| The growth of industrial and domestic water caused by economic and social development has continuously squeezed the space of agricultural water in recent years. Population increase makes the requirement for food security guarantee improve. The pressure of both aspects increases the difficulty of agricultural water supply security guarantee. Thus, it is necessary to carry out scientific irrigation and fine agricultural water management.The shortage of agricultural water management information will result in irrigation delay or waste of water. Timely and accurate acquisition of information has important significance for improving water management scientificity and level. Soil moisture content information is important basic information of scientific management of agricultural water. According to fine soil moisture content data, water need distribution of crops can be accurately grasped before irrigation. According to continuous space-time monitoring of soil moisture content change, agricultural irrigation effect can be well mastered in the irrigation process and after irrigation, and meanwhile space distribution of irrigation water can be estimated. Such information can offer support for estimating water needed by crops, making, implementing and adjusting irrigation water plan as well as monitoring irrigation water and estimating irrigation effect, greatly improve irrigation water management level and boost use utilization efficiency.The ways to acquire soil moisture content information mainly includes ground measurement, remote sensing inversion and model simulation. The three ways have their respective strengths and weaknesses. Ground measurement has relatively high precision, but its sampling cost is high and space representativeness is poor. Remote sensing inversion has spatial continuity, but most values are satellite transit instantaneous values. Besides, its time continuity is poor. In the cultivated land area with irrigation, it is difficult for the mode to accurately simulate spatial and temporal changes of soil moisture content in the irrigation area due to the influence of human activities, when spatial and temporal distribution of irrigation water volume cannot be accurately gained. Single use of any way cannot meet data demand of agricultural water management for soil moisture content. Data assimilation technology can realize the advantage integration of ground measurement, remote sensing inversion and model simulation, offer temporally and spatially continuous simulated data of soil moisture content and effectively improve consistency problem of multi-source data.Soil moisture content data assimilation started from 1990s. At present, the most representative data assimilation systems include North America Land Data Assimilation System (NLDAS), Europe Land Data Assimilation System (ELDAS) and China Land Data Assimilation System etc. Most of these depend on geostationary satellite or passive microwave data and can provide land data with high time resolution. However, spatial resolution is rough, generally above 10km.They are mainly oriented to global climate change and large-scale drought early-warning demand, and cannot meet the demand of agricultural water management and especially refinement.As aerospace industry develops rapidly in China, ground observation ability of satellite based on medium and high resolution ratio keeps improving. Hence, China owns the condition of forming ground observation with high time resolution through satellite networking with 30mresolution ratio or above. Thus, soil moisture content monitoring which is based on satellite remote sensing and independent satellite data source and oriented to agricultural water management becomes possible. Soil moisture content data required by agricultural water management has different spatial and temporal scale with data product of existing land data assimilation system. Soil moisture content data assimilation s oriented to this demand also has many technical difficulties which need to solving, mainly including the following:1. on the spatial and temporal scale required by agricultural water management, the influence of human activities cannot be ignored. The application of land surface process model is restricted, so it is necessary to solve reasonable application of land surface process model; 2. since spatial resolution improves, monocrystal scope of satellite data decreases, and it is necessary to depend on multiple data source combinations to solve space-time consistency problem of data in the scope of large area; 3. time scale is relatively broadened. There are various kinds of observation data sources within a time step. Hence, multiple data source fusion technology needs to be studied to improve consistency and overall observation accuracy; 4. instant observation acquired by remote sensing need to be transformed into the time scale required by agricultural water management; 5. soil moisture content data assimilation with high resolution within the scope of large area will be faced with the problem that the scope of observation field and simulation field is not consistent within the same time frame. Thus, it is necessary to research data assimilation technology of difference scope. In conclusion, it is necessary to settle the demands and problems, establish the formwork of soil moisture content data statistics technology oriented to agricultural water management and research solutions to various problems.For the above problems, the following researches were carried out in this paper:1. We summarized the problems on the basis of domestic and overseas research status about soil moisture content acquisition in agricultural water management and soil moisture content data assimilation; 2. Starting from the demand of agricultural water management, we analyzed and selected appropriate spatial and temporal scale for soil moisture content