Russian State Agrarian University - Moscow Timiryazev Academy, Moscow, Russian Federation.
Kirensky Institute of Physics of the Siberian Branch of the RAS - Division of Federal Research Center, Krasnoyarsk Scientific Center of the Siberian Branch of the RAS, Krasnoyarsk, Russian Federation, Siberian Federal University, Krasnoyarsk, Russian Federation.
Sci Total Environ. 2022 Feb 10;807(Pt 2):151121. doi: 10.1016/j.scitotenv.2021.151121. Epub 2021 Oct 21.
Soil surface moisture is one of the key parameters for describing the hydrological state and assessing the potential availability of water for irrigated plants. Because the radar backscattering coefficient is sensitive to soil moisture, the application of Sentinel-1 data may support soil surface moisture mapping at high spatial resolution by detecting spatial and temporal changes at the field scale for precision irrigation management. This mapping is required to control soil water erosion and preferential water flow to improve irrigation water efficiency and minimise negative impacts on surface and ground water bodies. Direct observations of soil surface moisture (5-cm thickness) were performed at an experimental plot in the study site of the All-Russian Scientific Research Institute of Irrigated Agriculture, near the village Vodnyy, Volgograd region. Soil surface moisture retrieval from Sentinel-1 was performed at the same location. A second set of soil surface moisture was calculated for the soil sampling sites using the permittivity model, based on the estimates of soil surface characteristics: a) reflectivity, obtained by the neural network method from Sentinel-1 observations; b) roughness, obtained from the geodata of the stereoscopic survey with unmanned aerial vehicle Phantom 4 Pro. The raster set of soil surface moisture geodata was obtained based on the reflectivity geodata raster set to solve the inverse problem using a permittivity model that considers the soil texture of the experimental plot. The determination coefficient (0.948) and standard deviation (2.04%) were obtained by comparing both sets of soil moisture point geodata taken from the same soil sampling sites. The values confirmed a satisfactory linear correlation between the directly measured and indirectly modelled sets. A comparison of the two sets of geodata indicated a satisfactory reproduction of the first set by the second set. As a result, the developed method can be considered as the scientific and methodological basis of the new technology of soil surface moisture monitoring by radar, which is one of the basic characteristics used in precision irrigation management.
土壤表面湿度是描述水文状态和评估灌溉植物潜在可用水量的关键参数之一。由于雷达后向散射系数对土壤湿度敏感,因此 Sentinel-1 数据的应用可以通过检测田间尺度的时空变化,支持高空间分辨率的土壤表面湿度制图,从而实现精确灌溉管理。这种制图对于控制土壤水蚀和优先水流是必要的,可以提高灌溉水效率,并最大限度地减少对地表水和地下水体的负面影响。在沃罗格达州沃迪尼村附近的全俄灌溉农业科学研究所研究点的实验田中,直接观测了土壤表面湿度(5 厘米厚)。在同一地点,从 Sentinel-1 中检索土壤表面湿度。使用基于土壤表面特征的估算值,从 Sentinel-1 观测值中获取的神经网络方法获得的反射率和从无人机立体测量获得的粗糙度,为土壤采样点计算了第二套土壤表面湿度。基于反射率栅格数据集,获得了土壤表面湿度栅格数据集,以使用考虑实验点土壤质地的介电模型来解决反问题。通过将来自同一土壤采样点的两组土壤湿度点地理数据进行比较,获得了决定系数(0.948)和标准偏差(2.04%)。该值确认了直接测量和间接建模两组土壤湿度之间令人满意的线性相关性。对两组地理数据的比较表明,第二组对第一组有很好的再现性。因此,所开发的方法可以被视为基于雷达的土壤表面湿度监测新技术的科学和方法学基础,这是精确灌溉管理中使用的基本特征之一。