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通过将SMAP土壤湿度反演数据同化到全球土壤水平衡模型中来进行农业干旱监测。

Agricultural Drought Monitoring via the Assimilation of SMAP Soil Moisture Retrievals Into a Global Soil Water Balance Model.

作者信息

Mladenova Iliana E, Bolten John D, Crow Wade, Sazib Nazmus, Reynolds Curt

机构信息

NASA GSFC, Hydrological Sciences Lab (617), Greenbelt, MD, United States.

UMD, Earth System Science Interdisciplinary Center, College Park, MD, United States.

出版信息

Front Big Data. 2020 Apr 9;3:10. doi: 10.3389/fdata.2020.00010. eCollection 2020.

DOI:10.3389/fdata.2020.00010
PMID:33693385
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7931972/
Abstract

From an agricultural perspective, drought refers to an unusual deficiency of plant available water in the root-zone of the soil profile. This paper focuses on evaluating the benefit of assimilating soil moisture retrievals from the Soil Moisture Active Passive (SMAP) mission into the USDA-FAS Palmer model for agricultural drought monitoring. This will be done by examining the standardized soil moisture anomaly index. The skill of the SMAP-enhanced Palmer model is assessed over three agricultural regions that have experienced major drought since the launch of SMAP in early 2015: (1) the 2015 drought in California (CA), USA, (2) the 2017 drought in South Africa, and (3) the 2018 mid-winter drought in Australia. During these three events, the SMAP-enhanced Palmer soil moisture estimates (PM+SMAP) are compared against the Climate Hazards group Infrared Precipitation with Stations (CHIRPS) rainfall dataset and Normalized Difference Vegetation Index (NDVI) products. Results demonstrate the benefit of assimilating SMAP and confirm its potential for improving U.S. Department of Agriculture-Foreign Agricultural Service root-zone soil moisture information generated using the Palmer model. In particular, PM+SMAP soil moisture estimates are shown to enhance the spatial variability of Palmer model root-zone soil moisture estimates and adjust the Palmer model drought response to improve its consistency with ancillary CHIRPS precipitation and NDVI information.

摘要

从农业角度来看，干旱是指土壤剖面根区植物可利用水分异常缺乏。本文重点评估将土壤湿度主动被动探测任务（SMAP）获取的土壤湿度数据同化到美国农业部外国农业服务局帕尔默模型中用于农业干旱监测的益处。这将通过检验标准化土壤湿度异常指数来实现。对2015年初SMAP发射以来经历过重大干旱的三个农业地区评估了SMAP增强帕尔默模型的技能：（1）2015年美国加利福尼亚州（CA）的干旱，（2）2017年南非的干旱，以及（3）2018年澳大利亚的冬季中期干旱。在这三次事件期间，将SMAP增强帕尔默土壤湿度估算值（PM+SMAP）与气候灾害组基于站点的红外降水数据集（CHIRPS）和归一化植被指数（NDVI）产品进行了比较。结果证明了同化SMAP的益处，并证实了其在改善美国农业部外国农业服务局使用帕尔默模型生成的根区土壤湿度信息方面的潜力。特别是，PM+SMAP土壤湿度估算值显示出增强了帕尔默模型根区土壤湿度估算值的空间变异性，并调整了帕尔默模型的干旱响应，以提高其与辅助CHIRPS降水和NDVI信息的一致性。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/372f/7931972/30dd8ff75665/fdata-03-00010-g0008.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/372f/7931972/bf91fae0e06b/fdata-03-00010-g0002.jpg
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本文引用的文献

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The climate hazards infrared precipitation with stations--a new environmental record for monitoring extremes.气候危害与站点的红外降水——一种新的极端环境监测记录。
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An overview of available crop growth and yield models for studies and assessments in agriculture.
用于农业研究与评估的现有作物生长和产量模型概述。
J Sci Food Agric. 2016 Feb;96(3):709-14. doi: 10.1002/jsfa.7359. Epub 2015 Sep 3.