利用机器学习技术基于Sentinel-1 SAR数据估算温带森林的光学植被指数和生物物理变量：以捷克为例
Estimating optical vegetation indices and biophysical variables for temperate forests with Sentinel-1 SAR data using machine learning techniques: A case study for Czechia
AI总结：
本研究利用机器学习结合Sentinel-1 SAR数据估算捷克温带森林的四种光学植被指数，克服云层限制，实现高精度（R² 70-86%）和亚周突变检测。
摘要：
当前用于监测森林生态系统的光学植被指数（VIs）已得到充分确立并广泛应用于各种场景，但可能受到云层等大气效应的限制。相比之下，合成孔径雷达（SAR）数据凭借信号穿透云层以及昼夜成像能力，能够提供完整时间序列（TS）的深入且系统的森林监测。本研究旨在通过机器学习（ML）利用SAR数据作为估算森林光学植被指数的替代方案，以解决光学卫星数据的局限性。虽然这种方法较为间接，且可能仅通过ML的力量才能实现，但它提出了一个科学问题：SAR信号中是否包含足够的相关信息来准确估算植被指数。本研究涵盖了利用多时相Sentinel-1 SAR及辅助数据估算四种植被指数（LAI、FAPAR、EVI和NDVI）的时间序列。研究聚焦于2021年捷克境内健康及受干扰的温带森林区域，地面真值标签由Sentinel-2多光谱数据生成。通过在Google Earth Engine（GEE）中创建配对的多模态时间序列数据集，包括时间和空间对齐的Sentinel-1、Sentinel-2、DEM、气象和土地覆盖数据集，实现了上述目标。引入基于DEM的辅助特征和额外的气象信息进一步改善了结果。在机器学习模型比较中，传统机器学习算法RFR和XGBoost在所有植被指数上均略优于AutoML方法auto-sklearn，取得了高精度（$R^2$在70%-86%之间）和低误差（MAE为0.055-0.29）。总体而言，基于SAR估算的植被指数可实现每年多达240次测量和20米的空间分辨率，且具有高精度。基于SAR的植被指数的一大优势在于能够以亚周时间精度检测森林的突变。
英文摘要：
Current optical vegetation indices (VIs) for monitoring forest ecosystems are well established and widely used in various applications, but can be limited by atmospheric effects such as clouds. In contrast, synthetic aperture radar (SAR) data can offer insightful and systematic forest monitoring with complete time series (TS) due to signal penetration through clouds and day and night image acquisitions. This study aims to address the limitations of optical satellite data by using SAR data as an alternative for estimating optical VIs for forests through machine learning (ML). While this approach is less direct and likely only feasible through the power of ML, it raises the scientific question of whether enough relevant information is contained in the SAR signal to accurately estimate VIs. This work covers the estimation of TS of four VIs (LAI, FAPAR, EVI and NDVI) using multitemporal Sentinel-1 SAR and ancillary data. The study focused on both healthy and disturbed temperate forest areas in Czechia for the year 2021, while ground truth labels generated from Sentinel-2 multispectral data. This was enabled by creating a paired multi-modal TS dataset in Google Earth Engine (GEE), including temporally and spatially aligned Sentinel-1, Sentinel-2, DEM, weather and land cover datasets. The inclusion of DEM-derived auxiliary features and additional meteorological information, further improved the results. In the comparison of ML models, the traditional ML algorithms, RFR and XGBoost slightly outperformed the AutoML approach, auto-sklearn, for all VIs, achieving high accuracies ($R^2$ between 70-86%) and low errors (0.055-0.29 of MAE). In general, up to 240 measurements per year and a spatial resolution of 20 m can be achieved using estimated SAR-based VIs with high accuracy. A great advantage of the SAR-based VI is the ability to detect abrupt forest changes with sub-weekly temporal accuracy.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
