基于MODIS数据的黄河源区土壤干湿状况时空格局变化
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摘要
为分析黄河源区土壤干湿状况时空格局变化,基于2007—2016年日序数第193～257天的MODIS 1 km分辨率地表温度(LST)和植被指数(NDVI)产品反演10年的温度-植被干旱指数(TVDI)。分析结果表明:2008—2010年实测土壤水分与TVDI的线性关系的相关系数达到0.7,说明TVDI可作为干旱指标有效地指示黄河源区的土壤干湿状况。在年际变化趋势上,10年间日序数第193～257天的TVDI均在0.4～0.6之间,属干旱分级中的正常状况,但东南部地区整体处于干旱-重旱。中、低植被覆盖区域的干旱趋势较为相似,10年来旱情均较为严重,TVDI数值均在0.6～1.0间浮动,其中低植被覆盖区所有时相的TVDI在10年内数值均在0.6及以上,表明旱情持续。高植被覆盖区域严重干旱的情况较中低覆盖区域缓解,但是干旱依然在10年间持续发生。旱情的空间变化特征显著,严重旱情多集中于东北部和东南部区域,分布趋势基本与中西部整体土壤水分较充足的客观事实吻合。
To determine the drought situation over the source area of Yellow River(SAYR) and to analyze the spatiotemporal patterns, the moderate resolution imaging spectroradiometer(MODIS) products, including MOD11 A2(8-day land surface temperature)and MOD13 A2(16-day vegetation index)in 1 km resolution, were used to obtain the temperature-vegetation drought index(TVDI) over a 10-year period from 2007 to 2016. The results are given as follows. 1) Significant correlation coefficient of 0.7 occurred in the linear fitting between TVDI and in-situ soil moisture measurements, indicating that TVDI could be recognized as an effective drought indicator over the SAYR for monitoring drought. 2) The average TVDI value over the whole study area ranged between 0.4 and 0.6 within the study period, showing a normal condition by drought classification standard. However, the southeastern region revealed various drought conditions. Moreover, grassland in different vegetation coverages in the past 10 years had serious drought conditions. Medium and low vegetation-covered areas showed the similar trend with the TVDI value varying from 0.6 to 1. Severe drought in high vegetation-covered regions was alleviated compared to those with low vegetation coverage. 3) The spatial variation of drought was significant. Severe drought occurred mostly in the northeastern and southeastern regions. The distribution trend basically corresponded to the fact that the overall soil water content in central and western regions was adequate while the regions with severe drought mainly distributed in the northeastern and southeastern regions.
To determine the drought situation over the source area of Yellow River(SAYR) and to analyze the spatiotemporal patterns, the moderate resolution imaging spectroradiometer(MODIS) products, including MOD11 A2(8-day land surface temperature)and MOD13 A2(16-day vegetation index)in 1 km resolution, were used to obtain the temperature-vegetation drought index(TVDI) over a 10-year period from 2007 to 2016. The results are given as follows. 1) Significant correlation coefficient of 0.7 occurred in the linear fitting between TVDI and in-situ soil moisture measurements, indicating that TVDI could be recognized as an effective drought indicator over the SAYR for monitoring drought. 2) The average TVDI value over the whole study area ranged between 0.4 and 0.6 within the study period, showing a normal condition by drought classification standard. However, the southeastern region revealed various drought conditions. Moreover, grassland in different vegetation coverages in the past 10 years had serious drought conditions. Medium and low vegetation-covered areas showed the similar trend with the TVDI value varying from 0.6 to 1. Severe drought in high vegetation-covered regions was alleviated compared to those with low vegetation coverage. 3) The spatial variation of drought was significant. Severe drought occurred mostly in the northeastern and southeastern regions. The distribution trend basically corresponded to the fact that the overall soil water content in central and western regions was adequate while the regions with severe drought mainly distributed in the northeastern and southeastern regions.
引文
[1] 沙莎, 郭铌, 李耀辉,等. 我国温度植被旱情指数 TVDI的应用现状及问题简述[J]. 干旱气象, 2014, 32(1):128-134.
