干旱是草地主要的自然灾害之一，建立有效的草地干旱监测模型，具有重要的现实意义。传统的干旱监测基于单一的气象或遥感数据，无法全面地刻画复杂的干旱事件。而已有的综合干旱监测模型多依赖于标准化降水蒸散指数(SPEI)等传统气象指标，传统气象指标的计算较为复杂，且对农业干旱的表征能力有限，因此应考虑应用更可靠稳定的数据源构建草地干旱监测模型。目前已形成一些耦合气候指标、水文指数和遥感信息来描述干旱过程的监测模型，如美国干旱监测系统USDM。以USDM干旱类别为先验干旱知识，采用随机森林方法，构建耦合多源遥感和气象数据的草地综合干旱监测模型，并考虑气候和地理条件的适用性在内蒙古草地区进行适用性验证。模型相较USDM具有1 km尺度更高的空间分辨率，在区域尺度上具有更优的监测能力；在内蒙古地区的验证结果表明，相较于单变量干旱指标，模型与土壤墒情具有更高的相关性，且能够以较高的时间分辨率监测出草地干旱的时空演变情况。
Drought is one of the major natural disasters in the grassland. Therefore developing an effective drought monitoring method for grassland has important practical significance. Traditional drought monitoring model is based on single meteorological or remote sensing data, unable to fully describe complex drought event. The construction of the existing comprehensive drought monitoring models mostly relies on the standardized precipitation evapotranspiration index and other traditional meteorological indicators. However, traditional meteorological index calculation is more complicated, and has certain limitation in terms of agricultural drought monitoring. The United States Drought Monitor (USDM) combines various drought-causing factors with the help of expert's knowledge, which is a more reliable drought indicator. Therefore, in this study, USDM drought categories were used as the prior drought knowledge, and the random forest method was adopted to build a comprehensive grassland drought monitoring model based on multi-source remote sensing and meteorological data. Meanwhile, the applicability of climate and geographical conditions was considered for application verification in Inner Mongolia. Compared with USDM, the model has higher spatial resolution at 1 km scale and better monitoring capability at regional scale. The results of application in Inner Mongolia show that the model has a higher correlation with soil moisture compared with single drought index, and can monitor the spatial and temporal evolution of drought in grassy areas with a higher temporal resolution.
[1] 黄艳艳, 王会军. 2020年全球变暖会创新高吗?[J]. 大气科学学报, 2020, 43(4):585-591. DOI:10.13878/j.cnki.dqkxxb.20200526007.
[2] Mishra A K, Singh V P. A review of drought concepts[J]. Journal of Hydrology, 2010, 391(1-2):202-216. DOI:10.1016/j.jhydrol.2010.07.012.
[3] 赵同谦, 欧阳志云, 贾良清, 等. 中国草地生态系统服务功能间接价值评价[J]. 生态学报, 2004,24(6):1101-1110. DOI:10.3321/j.issn:1000-0933.2004.06.002.
[4] Liu X, Zhu X, Pan Y, et al. Agricultural drought monitoring:Progress, challenges, and prospects[J]. Journal of Geographical Sciences, 2016, 26(6):750-767. DOI:10.1007/s11442-016-1297-9.
[5] Brown J F, Wardlow B D, Tadesse T, et al. The vegetation drought response index (VegDRI):a new integrated approach for monitoring drought stress in vegetation[J]. GIScience & Remote Sensing, 2008, 45(1):16-46. DOI:10.2747/1548-1603.45.1.16.
[6] Wu J J, Zhou L, Liu M, et al. Establishing and assessing the Integrated Surface Drought Index (ISDI) for agricultural drought monitoring in mid-eastern China[J]. International Journal of Applied Earth Observation and Geoinformation, 2013, 23:397-410. DOI:10.1016/j.jag.2012.11.003.
[7] Feng P Y, Wang B, Liu D L, et al. Machine learning-based integration of remotely-sensed drought factors can improve the estimation of agricultural drought in South-Eastern Australia[J]. Agricultural Systems, 2019, 173:303-316. DOI:10.1016/j.agsy.2019.03.015.
[8] 张建, 谢田晋, 杨万能, 等. 近地遥感技术在大田作物株高测量中的研究现状与展望[J]. 智慧农业(中英文), 2021, 3(1):1-15. DOI:10.12133/j.smartag.2021.3.1.202102-SA033.
[9] Svoboda M, Lecomte D, Hayes M, et al. The drought monitor[J]. Bulletin of the American Meteorological Society, 2002, 83(8):1181-1190. DOI:10.1175/1520-0477-83.8.1181.
