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Remote Sensing for Natural Resources    2025, Vol. 37 Issue (2) : 39-48     DOI: 10.6046/zrzyyg.2023350
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Collaborative monitoring of abandoned arable land in cloudy and rainy areas based on multisource remote sensing data
XIAO Wenju1(), YANG Yingpin1, WU Zhifeng1,2,3()
1. School of Geography and Remote Sensing, Guangzhou University, Guangzhou 510006, China
2. Southern Marine Science and Engineering Guangdong Laboratory (Guangzhou), Guangzhou 511458, China
3. MNR Key Laboratory for Geo-Environmental Monitoring of Great Bay Area, Shenzhen 518060, China
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Abstract  

In cloudy and rainy areas, the humid and hot climate and cloud contamination during the rainy season often cause the loss of optical data. Hence, optical data alone fail to enable the accurate monitoring of abandoned land. This study proposed a method for monitoring abandoned land in cloudy and rainy areas based on multisource remote sensing data. By integrating optical and synthetic aperture Radar (SAR) remote sensing data, this study extracted the multitemporal optical and SAR-derived features of vegetation and assessed their importance using the GINI index. Employing the random forest classifier, this study mapped the spatial distribution of abandoned land in Jiexi County in 2021. The results show that the proposed method achieved a relatively high accuracy in identifying abandoned land in cloudy and rainy areas, yielding an overall accuracy of 87.0%. This value represents an improvement of 6.7 and 13.8 percentage points, respectively, compared to the results derived solely from optical and SAR remote sensing features. The analysis reveals that the normalized difference vegetation index (NDVI), soil-adjusted vegetation index (SAVI), polarization entropy, normalized difference water index (NDWI), and anti-entropy are crucial for identifying abandoned land. Additionally, key months for distinguishing abandoned from non-abandoned land include February, April, June, August, and December. This study establishes a monitoring model for abandoned land based on multisource features and multitemporal phases, providing technical support for monitoring abandoned land in cloudy and rainy areas.
Keywords abandoned land      multisource remote sensing      cloudy and rainy areas      arable land      temporal features     
ZTFLH:  TP79  
Issue Date: 09 May 2025
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Wenju XIAO
Yingpin YANG
Zhifeng WU
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Wenju XIAO,Yingpin YANG,Zhifeng WU. Collaborative monitoring of abandoned arable land in cloudy and rainy areas based on multisource remote sensing data[J]. Remote Sensing for Natural Resources, 2025, 37(2): 39-48.
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https://www.gtzyyg.com/EN/10.6046/zrzyyg.2023350     OR     https://www.gtzyyg.com/EN/Y2025/V37/I2/39
Fig.1  Geographical location of the study area and spatial distribution of the samples
数据类型 时间 数据类型 时间
Sentinel-1 2021-01-09 Sentinel-2 2021-01-01
