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The article presents results of using remote sensing images and machine learning to map and assess land potential based on time-series of potential Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) composites. Land potential here refers to the potential vegetation productivity in the hypothetical absence of short-term anthropogenic influence, such as intensive agriculture and urbanization. Knowledge on this ecological land potential could support the assessment of levels of land degradation as well as restoration potentials. Monthly aggregated FAPAR time-series of three percentiles (0.05, 0.50 and 0.95 probability) at 250 m spatial resolution were derived from the 8-day GLASS FAPAR V6 product for 2000-2021 and used to determine long-term trends in FAPAR, as well as to model potential FAPAR in the absence of human pressure. CCa 3 million training points sampled from 12,500 locations across the globe were overlaid with 68 bio-physical variables representing climate, terrain, landform, and vegetation cover, as well as several variables representing human pressure including: population count, cropland intensity, nightlights and a human footprint index. The training points were used in an ensemble machine learning model that stacks three base learners (extremely randomized trees, gradient descended trees and artificial neural network) using a linear regressor as meta-learner. The potential FAPAR was then projected by removing the impact of urbanization and intensive agriculture in the covariate layers. The results of strict cross-validation show that the global distribution of FAPAR can be explained with an R of 0.89, with the most important covariates being growing season length, forest cover indicator and annual precipitation. From this model, a global map of potential monthly FAPAR for the recent year (2021) was produced, and used to predict gaps in actual . potential FAPAR. The produced global maps of actual . potential FAPAR and long-term trends were each spatially matched with stable and transitional land cover classes. The assessment showed large negative FAPAR gaps (actual lower than potential) for classes: urban, needle-leave deciduous trees, and flooded shrub or herbaceous cover, while strong negative FAPAR trends were found for classes: urban, sparse vegetation and rainfed cropland. On the other hand, classes: irrigated or post-flooded cropland, tree cover mixed leaf type, and broad-leave deciduous showed largely positive trends. The framework allows land managers to assess potential land degradation from two aspects: as an actual declining trend in observed FAPAR and as a difference between actual and potential vegetation FAPAR.

本文提出了一种利用遥感图像和机器学习来绘制和评估土地潜力的方法，该方法基于潜在吸收的光合有效辐射（FAPAR）时间序列的复合数据。这里的土地潜力是指在假设不存在短期人为影响（如集约农业和城市化）的情况下，植被的潜在生产力。对这种生态土地潜力的了解可以支持对土地退化程度以及恢复潜力的评估。

从 2000 年至 2021 年，从 8 天 GLASS FAPAR V6 产品中提取了 250 米空间分辨率的三个百分位数（0.05、0.50 和 0.95 概率）的每月聚合 FAPAR 时间序列，并用于确定 FAPAR 的长期趋势，以及在没有人为压力的情况下模拟潜在的 FAPAR。从全球 12500 个地点采集了 300 万个训练点，并与代表气候、地形、地貌和植被覆盖的 68 个生物物理变量以及代表人口数量、耕地强度、夜间灯光和人类足迹指数等人为压力的几个变量进行了叠加。这些训练点被用于一个集成机器学习模型中，该模型使用线性回归器作为元学习者，堆叠了三个基础学习者（极端随机树、梯度下降树和人工神经网络）。然后通过去除协变量层中城市化和集约农业的影响来预测潜在 FAPAR。严格的交叉验证结果表明，FAPAR 的全球分布可以用 R2 值为 0.89 来解释，最重要的协变量是生长季节长度、森林覆盖指标和年降水量。根据该模型，生成了最近一年（2021 年）潜在月度 FAPAR 的全球地图，并用于预测实际 FAPAR 的差距。生成的实际 FAPAR 和长期趋势的全球地图分别与稳定和过渡土地覆盖类别进行了空间匹配。评估结果表明，在城市、针叶落叶树和淹没灌木或草本植被覆盖等类别中，FAPAR 差距较大（实际值低于潜在值），而在城市、稀疏植被和雨养耕地等类别中，FAPAR 趋势呈负值。另一方面，灌溉或洪水后耕地、混合叶型树木覆盖和阔叶落叶树等类别表现出明显的正趋势。该框架允许土地管理者从两个方面评估潜在的土地退化：作为观测到的 FAPAR 实际下降趋势，以及作为实际和潜在植被 FAPAR 之间的差异。