| Soil moisture is the most active variable in the global water cycle,which affects the exchange of matter and energy between the surface and the atmosphere,and plays an important role in global climate change prediction,eco-hydrological land surface processes,global water and carbon cycle.In view of the important scientific research and practical value of soil moisture,how to obtain long-term soil moisture information has been the most important problem for its application in the related research fields.Microwave remote sensing technology has the characteristics of wide space coverage,high temporal resolution and sensitive to surface soil moisture.The microwave remote sensing has been the main way to soil moisture observation.Many microwave remote sensing satellite projects have continuously provided surface soil moisture products.Although the soil moisture products can be applied to large-scale surface hydrological processes,but the lower spatial resolution(> = 25km)can not capture the spatial heterogeneity of soil moisture at fine scale.This leads to uncertainties of the hydrological model at a watershed and small scale.What's more,with the hydrological and agricultural research developing,soil moisture products on behalf of the 0-5cm soil moisture has been unable to meet the ecological hydrological model needs for 0-30 cm or even deeper 0-200 cm root zone soil moisture information.In addition,it is worthy to note that the ultimate goal of the development of remote sensing technology is to achieve its social and economic benefits and serve the social development.Although soil moisture and microwave observation techniques have been widely used in many fields,but not in the field of forest disaster monitoring.It is of great significance to improve the accuracy of forest forecasting model and reduce the socio-economic loss caused by forest fires and realize the socio-economic value of remote sensing observation by applying microwave observation,which has a complementary data for optical data to forest fire monitoring.With the development of multi-source obvervation technology,multi-source observation data provide a possible means to solve the above-mentioned problems.How to combine multi-source data comprehensively and effectively is the key to solve the above problems.Therefore,according to the above-mentioned problems,this paper studies the following three aspects based on multi-source and multi-scale observation data:(1)A high-resolution near-surface soil moisture inversion algorithm over heterogeneous region is developed.Aiming at the problem that the high resolution near-surface soil moisture retrieval is unaccurate over the heterogeneous region,the airborne infrared-thermal data with higher resolution and less sub-pixel heterogeneity is used to improve the unaccuraty.For the temperature-vegetation(Ts/VI)space that the most widely applied method to retrieval soil moistrue using optical data,an evaluation of the airborne Ts/VI space(incorporated with air temperature)revealed that normalized difference vegetation index(NDVI)saturation and disturbed pixels were hindering the appropriate construction of the space.The non-disturbed Ts/VI space,which was modified by adjusting the NDVI saturation and eliminating the disturbed pixels,was clearly correlated with the measured SM.Moreover,based on the bayesian maximum entropy fusion framework,the multi-source data related to soil moisture information,including irrigation statistics and ground observation data,satellite optical data(ASTER),airborne passive microwave(PLMR)soil moisture products,are integrated to improve the soil moisture accuracy over heterogeneous region.(2)The retrieval of the long-term deep soil moisture.Based on the the long-term(1978-2014)near-surface soil moisture products(ECV_SM),the long-term deep soil moisture products(ECV_RZSM)were retrieved by the iterative exponential filtering algorithm,and the core parameters T was determined as 15 days.The results showed that the deep soil moisture products are in good agreement with the ground observation data,and can capture the temporal and spatial changes of soil moisture.(3)Explore the potential of near-surface,deep soil moisture and related microwave observation in monitoring forest fire monitoring.We analyzed t he relationship between surface/deep soil moisture,Microwave Polarization Difference Index(MPDI),vegetation optical depth(VOD)and the forest fire sensitivity index(live fuel moisture contetn,LFMC).The results showed that the sensitivity of the microwave indice to LFMC was as follows: near-surface soil moisture< deep soil moisture< MPDI< VOD.In particular,VOD at X-band is the best microwave index for LFMC,which has great potential for LFMC monitoring.This provides an complementary data for optical data monitoring LFMC,and provides an important data source for the forest fire prediction model.In conclusion,this study,based on the multi-source observation data,make an attempt to tackle some issues in current microwave soil moisture products about their low resolution,limited depth and limited application fields.The theoretical research and practical application development have yet to be further explored. |
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