SAR and multispectral data of Sentinel-1 and-2[J]. Remote Sensing of Environment, 2021, 253: 112232.
[35]	LI He, FU Dongjie, HUANG Chong, et al. An approach to high-resolution rice paddy mapping using time-series sentinel-1 SAR data in the Mun River basin, Thailand[J]. Remote Sensing, 2020, 12(23): 3959.
[36]	ACHANTA R, SUSSTRUNK S. Superpixels and polygons using simple non-iterative clustering[C]//Proceedings of 2017 IEEE Conference on Computer Vision and Pattern Recognition. Honolulu: IEEE, 2017: 4651-4660.

方案名	试验设计
方案1	生长期数量提取
方案2	生长期数量提取+生长期长度筛选
方案3	生长期数量提取+生长期长度筛选+SAR时序特征RI判别
本文方法	生长期数量提取+生长期长度筛选+SAR时序特征RI判别+SNIC超像素优化

方案	单季_PA/（%）	单季_UA/（%）	双季_PA/（%）	双季_UA/（%）	三季_PA/（%）	三季_UA/（%）	OA/（%）	Kappa
方案1	96.40	63.77	67.82	97.72	73.93	99.79	77.76	0.60
方案2	98.77	63.89	69.81	97.72	75.33	99.79	79.88	0.63
方案3	93.74	83.92	90.41	95.43	77.44	99.63	91.14	0.82
本文方法	94.07	85.66	91.59	95.70	78.30	99.90	92.02	0.84

方案	单季_PA/（%）	单季_UA/（%）	双季_PA/（%）	双季_UA/（%）	OA/（%）	Kappa
方案1	96.66	50.11	72.73	99.52	77.58	0.51
方案2	96.66	50.15	74.91	99.54	79.31	0.53
方案3	92.06	79.91	93.61	98.37	93.30	0.81
本文方法	94.50	83.90	95.10	98.97	94.98	0.85