Google Earth Engine（GEE）——NASA-USDA增强型SMAP全球土壤水分数据以10公里的空间分辨率提供全球的土壤水分信息

NASA-USDA增强型SMAP全球土壤水分数据以10公里的空间分辨率提供全球的土壤水分信息。该数据集包括：地表和地下土壤湿度（毫米），土壤湿度剖面（%），地表和地下土壤湿度异常（-）。

该数据集是通过使用一维集合卡尔曼滤波（EnKF）数据同化方法，将卫星派生的土壤水分主动被动（SMAP）3级土壤水分观测数据整合到修正的两层Palmer模型中而产生的。土壤水分异常是根据相关日期的气候学计算出来的。气候学是根据SMAP卫星观测的全部数据记录和31天中心移动窗口法估算的。SMAP土壤水分观测的同化有助于改善基于模型的土壤水分预测，特别是在世界缺乏高质量降水数据的仪器不良地区。

这个数据集是由美国宇航局戈达德空间飞行中心的水文科学实验室与

美国农业部对外农业服务局和美国农业部水文和遥感实验室合作开发。

Dataset Availability

2015-04-01T00:00:00 - 2020-12-31T00:00:00

Dataset Provider

NASA GSFC

Collection Snippet

ee.ImageCollection("NASA_USDA/HSL/SMAP_soil_moisture")

Resolution

27830 meters

Bands Table

* = Values are estimated

数据引用：

Mladenova, I.E., Bolten, J.D., Crow, W., Sazib, N. and Reynolds, C., 2020. Agricultural drought monitoring via the assimilation of SMAP soil moisture retrievals into a global soil water balance model. Front. Big Data, 3(10). doi:10.3389/fdata.2020.00010Article

Mladenova, I.E., Bolten, J.D., Crow, W.T., Sazib, N., Cosh, M.H., Tucker, C.J. and Reynolds, C., 2019. Evaluating the operational application of SMAP for global agricultural drought monitoring. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 12(9): 3387-3397. doi:10.1109/JSTARS.2019.2923555Article

Sazib, N., Mladenova, I., & Bolten, J. (2020). Assessing the Impact of ENSO on Agriculture over Africa using Earth Observation Data. Frontiers in Sustainable Food Systems, 4, 188. doi:10.3389/fsufs.2020.509914ArticleGoogle Scholar

Sazib, N., Mladenova, I. and Bolten, J., 2018. Leveraging the google earth engine for drought assessment using global soil moisture data. Remote sensing, 10(8): 1265. doi:10.3390/rs10081265Article

Bolten, J., W.T. Crow, X. Zhan, T.J. Jackson, and C.A. Reynolds (2010). Evaluating the Utility of Remotely Sensed Soil Moisture Retrievals for Operational Agricultural Drought Monitoring, IEEE Transactions on Geoscience and Remote Sensing, 3(1): 57-66. doi:10.1109/JSTARS.2009.2037163ArticleGoogle Scholar

Bolten, J., and W. T. Crow (2012). Improved prediction of quasi-global vegetation conditions using remotely sensed surface soil moisture, Geophysical Research Letters, 39: (L19406). [doi:10.1029/2012GL053470][https://doi.org/10.1029/2012GL053470]ArticleGoogle Scholar

Entekhabi, D, Njoku, EG, O'Neill, PE, Kellogg, KH, Crow, WT, Edelstein, WN, Entin, JK, Goodman, SD, Jackson, TJ, Johnson, J, Kimball, J, Piepmeier, JR, Koster, RD, Martin, N, McDonald, KC, Moghaddam, M, Moran, S, Reichle, R, Shi, JC, Spencer, MW, Thurman, SW, Tsang, L & Van Zyl, J (2010). The soil moisture active passive (SMAP) mission, Proceedings of the IEEE, 98(5): 704-716. doi:10.1109/JPROC.2010.2043918Article

I. E. Mladenova, J.D. Bolten, W.T. Crow, M.C. Anderson, C.R. Hain, D.M. Johnson, R. Mueller (2017). Intercomparison of Soil Moisture, Evaporative Stress, and Vegetation Indices for Estimating Corn and Soybean Yields Over the U.S., IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 10(4): 1328-1343. doi:10.1109/JSTARS.2016.2639338Article

O'Neill, P. E., S. Chan, E. G. Njoku, T. Jackson, and R. Bindlish (2016). SMAP L3 Radiometer Global Daily 36 km EASE-Grid Soil Moisture, Version 4. Boulder, Colorado USA. NASA National Snow and Ice Data Center Distributed Active Archive Center. doi:10.5067/ZX7YX2Y2LHEB

代码：

var dataset = ee.ImageCollection('NASA_USDA/HSL/SMAP_soil_moisture')
                  .filter(ee.Filter.date('2017-04-01', '2017-04-30'));
var soilMoisture = dataset.select('ssm');
var soilMoistureVis = {
  min: 0.0,
  max: 28.0,
  palette: ['0300ff', '418504', 'efff07', 'efff07', 'ff0303'],
};
Map.setCenter(-6.746, 46.529, 2);
Map.addLayer(soilMoisture, soilMoistureVis, 'Soil Moisture');