A new agricultural drought monitoring index combining MODIS NDWI and day–night land surface temperatures: a case study in China

2013 ◽  
Vol 34 (24) ◽  
pp. 8986-9001 ◽  
Author(s):  
Hao Sun ◽  
Xiang Zhao ◽  
Yunhao Chen ◽  
Adu Gong ◽  
Jing Yang
Keyword(s):  
Land Surface ◽  
Agricultural Drought ◽  
Drought Monitoring ◽  
Surface Temperatures ◽  
Land Surface Temperatures ◽  
Monitoring Index

scholarly journals The Role of Vegetation in Mitigating Urban Land Surface Temperatures: A Case Study of Munich, Germany during the Warm Season

2015 ◽  
Vol 7 (4) ◽  
pp. 4689-4706 ◽  
Author(s):  
Sadroddin Alavipanah ◽  
Martin Wegmann ◽  
Salman Qureshi ◽  
Qihao Weng ◽  
Thomas Koellner
Keyword(s):  
Land Surface ◽  
Warm Season ◽  
Urban Land ◽  
Surface Temperatures ◽  
Land Surface Temperatures

Modelling future land use land cover changes and their impacts on land surface temperatures in Rajshahi, Bangladesh

2020 ◽  
Vol 18 ◽  
pp. 100314 ◽  
Author(s):  
Abdulla - Al Kafy ◽  
Md. Shahinoor Rahman ◽  
Abdullah-Al- Faisal ◽  
Mohammad Mahmudul Hasan ◽  
Muhaiminul Islam
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Land Surface ◽  
Land Cover Changes ◽  
Land Use Land Cover ◽  
Surface Temperatures ◽  
Land Surface Temperatures

scholarly journals On the utility of land surface models for agricultural drought monitoring

2012 ◽  
Vol 16 (9) ◽  
pp. 3451-3460 ◽  
Author(s):  
W. T. Crow ◽  
S. V. Kumar ◽  
J. D. Bolten
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Cross Correlation ◽  
Root Zone ◽  
Vegetation Indices ◽  
Agricultural Drought ◽  
Drought Monitoring ◽  
Global Evaluation ◽  
Land Surface Models ◽  
Surface Models

Abstract. The lagged rank cross-correlation between model-derived root-zone soil moisture estimates and remotely sensed vegetation indices (VI) is examined between January 2000 and December 2010 to quantify the skill of various soil moisture models for agricultural drought monitoring. Examined modeling strategies range from a simple antecedent precipitation index to the application of modern land surface models (LSMs) based on complex water and energy balance formulations. A quasi-global evaluation of lagged VI/soil moisture cross-correlation suggests, when globally averaged across the entire annual cycle, soil moisture estimates obtained from complex LSMs provide little added skill (< 5% in relative terms) in anticipating variations in vegetation condition relative to a simplified water accounting procedure based solely on observed precipitation. However, larger amounts of added skill (5–15% in relative terms) can be identified when focusing exclusively on the extra-tropical growing season and/or utilizing soil moisture values acquired by averaging across a multi-model ensemble.

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