Remotely sensed data are dominated by mixed Land Use and Land Cover (LULC) types. Spectral unmixing is a technique to extract information from mixed pixels into their constituent LULC types and corresponding abundance fractions. Traditionally, solving this task has relied on either classical methods that require prior knowledge of endmembers or machine learning methods that avoid explicit endmembers calculation, also known as blind spectral unmixing (BSU). Most BSU studies based on Deep Learning (DL) focus on one time-step hyperspectral or multispectral data. To our knowledge, here we provide the first study on BSU of LULC classes using MODIS multispectral time series, in presence of missing data, with end-to-end DL models. We further boost the performance of a Long-Short Term Memory (LSTM)-based model by incorporating geographic plus topographic (geo-topographic) and climatic ancillary information. Our experiments show that combining spectral-temporal input data together with geo-topographic and climatic information substantially improves the abundance estimation of LULC classes in mixed pixels. To carry out this study, we built a new labeled dataset of the region of Andalusia (Spain) with monthly multispectral time series of pixels for the year 2013 from MODIS at 460m resolution, for two hierarchical levels of LULC classes, named Andalusia MultiSpectral MultiTemporal Unmixing (Andalusia-MSMTU). This dataset provides, at the pixel level, a multispectral time series plus ancillary information annotated with the abundance of each LULC class inside each pixel. The dataset (https://zenodo.org/record/7752348##.ZBmkkezMLdo) and code (https://github.com/jrodriguezortega/MSMTU) are available to the public.

翻译：遥感数据通常包含混合的土地利用/土地覆盖（LULC）类型。光谱解混技术可从混合像元中提取信息，将其分解为各LULC构成类型及其对应的丰度分数。传统方法需要先验知识的端元提取或依赖机器学习方法避免显式端元计算，后者称为盲光谱解混（BSU）。现有基于深度学习（DL）的BSU研究多聚焦于单时相高光谱或多光谱数据。据我们所知，本文首次提出利用端到端DL模型，在存在数据缺失的情况下，基于MODIS多光谱时间序列进行LULC类别的BSU研究。我们通过融合地理与地形（地理-地形）及气候辅助信息，进一步提升了基于长短期记忆（LSTM）模型的性能。实验表明，结合光谱-时序输入数据与地理-地形及气候信息，可显著改善混合像元中LULC类别的丰度估计。本研究构建了安达卢西亚（西班牙）地区的新标注数据集，包含2013年MODIS 460米分辨率月度多光谱时间序列像元，涵盖两个层级LULC类别体系，命名为安达卢西亚多光谱多时相解混数据集（Andalusia-MSMTU）。该数据集在像元层级提供带有各LULC类别丰度标注的多光谱时间序列及辅助信息。数据集（https://zenodo.org/record/7752348##.ZBmkkezMLdo）与代码（https://github.com/jrodriguezortega/MSMTU）均已公开。