Remotely sensed data are dominated by mixed Land Use and Land Cover (LULC) types. Spectral unmixing (SU) is a key technique that disentangles mixed pixels into constituent LULC types and their abundance fractions. While existing studies on Deep Learning (DL) for SU typically focus on single time-step hyperspectral (HS) or multispectral (MS) data, our work pioneers SU using MODIS MS time series, addressing missing data with end-to-end DL models. Our approach enhances a Long-Short Term Memory (LSTM)-based model by incorporating geographic, topographic (geo-topographic), and climatic ancillary information. Notably, our method eliminates the need for explicit endmember extraction, instead learning the input-output relationship between mixed spectra and LULC abundances through supervised learning. Experimental results demonstrate that integrating spectral-temporal input data with geo-topographic and climatic information significantly improves the estimation of LULC abundances in mixed pixels. To facilitate this study, we curated a novel labeled dataset for Andalusia (Spain) with monthly MODIS multispectral time series at 460m resolution for 2013. Named Andalusia MultiSpectral MultiTemporal Unmixing (Andalusia-MSMTU), this dataset provides pixel-level annotations of LULC abundances along with ancillary information. The dataset (https://zenodo.org/records/7752348) and code (https://github.com/jrodriguezortega/MSMTU) are available to the public.

翻译：遥感数据以混合土地利用与土地覆盖（LULC）类型为主导。光谱解混（SU）是将混合像元分解为组成LULC类型及其丰度分数的关键技术。尽管现有基于深度学习（DL）的光谱解混研究通常聚焦于单时相高光谱（HS）或多光谱（MS）数据，本研究开创性地利用MODIS多光谱时间序列进行光谱解混，并采用端到端深度学习模型处理缺失数据。我们的方法通过纳入地理、地形（地理地形）及气候辅助信息，对长短期记忆（LSTM）模型进行了增强。值得注意的是，本方法无需显式提取端元，而是通过监督学习直接建立混合光谱与LULC丰度之间的输入-输出映射关系。实验结果表明，将光谱-时间输入数据与地理地形及气候信息相结合，显著提升了混合像元中LULC丰度的估计精度。为支持本研究，我们构建了一个针对西班牙安达卢西亚地区的新型标注数据集，包含2013年每月460米分辨率的MODIS多光谱时间序列。该数据集命名为安达卢西亚多光谱多时相解混数据集（Andalusia-MSMTU），提供像素级LULC丰度标注及辅助信息。数据集（https://zenodo.org/records/7752348）与代码（https://github.com/jrodriguezortega/MSMTU）均已公开。