Optical radiative transfer models (RTM) are used to study the relationship between vegetation biophysical properties and corresponding canopy reflectances. In this paper, a RTM is inverted with satellite based surface reflectance observations to estimate key vegetation biophysical properties such as leaf area index (LAI) and leaf chlorophyll content (Cab). The complexity of model inversion makes model optimization challenging, particularly in dryland areas where the vegetation signal may have become confounded by bright soil backgrounds. Add to this, general difficulties in separating vegetation properties contributing to the combined surface reflectance signal. In this study, using a Bayesian approach, the inversion approach is written as a cost function to minimize. The high non-linearity of the RTM makes the analytical resolution of the optimization unpractical. To overcome this problem, a new multi-scale variational inversion approach is proposed. It solves progressively the inversion problem by first simplifying it, then solving it, and then coming back progressively to the original inversion problem. The approach is tested over a dryland irrigated agricultural system composed of fields of alfalfa, Rhodes grass, carrots and maize. Validation is done comparing results to in-situ measurements and other commonly used retrieval methods. The retrieved properties are shown to be in good agreement with in-situ observations of Cab (RMSE = 0.2 μg cm⁻² and R2 = 89%) and LAI (RMSE = 0.18 m² m⁻² and R2 = 92%), which is shown to be an improvement over other traditional variational techniques (Gradient, Newton, LUT and QNT).

光学辐射传递模型（RTM）用于研究植被生物物理特性与相应冠层反射率之间的关系。在本文中，将RTM与基于卫星的表面反射率观测值进行倒置，以估计关键的植被生物物理特性，例如叶面积指数（LAI）和叶绿素含量（Cab）。模型反演的复杂性使模型优化面临挑战，尤其是在干旱地区，植被信号可能已因明亮的土壤背景而变得混乱。除此之外，在分离植被特性时通常会遇到困难，这会增加组合的表面反射率信号。在这项研究中，使用贝叶斯方法，将反演方法写为成本函数以使其最小化。RTM的高非线性使得优化的分析分辨率不切实际。为了克服这个问题，提出了一种新的多尺度变分反演方法。它通过首先简化反演问题，然后解决它，然后逐步回到原始反演问题，逐步解决反演问题。该方法在由苜蓿，罗得草，胡萝卜和玉米田组成的旱地灌溉农业系统上进行了测试。通过将结果与原位测量结果和其他常用的检索方法进行比较来进行验证。结果表明，所获得的特性与驾驶室的原位观察结果非常吻合（RMSE = 0.2μgcm 它通过首先简化反演问题，然后解决它，然后逐步回到原始反演问题，逐步解决反演问题。该方法在由苜蓿，罗得草，胡萝卜和玉米田组成的旱地灌溉农业系统上进行了测试。通过将结果与原位测量结果和其他常用的检索方法进行比较来进行验证。结果表明，所获得的特性与驾驶室的原位观察结果非常吻合（RMSE = 0.2μgcm 它通过首先简化反演问题，然后解决它，然后逐步回到原始反演问题，逐步解决反演问题。该方法在由苜蓿，罗得草，胡萝卜和玉米田组成的旱地灌溉农业系统上进行了测试。通过将结果与原位测量结果和其他常用的检索方法进行比较来进行验证。结果表明，所获得的特性与驾驶室的原位观察结果非常吻合（RMSE = 0.2μgcm⁻²和R2 = 89％）和LAI（RMSE = 0.18 m ²  m ^-2，R2 = 92％），这是对其他传统变分技术（梯度，牛顿，LUT和QNT）的改进。