| 引用本文: | 张晓春,刘海若,严忆辉,等.基于物候特征的农田土壤表层含水率遥感反演[J].灌溉排水学报,2021,(10):1-9. |
| ZHANG Xiaochun,LIU Hairuo,YAN Yihui,et al.基于物候特征的农田土壤表层含水率遥感反演[J].灌溉排水学报,2021,(10):1-9. |
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| 摘要: |
| 【目的】土壤水分是作物生长的必要条件，反演土壤含水率对监测土壤墒情、减少作物受渍害程度、提高作物产量具有重要意义。【方法】建立了一种结合作物物候特征的农田土壤表层含水率遥感反演方法。首先基于预处理后的Sentinel-1 SAR影像获得入射角数据和VV极化条件下的总后向散射系数，并从环境卫星影像提取相应日期的作物植被覆盖指数NDVI（Normalized Difference Vegetation Index）。然后基于入射角、后向散射系数和NDVI，通过水云模型得到去除植被影响后的后向散射系数。结合研究区内作物物候特征，分生育阶段建立后向散射系数与土壤相对体积含水率之间的关系，并建立适用于研究区域作物物候特征的反演模型。【结果】根据2种轮作作物冬小麦和夏玉米物候特征分为4个时间段函数，分别为冬小麦的播种—分蘖期、越冬期、返青期—成熟期，和夏玉米的全生育期。分生育期阶段选取部分实测点土壤含水率数据进行计算，得到各时间段的土壤含水率与后向散射系数关系的经验方程，其线性相关系数分别达到0.40、0.80、0.91和0.79。通过提取固镇县与利辛县的作物种植结构，结合经验方程反演得到研究区农田土壤表层含水率分布图。基于剩余部分实测土壤含水率数据分别对反演模型进行验证，结果显示冬小麦全生育期总的复相关系数为0.73，夏玉米全生育期总的复相关系数为0.82。【结论】通过结合物候特征分生育阶段建立函数，为农作物种植区域的土壤表层含水率反演提供一种快速准确的方法。 |
| 关键词: 土壤含水率；遥感反演；后向散射系数；物候特征；农作物区 |
| DOI：10.13522/j.cnki.ggps.2021195 |
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| Retrieval of Surface Soil Water Content Using Remote Sensing in Incorporation with Phenological Traits of Crops |
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ZHANG Xiaochun, LIU Hairuo, YAN Yihui, TANG Rong, ZHANG Yu
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1. State Key Laboratory of Water Resources and Hydropower Engineering Science, Wuhan University,
Wuhan 430072, China; 2. Spatial Information Technology Application Research Department,
Changjiang River Scientific Research Institute, Wuhan 430010, China
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| Abstract: |
| 【Objective】Soil water controls crop growth and many ecological functions of terrestrial ecosystems but is difficult to measure at large scales due to soil heterogeneity. The development in air-born technologies such as drones and satellites over the past decades provides a new avenue to rapidly monitor soil water. The objective of this paper is to propose how phenological traits of crops can be used to help improve accuracy of the retrieval of soil water content using remote sensing imageries. 【Method】The method was based on Sentinel-1 satellite imageries. We first calculated the in-come angles and the total backscattering coefficient; we then obtained the vertical transmit and vertical receive (VV) polarization, and extracted the normalized difference vegetation index (NDVI) of the crops. Using the in-come angles, backscattering coefficient and NDVI, we calculated the backscattering coefficient by removing the influence of vegetation using the water cloud model. Based on phenological traits of the crops, the relationship between the backscattering coefficient and the relative volumetric soil water content was established for crops at different growth stages. The inversion model was validated against experimental data measured from fields rotated with winter wheat and summer maize.【Result】Dividing growth season of the winter wheat into three stages:? sowing-tillering, overwintering, turning-green and mature, while taking growth season of the maize as a single stage, the correlation coefficient between measured and estimated soil water contents at the four stages above was 0.40, 0.80, 0.91 and 0.79 respectively. Applying the method with the traits of the crops incorporated to two fields in Guzhen and Lixin, we calculated soil water distribution maps in them. Verification of the model against experimental data showed that its correlation coefficient was 0.73 for entire growth season of the winter wheat and 0.82 for growing season of the summer maize.【Conclusion】The regression model with phenological traits of crops at different growth stages incorporated can improve accuracy of the soil water content estimated using remote sensing imageries. |
| Key words: soil moisture content; remote sensing inversion; backscattering coefficient; phenological traits |
