Soil Moisture and Vegetation Growth are the most important and direct index in drought monitoring, and the
spectral interpretation of vegetation and soil are serious factors in the judgment of drought degree. Based on
the spectral character of water, recently, a new model of Surface Water Capacity Index (SWCI) has been put
forward, and the index is more sensitive to the surface water content, and suit for regional drought
monitoring. The comparative analysis showed: SWCI is more sensitive than NDVI to monitoring surface soil
water content; this is available in real-time soil drought monitoring.
Soil Moisture and Vegetation Growth are the most important and direct index in drought monitoring, and the
spectrum interpretation of vegetation and soil are serious factors in the judgment of drought degree. To find a
more real-time monitoring index of cropland soil moisture by remote sensing, a Cropland Soil Moisture Index
(CSMI) was established in this paper based on the effective reflections of Normalized Difference Vegetation
Index (NDVI) on deeper soil moisture and well expressions of Surface Water Content Index (SWCI) on
surface soil moisture. By validation with different time-series MODIS data, the Cropland Soil Moisture Index
(CSMI) not only overcome the limitation of hysteretic nature and saturated quickly of Normalized Difference
Vegetation Index (NDVI), but also take the advantage of the Surface Water Content Index (SWCI) which
effectively reduce the atmosphere disturbance and retrieval surface soil water content better. The index passed
the significant F-tests with α = 0. 01, and is a true real-time drought monitoring index.
The thesis, on the basis of the researches in the past, discusses the researches on agricultural drought
monitoring, forecasting and loss assessment evaluation as well as its application status in China. While
discussing and comparing different soil moisture monitoring methods, the thesis also introduces Gstar-1
which is an automatic soil moisture observer with independent property right, and CSMI which is the
new remote sensing monitoring index for soil moisture on the basis of MODIS data, and gives a
comprehensive introduction to the loss assessment of China. Through the real-time monitoring,
forecasting and assessment of drought occurrence and development, the thesis is dedicated to reducing
the influence of drought to agricultural production to the largest extent. At last, on the basis of the
problems in research, the thesis proposes the future research direction.
Based on the relationship between yield of winter wheat at different growth stages and meteorological factors, the actual
yield can be segregated step by step. Based on the yield data of reviving period to heading date without meteorological
disasters, the ideal yield were calculated with Lagrange's Interpolation Polynomial and the yield loss rate affected by
meteorological disasters have been assessed. By analyzing the degree of different meteorological disasters and their
effect on yield from turning green period to heading date, the model of evaluation on late frost loss was defined. Take
Shangqiu station as representative in Huanghuai Area, the yield loss caused by frost from 1980 to 2006 were simulated
and analyzed. The results shows that the average rate of yield loss was 11.7% and the heavy disaster years of yield loss
can be above 30%.
Soil Moisture and Vegetation Growth are the most important and direct index in drought monitoring, and the
spectrum interpretation of vegetation and soil are serious factors in the judgment of drought degree. To find a
more real-time monitoring index of cropland soil moisture by remote sensing, a Cropland Soil Moisture Index
(CSMI) was established in this paper based on the effective reflections of Normalized Difference Vegetation
Index (NDVI) on deeper soil moisture and well expressions of Surface Water Content Index (SWCI) on
surface soil moisture. By validation with different time-series MODIS data, the Cropland Soil Moisture Index
(CSMI) not only overcome the limitation of hysteretic nature and saturated quickly of Normalized Difference
Vegetation Index (NDVI), but also take the advantage of the Surface Water Content Index (SWCI) which
effectively reduce the atmosphere disturbance and retrieval surface soil water content better. The index passed
the significant F-tests with α = 0. 01, and is a true real-time drought monitoring index.
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