Earth observation (EO) satellite missions have been providing detailed images
about the state of the Earth and its land cover for over 50 years. Long term
missions, such as NASA's Landsat, Terra, and Aqua satellites, and more
recently, the ESA's Sentinel missions, record images of the entire world every
few days. Although single images provide point-in-time data, repeated images of
the same area, or satellite image time series (SITS) provide information about
the changing state of vegetation and land use. These SITS are useful for
modeling dynamic processes and seasonal changes such as plant phenology. They
have potential benefits for many aspects of land and natural resource
management, including applications in agricultural, forest, water, and disaster
management, urban planning, and mining. However, the resulting satellite image
time series (SITS) are complex, incorporating information from the temporal,
spatial, and spectral dimensions. Therefore, deep learning methods are often
deployed as they can analyze these complex relationships. This review presents
a summary of the state-of-the-art methods of modelling environmental,
agricultural, and other Earth observation variables from SITS data using deep
learning methods. We aim to provide a resource for remote sensing experts
interested in using deep learning techniques to enhance Earth observation
models with temporal information.

利用深度学习方法对卫星图像时间序列数据进行环境、农业和其他地球观测变量的建模的最新方法进行了综述，为遥感专家提供了一个利用时间信息增强地球观测模型的资源。

卫星图像时间序列分析的深度学习：一综述

Deep Learning for Satellite Image Time Series Analysis: A Review

Changes in climate can greatly affect the phenology of plants, which can have
important feedback effects, such as altering the carbon cycle. These
phenological feedback effects are often induced by a shift in the start or end
dates of the growing season of plants. The normalized difference vegetation
index (NDVI) serves as a straightforward indicator for assessing the presence
of green vegetation and can also provide an estimation of the plants' growing
season. In this study, we investigated the effect of soil temperature on the
timing of the start of the season (SOS), timing of the peak of the season
(POS), and the maximum annual NDVI value (PEAK) in subarctic grassland
ecosystems between 2014 and 2019. We also explored the impact of other
meteorological variables, including air temperature, precipitation, and
irradiance, on the inter-annual variation in vegetation phenology. Using
machine learning (ML) techniques and SHapley Additive exPlanations (SHAP)
values, we analyzed the relative importance and contribution of each variable
to the phenological predictions. Our results reveal a significant relationship
between soil temperature and SOS and POS, indicating that higher soil
temperatures lead to an earlier start and peak of the growing season. However,
the Peak NDVI values showed just a slight increase with higher soil
temperatures. The analysis of other meteorological variables demonstrated their
impacts on the inter-annual variation of the vegetation phenology. Ultimately,
this study contributes to our knowledge of the relationships between soil
temperature, meteorological variables, and vegetation phenology, providing
valuable insights for predicting vegetation phenology characteristics and
managing subarctic grasslands in the face of climate change. Additionally, this
work provides a solid foundation for future ML-based vegetation phenology
studies.

研究发现，气候变化对植物的表型学有重要影响，尤其通过改变碳循环。本研究使用机器学习技术和 SHapley 加性解释（SHAP）值分析了土壤温度、气象变量与植被表型学的关系，结果表明土壤温度与季节开始和高峰有显著关联，而气象变量也对植被表型学的年际变化产生影响，为预测和管理亚北极草地的植被表型学特征提供了宝贵的洞察和基础。