Next Article in Journal
Erratum: Wang, G. et al. Multi-Spectral Remote Sensing of Phytoplankton Pigment Absorption Properties in Cyanobacteria Bloom Waters: A Regional Example in the Western Basin of Lake Erie. Remote Sens. 2017, 9, 1309
Previous Article in Journal
Hyperspectral Image Classification Using Convolutional Neural Networks and Multiple Feature Learning
Previous Article in Special Issue
Erratum: Tan C., et al. Spatial–Temporal Characteristics and Climatic Responses of Water Level Fluctuations of Global Major Lakes from 2002 to 2010. Remote Sens. 2017, 9, 150
Article Menu
Issue 2 (February) cover image

Export Article

Open AccessArticle
Remote Sens. 2018, 10(2), 301; https://doi.org/10.3390/rs10020301

Standardized Soil Moisture Index for Drought Monitoring Based on Soil Moisture Active Passive Observations and 36 Years of North American Land Data Assimilation System Data: A Case Study in the Southeast United States

1
Department of Geography and Anthropology, Louisiana State University, Baton Rouge, LA 70803, USA
2
NASA DEVELOP National Program, NASA Langley Research Center, MS 307, Hampton, VA 23681, USA
3
NASA DEVELOP National Program, Wise County Contractor, Wise County and City of Norton Clerk of Court’s Office, 206 E. Main Street, Wise, VA 24293, USA
*
Author to whom correspondence should be addressed.
Received: 17 January 2018 / Revised: 31 January 2018 / Accepted: 9 February 2018 / Published: 15 February 2018
(This article belongs to the Special Issue Remote Sensing of Climate Change and Water Resources)
Full-Text   |   PDF [3293 KB, uploaded 24 February 2018]   |  

Abstract

Droughts can severely reduce the productivity of agricultural lands and forests. The United States Department of Agriculture (USDA) Southeast Regional Climate Hub (SERCH) has launched the Lately Identified Geospecific Heightened Threat System (LIGHTS) to inform its users of potential water deficiency threats. The system identifies droughts and other climate anomalies such as extreme precipitation and heat stress. However, the LIGHTS model lacks input from soil moisture observations. This research aims to develop a simple and easy-to-interpret soil moisture and drought warning index—standardized soil moisture index (SSI)—by fusing the space-borne Soil Moisture Active Passive (SMAP) soil moisture data with the North American Land Data Assimilation System (NLDAS) Noah land surface model (LSM) output. Ground truth soil moisture data from the Soil Climate Analysis Network (SCAN) were collected for validation. As a result, the accuracy of using SMAP to monitor soil moisture content generally displayed a good statistical correlation with the SCAN data. The validation through the Palmer drought severity index (PDSI) and normalized difference water index (NDWI) suggested that SSI was effective and sensitive for short-term drought monitoring across large areas. View Full-Text
Keywords: remote sensing; Soil Moisture Active Passive; North American Land Data Assimilation System; drought; soil moisture; standardized soil moisture index remote sensing; Soil Moisture Active Passive; North American Land Data Assimilation System; drought; soil moisture; standardized soil moisture index
Figures

Graphical abstract

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
SciFeed

Share & Cite This Article

MDPI and ACS Style

Xu, Y.; Wang, L.; Ross, K.W.; Liu, C.; Berry, K. Standardized Soil Moisture Index for Drought Monitoring Based on Soil Moisture Active Passive Observations and 36 Years of North American Land Data Assimilation System Data: A Case Study in the Southeast United States. Remote Sens. 2018, 10, 301.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Remote Sens. EISSN 2072-4292 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top