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Remotely sensed data are dominated by mixed Land Use and Land Cover (LULC)types. Spectral unmixing (SU) is a key technique that disentangles mixed pixelsinto constituent LULC types and their abundance fractions. While existingstudies on Deep Learning (DL) for SU typically focus on single time-stephyperspectral (HS) or multispectral (MS) data, our work pioneers SU using MODISMS time series, addressing missing data with end-to-end DL models. Our approachenhances a Long-Short Term Memory (LSTM)-based model by incorporatinggeographic, 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 LULCabundances through supervised learning. Experimental results demonstrate thatintegrating spectral-temporal input data with geo-topographic and climaticinformation significantly improves the estimation of LULC abundances in mixedpixels. To facilitate this study, we curated a novel labeled dataset forAndalusia (Spain) with monthly MODIS multispectral time series at 460mresolution for 2013. Named Andalusia MultiSpectral MultiTemporal Unmixing(Andalusia-MSMTU), this dataset provides pixel-level annotations of LULCabundances along with ancillary information. The dataset(https://zenodo.org/records/7752348) and code(https://github.com/jrodriguezortega/MSMTU) are available to the public.

Bidirectional recurrent imputation and abundance estimation of LULC  classes with MODIS multispectral time series and geo-topographic and climatic  data