地表温度与植被盖度的两阶段特征空间模式及其参数反演方法研究

Space of land surface temperature and fractional vegetation cover (LST-Fv) has important application prospects in partitioning evapotranspiration and monitoring deep soil moisture based on remote sensing data. However, traditional conceptual model of the triangle and trapezoid LST-Fv space do not take into account the variation degree difference and variation speed difference between vegetation and soil radiation temperatures when soil dries out. Moreover, they cannot interpret the evolution process of LST-Fv space from triangle to trapezoid and would introduce higher errors in partitioning evapotranspiration and monitoring deep soil moisture. In order to make up for the above deficiencies, a new conceptual model of LST-Fv space is suggested and developed in this study named two-stage LST-Fv space which is a trapezoid consisted of two triangles. The main research contents include: (1) clarifying the uncertainties and application scope of the two-stage LST-Fv space; (2) developing the method of determining theoretical boundary of the two-stage LST-Fv space; (3) modeling and partitioning evapotranspiration using the two-stage LST-Fv space; (4) constructing a model for monitoring surface and deep soil moisture based on the two-stage LST-Fv space. We believe this work is likely to improve the accuracy in modeling and partitioning evapotranspiration as well as monitoring deep soil moisture utilizing LST-Fv space and finally provide more accurate knowledge about evapotranspiration and soil moisture for the monitoring of agricultural drought, plant growth and productivity, as well as for irrigation and cultivation management procedures.

地表温度与植被盖度（LST-Fv）特征空间在基于遥感技术的地表蒸散估算和分离以及深层土壤湿度监测方面具有广阔的应用前景。然而，传统的三角形和梯形特征空间模式没有兼顾土壤湿度驱动下，植被与裸土辐射温度存在的响应速度和响应程度差异，难以解释LST-Fv特征空间从三角形到梯形的演变过程，为地表蒸散的监测引入了不必要的误差，并难以界定表层和根层土壤湿度变异。据此，本课题拟发展一种LST-Fv的两阶段特征空间模式，其中两阶段特征空间是由上下两个三角形耦合而成的一种梯形空间。研究内容包括：阐明两阶段特征空间模式的不确定性和适用范围，发展其“干边/湿边”确定方法，构建基于两阶段特征空间模式的地表蒸散估算及其分离、以及表层和根层土壤湿度监测模型。本课题有望提高LST-Fv特征空间在地表蒸散估算、分离和深层土壤湿度监测上的应用效果，为农业干旱监测、农作物产量预测等，提供更为准确细致的地表蒸散和土壤湿度信息。

地表温度与植被盖度（LST/FVC）特征空间在基于遥感技术的地表蒸散估算和分离以及深层.土壤湿度监测方面具有广阔的应用前景。然而，传统的三角形和梯形特征空间模式没有兼顾土壤湿度驱动下，植被与裸土辐射温度存在的响应速度和响应程度差异，难以解释LST/FVC特征空间从三角形到梯形的演变过程，为地表蒸散的监测引入了不必要的误差，并难以界定表层和根层土壤湿度变异。据此，本课题提出LST/FVC的两阶段特征空间模式及其参数反演方法。研究内容包括：阐明两阶段特征空间模式的不确定性和适用范围，发展其“干边/湿边”确定方法，构建基于两阶段特征空间模式的地表蒸散估算及其分离等。本课题取得的主要成果包括：提出地表温度与植被盖度两阶段特征空间概念模式，构建基于两阶段特征空间的地表蒸散估算及其分离模型，发展两阶段特征空间“干湿边”的理论计算方法等。课题研究成果进一步应用到城市热环境以及农业干旱监测，设计了基于LST/FVC空间的近地表气温算估算法，对比分析了北京城市冠层热岛和地表热岛的差异，阐明了绿度类和水分类光谱植被指数对农业干旱响应差异的原因。研究成果提高了LST/FVC特征空间在地表蒸散估算、分离和深层土壤湿度监测上的应用效果，有潜力为农业干旱监测、农作物产量预测等，提供更为准确细致的地表蒸散和土壤湿度信息。.

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