The application of normalized multi-band drought index (NMDI) method in cropland drought monitoring

The method of Normalized Multi-Band Drought Index (NMDI) is constructed by fully considered the channel 2 (860nm) sensitive to leaf water content changes and the difference between two liquid water absorption bands (1640 nm and 2130 nm) as the soil and vegetation water sensitive band. The potential have been confirmed with the application in different time-series MODIS data. The results show: there is a significant correlation between Normalized Multi-Band Drought Index (NMDI) and soil moisture, the index adopted passed the significant F-tests with α = 0. 01. So the method of Normalized Multi-Band Drought Index (NMDI) could be used in Henan drought monitoring. We found that the index of NMDI application to areas with moderate vegetation coverage, however, needs further investigation.

[1]  Joel S. Levine,et al.  Evaluation of a technique for satellite-derived area estimation of forest fires , 1992 .

[2]  P. Fulé,et al.  Comparison of burn severity assessments using Differenced Normalized Burn Ratio and ground data , 2005 .

[3]  B. Gao NDWI—A normalized difference water index for remote sensing of vegetation liquid water from space , 1996 .

[4]  Louis Moreau,et al.  Monitoring fire activities in the boreal ecosystem , 1997 .

[5]  J. Qu,et al.  Forest fire detection using the normalized multi-band drought index (NMDI) with satellite measurements , 2008 .

[6]  E. Kasischke,et al.  Locating and estimating the areal extent of wildfires in alaskan boreal forests using multiple-season AVHRR NDVI composite data , 1995 .

[7]  M. Tamura,et al.  Estimation of leaf water status to monitor the risk of forest fires by using remotely sensed data , 2004 .

[8]  A. Viña,et al.  Drought Monitoring with NDVI-Based Standardized Vegetation Index , 2002 .

[9]  T. Carlson,et al.  On the relation between NDVI, fractional vegetation cover, and leaf area index , 1997 .

[10]  F. Kogan,et al.  Drought Monitoring and Corn Yield Estimation in Southern Africa from AVHRR Data , 1998 .

[11]  Limin Yang,et al.  An analysis of relationships among climate forcing and time-integrated NDVI of grasslands over the U.S. northern and central Great Plains , 1998 .

[12]  F. Kogan Remote sensing of weather impacts on vegetation in non-homogeneous areas , 1990 .

[13]  James P. Verdin,et al.  A five‐year analysis of MODIS NDVI and NDWI for grassland drought assessment over the central Great Plains of the United States , 2007 .

[14]  Pei Zhang,et al.  Monitoring and spatio-temporal evolution researching on vegetation leaf water in China , 2004, IGARSS 2004. 2004 IEEE International Geoscience and Remote Sensing Symposium.

[15]  J. Qu,et al.  NMDI: A normalized multi‐band drought index for monitoring soil and vegetation moisture with satellite remote sensing , 2007 .

[16]  S. Running,et al.  Developing Satellite-derived Estimates of Surface Moisture Status , 1993 .

[17]  S. Tarantola,et al.  Designing a spectral index to estimate vegetation water content from remote sensing data: Part 1 - Theoretical approach , 2002 .

[18]  Jay D. Miller,et al.  Mapping forest post-fire canopy consumption in several overstory types using multi-temporal Landsat TM and ETM data , 2002 .

[19]  S. Tarantola,et al.  Designing a spectral index to estimate vegetation water content from remote sensing data: Part 2. Validation and applications , 2002 .

[20]  Liping Di,et al.  Modelling relationships between NDVI and precipitation during vegetative growth cycles , 1994 .