[2] 刘宪锋, 朱秀芳, 潘耀忠,等. 农业干旱监测研究进展与展望[J]. 地理学报, 2015, 70(11):1 835-1 848.
[3] 柳钦火, 辛景峰, 辛晓洲,等. 基于地表温度和植被指数的农业干旱遥感监测方法[J]. 科技导报, 2007, 25(6):12-18.
[4] Li H, Li C, Lin Y, et al. Surface temperature correction in TVDI to evaluate soil moisture over a large area[J]. Journal of Food, Agriculture & Environment, 2010, 8(3/4): 1 141-1 145.
[5] Carlson T. An overview of the “triangle method” for estimating surface evapotranspiration and soil moisture from satellite imagery[J]. Sensors, 2007, 7(8): 1 612-1 629.
[6] Price J C. Using spatial context in satellite data to infer regional scale evapotranspiration[J]. IEEE Transactions on Geoscience and Remote Sensing, 1990, 28(5): 940-948.
[7] Carlson T N, Gillies R R, Schmugge T J. An interpretation of methodologies for indirect measurement of soil-water content[J]. Agricultural and Forest Meteorology, 1995, 77(3): 191-205.
[8] Gillies R R, Carlson T N, Cui J, et al. A verification of the ‘triangle’ method for obtaining surface soil water content and energy fluxes from remote measurements of the normalized difference vegetation index (NDVI) and surface radiant temperature[J]. International Journal of Remote Sensing, 1997, 18(15): 3 145-3 166.
[9] Moranm S, Clarke T R, Inoue Y, et al. Estimating crop water deficiency using the relation between surface minus air temperature and spectral vegetation index[J]. Remote Sensing of Environment, 1994, 49: 246-263.
[10] Stisen S, Sandholt I, N?rgaard A, et al. Combining the triangle methodl with thermal inertia to estimate regional evapotanspiration-applied to MSG-SEVIRI data in the Senegal River Basin[J]. Remote Sensing of Environment, 2008, 112(3): 1 242-1 255.
[11] Gillies R R, Temesgen B, Quattrochi D A, et al. Coupling thermal infrared and visible satellite measurements to infer biophysical variables at the land surface[M]//Thermal Remote Sensing in Land Surface Processes, 2003.doi:10.1201/9280203502174-06.
[12] Sandholt I, Rasmussen K, Andersen J. A simple interpretation of the surface temperature/vegetation index space for assessment of surface moisture status[J]. Remote Sensing of Environment, 2002, 79(2): 213-224.
[13] Mallick K, Bhattacharya B K, Patel N K. Estimating volumetric surface moisture content for cropped soils using a soil wetness index based on surface temperature and NDVI[J]. Agricultural & Forest Meteorology, 2009, 149(8): 1 327-1 342.
[14] Patel N R, Anupashsha R, Kumar S, et al. Assessing potential of MODIS derived temperature/vegetation condition index (TVDI) to infer soil moisture status[J]. International Journal of Remote Sensing, 2009, 30(1): 23-39.
[15] Holzman M E, Rivas R, Piccolo M. Estimating soil moisture and the relationship with crop yield using surface temperature and vegetation index[J]. International Journal of Applied Earth Observation & Geoinformation, 2014, 28(5): 181-192.
[16] 李新辉, 宋小宁, 周霞. 半干旱区土壤湿度遥感监测方法研究[J].地理与地理信息科学, 2010, 26(1):90-93.
[17] 王慧慧, 周廷刚, 杜嘉,等. 温度植被旱情指数在吉林省干旱监测中的应用[J]. 遥感技术与应用, 2013, 28(2):324-329.
[18] 陈立文, 张友静, 邓世赞,等. 基于温度植被干旱指数的黄河源区土壤表层含水量反演[J]. 水利水电科技进展, 2012, 32(4): 6-22.
[19] 李开明, 李绚, 王翠云,等. 黄河源区气候变化的环境效应研究[J]. 冰川冻土, 2013, 35(5):1 183-1 192.
