Current optical vegetation indices (VIs) for monitoring forest ecosystems are widely used in various applications. However, continuous monitoring based on optical satellite data can be hampered by atmospheric effects such as clouds. On the contrary, synthetic aperture radar (SAR) data can offer insightful and systematic forest monitoring with complete time series due to signal penetration through clouds and day and night acquisitions. The goal of this work is to overcome the issues affecting optical data with SAR data and serve as a substitute for estimating optical VIs for forests using machine learning. Time series of four VIs (LAI, FAPAR, EVI and NDVI) were estimated using multitemporal Sentinel-1 SAR and ancillary data. This was enabled by creating a paired multi-temporal and multi-modal dataset in Google Earth Engine (GEE), including temporally and spatially aligned Sentinel-1, Sentinel-2, digital elevation model (DEM), weather and land cover datasets (MMT-GEE). The use of ancillary features generated from DEM and weather data improved the results. The open-source Automatic Machine Learning (AutoML) approach, auto-sklearn, outperformed Random Forest Regression for three out of four VIs, while a 1-hour optimization length was enough to achieve sufficient results with an R2 of 69-84% low errors (0.05-0.32 of MAE depending on VI). Great agreement was also found for selected case studies in the time series analysis and in the spatial comparison between the original and estimated SAR-based VIs. In general, compared to VIs from currently freely available optical satellite data and available global VI products, a better temporal resolution (up to 240 measurements/year) and a better spatial resolution (20 m) were achieved using estimated SAR-based VIs. A great advantage of the SAR-based VI is the ability to detect abrupt forest changes with a sub-weekly temporal accuracy.

翻译：当前用于监测森林生态系统的光学植被指数（VIs）广泛应用于各类场景。然而，基于光学卫星数据的连续监测常受大气效应（如云层）影响。相比之下，合成孔径雷达（SAR）数据凭借信号穿透云层及全天时成像能力，可提供具有完整时序的洞察性森林系统监测。本研究旨在利用SAR数据克服光学数据的局限性，并作为机器学习估算森林光学VIs的替代方案。通过多时相Sentinel-1 SAR数据与辅助数据，估算出四种VIs（LAI、FAPAR、EVI和NDVI）的时间序列。这得益于在Google Earth Engine（GEE）中构建配对的多时相多模态数据集MMT-GEE，包含时间与空间对齐的Sentinel-1、Sentinel-2、数字高程模型（DEM）、气象及土地覆盖数据。基于DEM与气象数据生成的辅助特征显著提升了结果。开源自动机器学习（AutoML）方法auto-sklearn在四种VIs中的三种上优于随机森林回归，且仅需1小时优化即可获得足够效果（R²达69-84%，MAE依VI类型在0.05-0.32区间）。时间序列分析与空间对比案例研究显示，原始与估算的SAR-VIs具有高度一致性。总体而言，相较于当前免费获取的光学卫星数据及全球VI产品，基于SAR估算的VIs实现了更优的时间分辨率（每年最高240次观测）和空间分辨率（20米）。SAR-VI的一大优势在于能以亚周级时间精度探测森林突变。