data assimilation. Meanwhile, we set up the overall framework of soil moisture content assimilation based on the agricultural water management, formulated different schemes on capture of soil moisture content for irrigation period and non-irrigation period respectively, guaranteed the capture of temporally and spatially continuous soil moisture content during the irrigation period by establishing the fusion method of spatial interpolation of multisource remote-sensing soil moisture content based on crop types and daytime multisource data, and established the scheme on soil moisture content assimilation based on the scale of land parcel during the non-irrigation period; 3. We studied the conversion method for temporal and spatial scale of soil moisture content. On the time scale, we developed the method for temporal and spatial scale of multisource soil moisture content based on the time-spectrum features and realized the transfer from the instant soil moisture content by remote sensing inversion to daily mean value. On the space scale, we converted the research unit into the scale of land parcel. Using the land parcels with unified crops and soil attributes as the research units more complies with the characteristics of agricultural water areas, avoids the problems such as decomposition of mixed pixels and excessive dependence on high-resolution data caused by the research units of pixels, and doesn’t involve compromises in the spatial scale and simulating precision like the grid simulation. When the application needs are met, the production efficiency of products can also be improved at a certain degree. Meanwhile, we put forward the land parcel encoding scheme based on agricultural water and this laid a foundation for further introducing the agricultural water management system in the land surface process model.4. In view of the problem of limited model application during the irrigation period, we proposed the daytime fusion method of spatial interpolation of multisource remote-sensing soil moisture content based on crop types and multisource data to guarantee the monitoring of temporally and spatial continuous soil moisture content during the irrigation period.5. As for the non-irrigation period, we used the Noah land surface process model to set up the EnKF-based soil moisture content assimilation scheme, studied the parameter calibration of assimilation algorithm, studied the influences of relevant parameters on the assimilation result, and realized the application of algorithm.6. We used Hetao Irrigation District as the research region, carried out method verification and application study based on multi-satellite remote sensing data, ground observation data and model simulation, conducted soil moisture content data assimilation, and formed soil moisture content data on day-land parcel scale in the research area. Besides, according to the data, we analyzed water demand of crops before irrigation, monitored irrigation area after irrigation, analyzed and verified the final results.We made the conclusions below through the study in this paper.1. Day-land parcel scale is the temporal and spatial scale suitable for agricultural water management. Such soil moisture content can satisfy temporal and spatial precision requirement of agricultural water management for data, effectively avoid mixed pixel problem and reduces calculation load. So, it has agricultural water feature.2. The interpolation and fusion of multi-satellite data source can effectively boost temporal-spatial resolution of soil moisture content monitored by satellite remote sensing, expand monitoring scope and improve multi-source data consistency. Based on satellite data source with independent medium and high resolution ratio, covered observation of high temporal-spatial resolution of agricultural production can be achieved by application of interpolation and fusion technology of multi-source satellite data.3. In view of the influence of human activities, different data solutions are established for irrigation period and non-irrigation period. In the irrigation period, remote sensing observation is fully utilized for interpolation and fusion so as to make up for defects of the model. In the non-irrigation period, model drive is utilized for assimilation to gain temporally and spatially continuous data, continuously update error distribution features, improve assimilation effect, furthest synthesize respective advantages of observation and simulation and offer soil moisture content data service for agricultural water management. Data assimilation technology under the condition in which some regions lack monitoring is developed for agricultural water management within the scope of large area. The statistical approach is applied to expand error statistics features, which improves use efficiency of observation data and consistency of regional data.5. The technical framework of soil moisture content data assimilation based on agricultural water management and multiple techniques included were applied in Hetao Irrigation District, Inner Mongolia. The results show that the technical framework is reasonable and feasible. By comprehensive application of various techniques and methods in the framework, temporally and spatially continuous soil moisture content data on day-land parcel scale can be acquired to offer quantitative data for estimating water demand of crops before irrigation, monitoring irrigation area in the irrigation process and after irrigation, estimating irrigation amount and estimating irrigation effect, and to provide support for formulating, implementing, supervising, adjusting and evaluating the irrigation plan. Hence, temporally and spatially continuous soil moisture content data on day-land parcel scale are of vital function for improving agricultural water management level. |
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