[20] 张镱锂, 刘林山, 摆万奇,等. 黄河源地区草地退化空间特征[J]. 地理学报, 2006, 61(1): 3-14.
[21] 刘浩. 基于MODIS数据的土壤湿度反演研究[D]. 山东烟台:鲁东大学, 2014.
[22] Wang Y, Song X, Leng P, et al. Estimation of surface soil moisture using Fengyun-2E (FY-2E) data: a case study over the Sources Area of the Yellow River[C]//2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Beijing, 2016:4 327-4 330.
[23] Garcia M, Fernández N, Villagarcía L, et al. Accuracy of the temperature-vegetation dryness index using MODIS under water-limited vs. energy-limited evapotranspiration conditions[J]. Remote Sensing of Environment, 2014, 149: 100-117.
[24] 薛天翼, 白建军. 基于TVDI和气象数据的陕西省春季旱情时空分析[J]. 水土保持研究, 2017, 24(4): 240-246.
[25] 姚春生, 张增祥, 汪潇. 使用温度植被干旱指数法 (TVDI) 反演新疆土壤湿度[J]. 遥感技术与应用, 2004, 19(6): 473-478.
[26] 吴孟泉, 崔伟宏, 李景刚. 温度植被干旱指数 (TVDI) 在复杂山区干旱监测的应用研究[J]. 干旱区地理, 2007(1): 30-35.
[27] Chen J, Wang C, Jiang H, et al. Estimating soil moisture using Temperature-Vegetation Dryness Index (TVDI) in the Huang-huai-hai (HHH) plain[J]. International Journal of Remote Sensing, 2011, 32(4): 1 165-1 177.
[28] Wang C, Qi S, Niu Z, et al. Evaluating soil moisture status in China using the temperature-vegetation dryness index (TVDI)[J]. Canadian Journal of Remote Sensing, 2004, 30(5): 671-679.
[29] Hoffmann H, Jensen R, Thomsen A, et al. Crop water stress maps for an entire growing season from visible and thermal UAV imagery[J]. Biogeosciences, 2016, 13: 1-30.
[2] 刘宪锋, 朱秀芳, 潘耀忠,等. 农业干旱监测研究进展与展望[J]. 地理学报, 2015, 70(11):1 835-1 848.
[3] 柳钦火, 辛景峰, 辛晓洲,等. 基于地表温度和植被指数的农业干旱遥感监测方法[J]. 科技导报, 2007, 25(6):12-18.
[4] Li H, Li C, Lin Y, et al. Surface temperature correction in TVDI to evaluate soil moisture over a large area[J]. Journal of Food, Agriculture & Environment, 2010, 8(3/4): 1 141-1 145.
[5] Carlson T. An overview of the “triangle method” for estimating surface evapotranspiration and soil moisture from satellite imagery[J]. Sensors, 2007, 7(8): 1 612-1 629.
[6] Price J C. Using spatial context in satellite data to infer regional scale evapotranspiration[J]. IEEE Transactions on Geoscience and Remote Sensing, 1990, 28(5): 940-948.
[7] Carlson T N, Gillies R R, Schmugge T J. An interpretation of methodologies for indirect measurement of soil-water content[J]. Agricultural and Forest Meteorology, 1995, 77(3): 191-205.
[8] Gillies R R, Carlson T N, Cui J, et al. A verification of the ‘triangle’ method for obtaining surface soil water content and energy fluxes from remote measurements of the normalized difference vegetation index (NDVI) and surface radiant temperature[J]. International Journal of Remote Sensing, 1997, 18(15): 3 145-3 166.
[9] Moranm S, Clarke T R, Inoue Y, et al. Estimating crop water deficiency using the relation between surface minus air temperature and spectral vegetation index[J]. Remote Sensing of Environment, 1994, 49: 246-263.
[10] Stisen S, Sandholt I, N?rgaard A, et al. Combining the triangle methodl with thermal inertia to estimate regional evapotanspiration-applied to MSG-SEVIRI data in the Senegal River Basin[J]. Remote Sensing of Environment, 2008, 112(3): 1 242-1 255.
