| Soil water is an important factor in the process of the land atmosphere energy exchange, which has a strong role in the control of land surface evapotranspiration, water transport, and carbon cycle. It is a measure of an important indicator of the level of soil drought climate, ecology, hydrology, agriculture and other fields. Soil moisture is the most basic factor of plant water stress and monitoring crop drought. With the increasing demand of agriculture and domestic water, the shortage of agricultural water resources also affectes the production and development of agriculture. Soil moisture content monitoring can accurately grasp the distribution of soil moisture, predicting future development status, can improve the efficiency of agricultural water, saving water resources, and has important significance for improving the efficiency of agricultural irrigation water use efficiency, sustainable utilization of water and Soil resources, and the monitoring of drought. At the same time, the temporal and spatial distribution of soil moisture is the important impact factors of soil hydrological and is one of the important content of the regional hydrological process research, which can provide ground observation data support for remote sensing inversion, and provides an important reference for ecological construction and planning. The study of spatial and temporal variation of soil moisture plays an important role in improving the utilization efficiency and management level of soil water resources.This paper takes Kenli County o and Wudi County f the Yellow River Delta as the research area. Firstly, the water status of Kenli County and Wudi County were analyzed systematically based on the measured water data in the study area. In order to explore the feasible methods of predicting soil water content by using the combination of near earth high spectrum and remote sensing image data, the data of Kenli County in the spring of 2014 was used as an example. Based on the hyper-spectral narrow-band reflectance measured outdoors Landsat 8 wide-band reflectance were simulated with two fitting methods, center wavelength reflectance and band average reflectance methods; by means of band combination in four modes, with sensitive spectral parameters selected according to correlativity; then hyper-spectral single-form band combination and multi-form band combination soil moisture estimation models were established with the multiple stepwise linear regression analysis method, and then screened with the two fitting methods for the best model. Soil information in the remote sensing images was obtained using the linear mixed pixel decomposition method after excluding the vegetation information; the soil information was compared with the measured hyper-spectral reflectance and remote sensing image reflectances were corrected with the ratio and mean method. On this basis, the best hyper-spectral model for estimation of soil moisture contents was applied to the Landsat 8 satellite images. Hence, remote sensing inversion of soil moisture contents in the study area was realized. By using the data of Wudi County in the spring of 2014 and other seasonal data of Kenli in 2013, the spatial and temporal applicability of remote sensing inversion model was tested. Finally, the remote sensing inversion model was applied to Kenli county and Wudi County in 2015, and thesoil moisture content was obtained in the spring of 2015. The main conclusions are as follows:(1) According to the statistical analysis of field survey data, the average soil moisture content of Kenli County in autumn of 2013 and spring of 2014 was close; the content in the winter of 2013 was low. The average soil moisture in autumn of 2013 and spring of 2014 in Wudi County was close, and the average content of soil moisture in winter, summer and autumn of 2014 was low. From the spatial distribution, soil moisture had a more consistent spatial distribution pattern with bigger spatial fragmentation degree in the spring, autumn, winter of 2013 and spring of 2014 in Kenli County; while water spatial continuity was strong with continuous distribution in summer of 2014. Soil moisture had a more consistent spatial distribution pattern with bigger spatial fragmentation degree in the fall, winter of 2013, and spring of 2014; while water spatial continuity was strong with continuous distribution in summer,fall of 2014 in Wudi County. From the inter annual change, soil moisture in the spring of Kenli in 2014 was lower than that in spring of 2013. Soil moisture in the fall of Wudi in 2014 was lower than that in spring of 2013. Overall, in 2014, the soil water content in the study area was lower than that in 2013. There were significant differences in Soil water content of different vegetation types. In Wudi and Kenli, similar laws were presented. Cultivated soil water content were lower than those of saline land, salt wasteland, Soil moisture content showed bare land >suaeda salsa > reed > thatch.(2) The spectral curves generally proceeded gently in a similar shape; Soil reflectance tended to decline with rising water content; different water content of the soil spectral reflectance showed that the intensity of the difference, in the shorter wavelength, the reflectivity with the Soil moisture increased rapidly, while in the longer wavelength part, the reflectivity change is relatively gentle; and to a certain extent, the 7 bands of Landsat 8 OLI were related with Soil moisture;(3) Estimation model of spectral parameters based on difference mode of single-form band combination were superior; and the model based on multi-form band combination was superior to that based on single-form band combination; the best model was the estimation model based on multi-form band combination of average reflectance, Y=60.833-12.737×R655/R440+208.397×(R1610-R2200)+67.536×(R865-R2200)+0.815×(R440+R480)/(R440-R480),the decision coefficient of the model R 2 is 0.701.(4) The outdoor measured reflectance fitted with the band average reflectance method was quite consistent with the remote sensing image reflectance in variation trend up to an extremely significant level. Therefore, the model can be applied to the estimation of soil moisture in Kenli County in remote sensing image, and it can realize the remote sensing retrieval of regional soil moisture.(5) Soil moisture data in spring of Wudi County in 2014, and of summer, autumn and winter of Kenli in 2013 was used to test the applicability of the time and space in the remote sensing inversion model of spring Kenli County in 2014. The results indicated that the 2014 spring Kenli County remote sensing inversion model was applicable to the inversion of water in spring 2014, Wudi County, but effect of Kenli County other seasons was poor, which showed that 2014 spring Kenli County remote sensing inversion model had applicability in other area of the same period in the Yellow River Delta, but not with seasons in the universality. |
PDF Full Text Request