[10] 管晓丹, 郭铌, 黄建平, 等. 植被状态指数监测西北干旱的适用性分析[J]. 高原气象, 2008, 27(5):1046-1053.
[11] 杨波, 马苏, 王彬武, 等. 基于MODIS的湖南省农业干旱监测模型[J]. 自然资源学报, 2012, 27(10):1788-1796.DOI:10.11849/zrzyxb.2012.10.016.
[12] Rui H, Beaudoing H. README Document for NASA GLDAS Version 2 Data Products[EB/OL]. (2019-08-06)[2021-08-25]. https://data.mint.isi.edu/files/raw-data/GLDAS_NOAH025_M.2.0/doc/README_GLDAS2.pdf.
[13] 叶建刚, 申双和, 吕厚荃. 修正帕默尔干旱指数在农业干旱监测中的应用[J]. 中国农业气象, 2009, 30(2):257-261. DOI:10.3969/j.issn.1000-6362.2009.02.028.
[14] 刘高鸣, 谢传节, 何天乐, 等. 基于多源数据的农业干旱监测模型构建[J]. 地球信息科学学报, 2019, 21(11):1811-1822. DOI:10.12082/dqxxkx.2019.180666.
[15] 杜灵通, 田庆久, 王磊, 等. 基于多源遥感数据的综合干旱监测模型构建[J]. 农业工程学报, 2014, 30(9):126-132. DOI:10.3969/j.issn.1002-6819.2014.09.016.
[16] Zambrano F, Lillo-Saavedra M, Verbist K, et al. Sixteen years of agricultural drought assessment of the BioBío region in Chile using a 250 m resolution vegetation condition index (VCI)[J]. Remote Sensing, 2016, 8(6):530. DOI:10.3390/rs8060530.
[17] Anyamba A, Tucker C J, Eastman J R. NDVI anomaly patterns over Africa during the 1997/98 ENSO warm event[J]. International Journal of Remote Sensing, 2001, 22(10):1847-1859. DOI:10.1080/01431160010029156.
[18] Ji L, Peters A J. Assessing vegetation response to drought in the northern Great Plains using vegetation and drought indices[J]. Remote Sensing of Environment, 2003, 87(1):85-98. DOI:10.1016/S0034-4257(03)00174-3.
[19] Nicholson S E, Farrar T J. The influence of soil type on the relationships between NDVI, rainfall, and soil moisture in semiarid Botswana. I. NDVI response to rainfall[J]. Remote Sensing of Environment, 1994, 50(2):107-120. DOI:10.1016/0034-4257(94)90038-8.
[20] 董师师, 黄哲学. 随机森林理论浅析[J]. 集成技术, 2013, 2(1):1-7.
[21] 王玉娜, 李粉玲, 王伟东, 等. 基于无人机高光谱的冬小麦氮素营养监测[J]. 农业工程学报, 2020, 36(22):31-39.DOI:10.11975/j.issn.1002-6819.2020.22.004.
[22] 高子恒, 丁炜, 何静. 基于随机森林算法和MODIS数据的日喀则地区土地覆盖分类与动态监测[J]. 安徽农业科学, 2020, 48(16):1-12. DOI:10.3969/j.issn.0517-6611.2020.16.001.
[23] 黄芳芳, 雷鸣, 张力, 等. 基于随机森林和决策树的马尾松松材线虫病监测方法[J]. 信息通信, 2019, 32(12):32-36. DOI:10.3969/j.issn.1673-1131.2019.12.011.
[24] 姜红, 何清, 曾晓青, 等. 基于随机森林和卷积神经网络的FY-4A号卫星沙尘监测研究[J]. 高原气象, 2021, 40(3):680-689. DOI:10.7522/j.issn.1000-0534.2020.00060.
[25] Park S, Im J, Jang E, et al. Drought assessment and monitoring through blending of multi-sensor indices using machine learning approaches for different climate regions[J]. Agricultural and Forest Meteorology, 2016, 216:157-169. DOI:10.1016/j.agrformet.2015.10.011.
[26] 冯定原,邱新法.农业干旱的成因、指标、时空分布和防旱抗旱对策[J].中国减灾,1995(01):22-27.
[27] Hunt E R Jr, Rock B N Jr. Detection of changes in leaf water content using Near-and Middle-Infrared reflectances[J]. Remote Sensing of Environment, 1989, 30(1):43-54. DOI:10.1016/0034-4257(89)90046-1.
[28] 孙嵩松, 王喜民. 基于多源遥感数据的干旱监测研究[J]. 山东农业科学, 2019, 51(2):150-157. DOI:10.14083/j.issn.1001-4942.2019.02.029.