[11] Gillies R R, Temesgen B, Quattrochi D A, et al. Coupling thermal infrared and visible satellite measurements to infer biophysical variables at the land surface[M]//Thermal Remote Sensing in Land Surface Processes, 2003.doi:10.1201/9280203502174-06.
[12] Sandholt I, Rasmussen K, Andersen J. A simple interpretation of the surface temperature/vegetation index space for assessment of surface moisture status[J]. Remote Sensing of Environment, 2002, 79(2): 213-224.
[13] Mallick K, Bhattacharya B K, Patel N K. Estimating volumetric surface moisture content for cropped soils using a soil wetness index based on surface temperature and NDVI[J]. Agricultural & Forest Meteorology, 2009, 149(8): 1 327-1 342.
[14] Patel N R, Anupashsha R, Kumar S, et al. Assessing potential of MODIS derived temperature/vegetation condition index (TVDI) to infer soil moisture status[J]. International Journal of Remote Sensing, 2009, 30(1): 23-39.
[15] Holzman M E, Rivas R, Piccolo M. Estimating soil moisture and the relationship with crop yield using surface temperature and vegetation index[J]. International Journal of Applied Earth Observation & Geoinformation, 2014, 28(5): 181-192.
[16] 李新辉, 宋小宁, 周霞. 半干旱区土壤湿度遥感监测方法研究[J].地理与地理信息科学, 2010, 26(1):90-93.
[17] 王慧慧, 周廷刚, 杜嘉,等. 温度植被旱情指数在吉林省干旱监测中的应用[J]. 遥感技术与应用, 2013, 28(2):324-329.
[18] 陈立文, 张友静, 邓世赞,等. 基于温度植被干旱指数的黄河源区土壤表层含水量反演[J]. 水利水电科技进展, 2012, 32(4): 6-22.
[19] 李开明, 李绚, 王翠云,等. 黄河源区气候变化的环境效应研究[J]. 冰川冻土, 2013, 35(5):1 183-1 192.
[20] 张镱锂, 刘林山, 摆万奇,等. 黄河源地区草地退化空间特征[J]. 地理学报, 2006, 61(1): 3-14.
[21] 刘浩. 基于MODIS数据的土壤湿度反演研究[D]. 山东烟台:鲁东大学, 2014.
[22] Wang Y, Song X, Leng P, et al. Estimation of surface soil moisture using Fengyun-2E (FY-2E) data: a case study over the Sources Area of the Yellow River[C]//2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Beijing, 2016:4 327-4 330.
[23] Garcia M, Fernández N, Villagarcía L, et al. Accuracy of the temperature-vegetation dryness index using MODIS under water-limited vs. energy-limited evapotranspiration conditions[J]. Remote Sensing of Environment, 2014, 149: 100-117.
[24] 薛天翼, 白建军. 基于TVDI和气象数据的陕西省春季旱情时空分析[J]. 水土保持研究, 2017, 24(4): 240-246.
[25] 姚春生, 张增祥, 汪潇. 使用温度植被干旱指数法 (TVDI) 反演新疆土壤湿度[J]. 遥感技术与应用, 2004, 19(6): 473-478.
[26] 吴孟泉, 崔伟宏, 李景刚. 温度植被干旱指数 (TVDI) 在复杂山区干旱监测的应用研究[J]. 干旱区地理, 2007(1): 30-35.
[27] Chen J, Wang C, Jiang H, et al. Estimating soil moisture using Temperature-Vegetation Dryness Index (TVDI) in the Huang-huai-hai (HHH) plain[J]. International Journal of Remote Sensing, 2011, 32(4): 1 165-1 177.
[28] Wang C, Qi S, Niu Z, et al. Evaluating soil moisture status in China using the temperature-vegetation dryness index (TVDI)[J]. Canadian Journal of Remote Sensing, 2004, 30(5): 671-679.
[29] Hoffmann H, Jensen R, Thomsen A, et al. Crop water stress maps for an entire growing season from visible and thermal UAV imagery[J]. Biogeosciences, 2016, 13: 1-30.