Sentinel-1 2021-02-26 Sentinel-2 2021-02-10
Sentinel-1 2021-03-22 Sentinel-2 2021-03-12
Sentinel-1 2021-04-15 Sentinel-2 2021-04-11
Sentinel-1 2021-05-09 Sentinel-2 2021-05-11
Sentinel-1 2021-06-03 Sentinel-2 2021-06-10
Sentinel-1 2021-07-20 Sentinel-2 2021-07-10
Sentinel-1 2021-08-25 Sentinel-2 2021-08-19
Sentinel-1 2021-09-18 Sentinel-2 2021-09-18
Sentinel-1 2021-10-12 Sentinel-2 2021-10-18
Sentinel-1 2021-11-17 Sentinel-2 2021-11-17
Sentinel-1 2021-12-23 Sentinel-2 2021-12-07
Tab.1  Time series dataset of Sentinel-1 and Sentinel-2
Fig.2  Examples of abandoned landand non-abandoned land
Fig.3  Extraction results of cultivated land
Fig.4  Technical flowchart
Fig.5  Multi-source feature time series curve
Fig.6  Result of multi-source feature filtering
Fig.7  Model error plot
特征 类别 撂荒地 水稻 其他作物 总计 制图精度/% 用户精度/% 总体精度/%
SAR特征 撂荒地 141 18 26 185 76.5 73.1 73.2
水稻 15 144 28 187 77.1 77.8
其他作物 37 23 116 176 65.7 68.2
总计 193 185 170 548 — —
光学特征 撂荒地 160 3 18 181 88.7 73.9 80.3
水稻 7 171 10 183 93.2 85.2
其他作物 23 15 146 184 79.5 81.2
总计 190 189 174 548 — —
SAR特征+
光学特征		 撂荒地 160 3 18 181 88.7 84.2 87.0
水稻 7 171 10 183 93.2 90.5
其他作物 23 15 146 184 79.5 83.9
总计 190 189 174 548 — —
Tab.2  Confusion matrix with different feature combinations
类别 撂荒地 水稻 其他作物 总计 制图精度/%
撂荒地 137 26 18 181 75.69
水稻 21 155 22 198 78.27
其他作物 19 27 123 169 72.78
总计 177 208 163 548 —
用户精度/% 77.40 74.52 75.46 — —
总体精度/% 75.73
Tab.3  Precision verification confusion matrix table based on the combination of optical and SAR features
Fig.8  Extraction results of abandoned land, rice, and other crops
[1] 李升发, 李秀彬. 耕地撂荒研究进展与展望[J]. 地理学报, 2016, 71(3):370-389.
doi: 10.11821/dlxb201603002
[1] Li S F, Li X B. Progress and prospect on farmland abandonment[J]. Acta Geographica Sinica, 2016, 71(3):370-389.
doi: 10.11821/dlxb201603002
[2] 陈欣怡, 郑国全. 国内外耕地撂荒研究进展[J]. 中国人口·资源与环境, 2018, 28(s2):37-41.
[2] Chen X Y, Zheng G Q. Research progress of cultivated land abandonment at home and abroad[J]. China Population,Resources and Environment, 2018, 28(s2):37-41.
[3] 杨通, 郭旭东, 于潇, 等. 撂荒地监测方法与生态影响述评[J]. 生态环境学报, 2020, 29(8):1683-1692.
doi: 10.16258/j.cnki.1674-5906.2020.08.021
[3] Yang T, Guo X D, Yu X, et al. Review on monitoring methods and ecological impact of abandoned agricultural land[J]. Ecology and Environmental Sciences, 2020, 29(8):1683-1692.
[4] 周小迦. 丘陵地带耕地撂荒遥感监测应用研究[J]. 自然资源遥感, 2024, 36(1):235-241.doi:10.6046/zrzyyg.2022435.
[4] Zhou X J. Application of remote sensing monitoring in abandoned arable land in a hilly region[J]. Remote Sensing for Natural Resources, 2024, 36(1):235-241.doi:10.6046/zrzyyg.2022435.
[5] 罗雅红, 龚建周, 李天翔, 等. 基于MaxEnt模型提取撂荒耕地——以四川省武胜县为例[J]. 农业资源与环境学报, 2021, 38(6):1084-1093.
[5] Luo Y H, Gong J Z, Li T X, et al. Extraction of abandoned farmland based on MaxEnt model:A case study of Wusheng County,Sichuan Province[J]. Journal of Agricultural Resources and Environment, 2021, 38(6):1084-1093.
[6] 杨颖频, 吴志峰, 骆剑承, 等. 时空协同的地块尺度作物分布遥感提取[J]. 农业工程学报, 2021, 37(7):166-174.
[6] Yang Y P, Wu Z F, Luo J C, et al. Parcel-based crop distribution extraction using the spatiotemporal collaboration of remote sensing data[J]. Transactions of the Chinese Society of Agricultural Engineering, 2021, 37(7):166-174.
