Döpper Veronika, Rocha Alby Duarte, Berger Katja, Gränzig Tobias, Verrelst Jochem, Kleinschmit Birgit, Förster Michael
Geoinformation in Environmental Planning Lab, Technische Universität Berlin (TUB), Berlin, Germany.
Department of Geography, Ludwig-Maximilians-Universität München (LMU), Munich, Munich, Germany.
Int J Appl Earth Obs Geoinf. 2022 May 18;110:102817. doi: 10.1016/j.jag.2022.102817. eCollection 2022 Jun.
The monitoring of soil moisture content (SMC) at very high spatial resolution (<10m) using unmanned aerial systems (UAS) is of high interest for precision agriculture and the validation of large scale SMC products. Data-driven approaches are the most common method to retrieve SMC with UAS-borne data at water limited sites over non-disturbed agricultural crops. A major disadvantage of data-driven algorithms is the limited transferability in space and time and the need of a high number of ground reference samples. Physically-based approaches are less dependent on the amount of samples and are transferable in space and time. This study explores the potential of (1) a hybrid method targeting the soil brightness factor of the PROSAIL model using a variational heteroscedastic Gaussian Processes regression (VHGPR) algorithm, and (2) a data-driven method employing VHGPR for the retrieval of SMC over three grassland sites based on UAS-borne VIS-NIR (399-1001 nm) hyperspectral data. The sites were managed by mowing (Fendt), grazing (Grosses Bruch) and irrigation (Marquardt). With these distinct local pre-conditions we aimed to identify factors that favor and limit the retrieval of SMC. The hybrid approach presented encouraging results in Marquardt (RMSE = 1.5 Vol_%, R = 0.2). At the permanent grassland sites (Fendt, Grosses Bruch) the thatch layer jeopardized the application of the hybrid model. We identified the complex canopy structure of grassland as the main factor impacting the hybrid SMC retrieval. The data-driven approach showed high accuracy for Fendt (R = 0.84, RMSE = 8.66) and Marquardt (R = 0.4, RMSE = 10.52). All data-driven models build on the LAI-SMC relationship. However, this relationship was hampered by mowing (Fendt), leading to a lack of transferability in time. The alteration of plant traits by grazing prevents finding a relationship with SMC in Grosses Bruch. In Marquardt, we identified the timelag between changes in SMC and plant response as the main reason of decrease in model accuracy. Yet, the model performance is accurate in undisturbed and water-limited areas (Marquardt). The analysis points to challenges that need to be tackled in future research and opens the discussion for the development of robust models to retrieve high resolution SMC from UAS-borne remote sensing observations.
利用无人机系统（UAS）以非常高的空间分辨率（<10米）监测土壤湿度含量（SMC），对于精准农业和大规模SMC产品的验证具有重要意义。在水资源有限地区的未受干扰农作物上，数据驱动方法是利用无人机搭载数据反演SMC最常用的方法。数据驱动算法的一个主要缺点是在空间和时间上的可转移性有限，并且需要大量的地面参考样本。基于物理的方法对样本数量的依赖性较小，并且在空间和时间上具有可转移性。本研究探讨了以下两种方法的潜力：（1）一种混合方法，使用变分异方差高斯过程回归（VHGPR）算法针对PROSAIL模型的土壤亮度因子；（2）一种数据驱动方法，基于无人机搭载的可见-近红外（399-1001纳米）高光谱数据，利用VHGPR反演三个草地站点的SMC。这些站点分别采用割草（芬特）、放牧（格罗斯布鲁赫）和灌溉（马夸特）的管理方式。基于这些不同的当地前提条件，我们旨在确定有利于和限制SMC反演的因素。混合方法在马夸特取得了令人鼓舞的结果（均方根误差RMSE = 1.5体积%，相关系数R = 0.2）。在永久性草地站点（芬特、格罗斯布鲁赫），草垫层危及了混合模型的应用。我们确定草地复杂的冠层结构是影响混合SMC反演的主要因素。数据驱动方法在芬特（R = 0.84，RMSE = 8.66）和马夸特（R = 0.4，RMSE = 10.52）表现出高精度。所有数据驱动模型都基于叶面积指数-土壤湿度含量（LAI-SMC）关系。然而，这种关系在芬特受到割草的影响，导致在时间上缺乏可转移性。放牧导致植物性状改变，使得在格罗斯布鲁赫无法找到与SMC的关系。在马夸特，我们确定SMC变化与植物响应之间的时间滞后是模型精度下降的主要原因。然而，该模型在未受干扰和水资源有限的地区（马夸特）表现准确。分析指出了未来研究中需要解决的挑战，并开启了关于开发强大模型以从无人机搭载的遥感观测中反演高分辨率SMC的讨论。