[7] 钱丽沙, 姜浩, 陈水森, 等. 基于时空滤波Sentinel-1时序数据的田块尺度岭南作物分布提取[J]. 农业工程学报, 2022, 38(5):158-166.
[7] Qian L S, Jiang H, Chen S S, et al. Extracting field-scale crop distribution in Lingnan using spatiotemporal filtering of Sentinel-1 time-series data[J]. Transactions of the Chinese Society of Agricultural Engineering, 2022, 38(5):158-166.
[8] 马丽, 徐新刚, 刘良云, 等. 基于多时相NDVI及特征波段的作物分类研究[J]. 遥感技术与应用, 2008, 23(5):520-524.
[8] Ma L, Xu X G, Liu L Y, et al. Study on crops classification based on multi-temporal NDVI and characteristic bands[J]. Remote Sensing Technology and Application, 2008, 23(5):520-524.
[9] 刘杰, 刘吉凯, 安晶晶, 等. 基于时序Landsat8 OLI多特征与随机森林算法的作物精细分类研究[J]. 干旱地区农业研究, 2020, 38(3):281-288,298.
[9] Liu J, Liu J K, An J J, et al. Precise crop classification based on multi-features from time-series Landsat8 OLI images and random forest algorithm[J]. Agricultural Research in the Arid Areas, 2020, 38(3):281-288,298.
[10] 林忠辉, 莫兴国. NDVI时间序列谐波分析与地表物候信息获取[J]. 农业工程学报, 2006, 22(12):138-144.
[10] Lin Z H, Mo X G. Phenologies from harmonics analysis of AVHRR NDVI time series[J]. Transactions of the Chinese Society of Agricultural Engineering, 2006, 22(12):138-144.
[11] 杜保佳, 张晶, 王宗明, 等. 应用Sentinel-2A NDVI时间序列和面向对象决策树方法的农作物分类[J]. 地球信息科学学报, 2019, 21(5):740-751.
doi: 10.12082/dqxxkx.2019.180412
[11] Du B J, Zhang J, Wang Z M, et al. Crop mapping based on Sentinel-2A NDVI time series using object-oriented classification and decision tree model[J]. Journal of Geo-Information Science, 2019, 21(5):740-751.
[12] 王玲玉, 周忠发, 赵馨, 等. 基于地块级时序遥感的喀斯特石漠化地区撂荒地时空演变[J]. 水土保持学报, 2020, 34(1):92-99,107.
[12] Wang L Y, Zhou Z F, Zhao X, et al. Spatiotemporal evolution of Karst rocky desertification abandoned cropland based on farmland-parcels time-series remote sensing[J]. Journal of Soil and Water Conservation, 2020, 34(1):92-99,107.
[13] 宋宪强, 梁钊雄, 周红艺, 等. 基于决策树与时序NDVI变化检测的耕地撂荒遥感监测——以四川省凉山州普格县为例[J]. 山地学报, 2021, 39(6):912-921.
[13] Song X Q, Liang Z X, Zhou H Y, et al. An updated method to monitor the changes in spatial distribution of abandoned land based on decision tree and time series NDVI change detection:A case study of Puge County,Liangshan Prefecture,Sichuan Province,China[J]. Mountain Research, 2021, 39(6):912-921.
[14] 杨通, 郭旭东, 于潇, 等. 基于多源数据的村域撂荒驱动力分析及模型模拟[J]. 干旱区资源与环境, 2019, 33(11):62-69.
[14] Yang T, Guo X D, Yu X, et al. Driving force and model simulation of farmland abandonment in village scale based on multisource data[J]. Journal of Arid Land Resources and Environment, 2019, 33(11):62-69.
[15] Meijninger W, Elbersen B, van Eupen M, et al. Identification of early abandonment in cropland through radar-based coherence data and application of a Random-Forest model[J]. GCB Bioenergy, 2022, 14(7):735-755.
[16] 王晨丞, 王永前, 王利花. 基于SAR纹理信息的农作物识别研究——以农安县为例[J]. 遥感技术与应用, 2021, 36(2):372-380.
doi: 10.11873/j.issn.1004-0323.2021.2.0372
[16] Wang C C, Wang Y Q, Wang L H. Crop identification based on SAR texture information:A case study of Nong’an County[J]. Remote Sensing Technology and Application, 2021, 36(2):372-380.
[17] Luo C, Qi B, Liu H, et al. Using time series sentinel-1 images for object-oriented crop classification in Google Earth Engine[J]. Remote Sensing, 2021, 13(4):561.
[18] Xu L, Zhang H, Wang C, et al. Crop classification based on temporal information using sentinel-1 SAR time-series data[J]. Remote Sensing, 2018, 11(1):53.
[19] Yang Y, Wu Z, Xiao W, et al. Abandoned land mapping based on spatiotemporal features from PolSAR data via deep learning methods[J]. Remote Sensing, 2023, 15(16):3942.
[20] 张昊, 高小红, 史飞飞, 等. 基于Sentinel-2 MSI与Sentinel-1 SAR相结合的黄土高原西部撂荒地提取——以青海民和县为例[J]. 自然资源遥感, 2022, 34(4):144-154.
[20] Zhang H, Gao X H, Shi F F, et al. Sentinel-2 MSI and Sentinel-1 SAR based information extraction of abandoned land in the western Loess Plateau:A case study of Minhe County in Qinghai[J]. Remote Sensing for Natural Resources, 2022, 34(4):144-154.
[21] Bucha T, Papčo J, Sačkov I, et al. Woody above-ground biomass estimation on abandoned agriculture land using Sentinel-1 and Sentinel-2 data[J]. Remote Sensing, 2021, 13(13):2488.
[22] 肖国峰, 朱秀芳, 侯陈瑶, 等. 撂荒耕地的提取与分析——以山东省庆云县和无棣县为例[J]. 地理学报, 2018, 73(9):1658-1673.
doi: 10.11821/dlxb201809004
[22] Xiao G F, Zhu X F, Hou C Y, et al. Extraction and analysis of abandoned farmland:A case study of Qingyun and Wudi Counties in Shandong Province[J]. Acta Geographica Sinica, 2018, 73(9):1658-1673.
[23] Song W. Mapping cropland abandonment in mountainous areas using an annual land-use trajectory approach[J]. Sustainability, 2019, 11(21):5951.
[24] 刘瑞清, 李加林, 孙超, 等. 基于Sentinel-2遥感时间序列植被物候特征的盐城滨海湿地植被分类[J]. 地理学报, 2021, 76(7):1680-1692.
doi: 10.11821/dlxb202107008
[24] Liu R Q, Li J L, Sun C, et al. Classification of Yancheng coastal wetland vegetation based on vegetation phenological characteristics derived from Sentinel-2 time-series[J]. Acta Geographica Sinica, 2021, 76(7):1680-1692.
doi: 10.11821/dlxb202107008
[25] Portalés-Julià E, Campos-Taberner M, García-Haro F J, et al. Assessing the Sentinel-2 capabilities to identify abandoned crops using deep learning[J]. Agronomy, 2021, 11(4):654.
[26] Luo C, Liu H J, Lu L P, et al. Monthly composites from Sentinel-1 and Sentinel-2 images for regional major crop mapping with Google Earth Engine[J]. Journal of Integrative Agriculture, 2021, 20(7):1944-1957.
[27] 石娴, 明艳芳, 刘春秀, 等. 时序影像在冬小麦种植区提取中的应用分析[J]. 无线电工程, 2021, 51(12):1567-1576.
[27] Shi X, Ming Y F, Liu C X, et al. Analysis on the application of time series image in extraction of winter wheat planting area[J]. Radio Engineering, 2021, 51(12):1567-1576.
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