# Probabilistic NDVI Forecasting from Sparse Satellite Time Series and Weather Covariates

arXiv: https://arxiv.org/abs/2602.17683 · HTML: https://mlsanity.com/paper/2602.17683/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 50 references that are in the map.
- First posted on arXiv 4 Feb 2026.

## Builds on (most cited first, 13 of 13)

- [A Time Series is Worth 64 Words: Long-term Forecasting with Transformers](https://mlsanity.com/paper/2211.14730/) (2022)
- [DeepAR: Probabilistic Forecasting with Autoregressive Recurrent Networks](https://mlsanity.com/paper/1704.04110/) (2017)
- [Time-LLM: Time Series Forecasting by Reprogramming Large Language Models](https://mlsanity.com/paper/2310.01728/) (2023)
- [InceptionTime: Finding AlexNet for Time Series Classification](https://mlsanity.com/paper/1909.04939/) (2019)
- [Chronos-2: From Univariate to Universal Forecasting](https://mlsanity.com/paper/2510.15821/) (2025)
- [AutoGluon-TimeSeries: AutoML for Probabilistic Time Series Forecasting](https://mlsanity.com/paper/2308.05566/) (2023)
- [Multi-modal learning for geospatial vegetation forecasting](https://mlsanity.com/paper/2303.16198/) (2023)
- [RadioPathomics: Multimodal Learning in Non-Small Cell Lung Cancer for Adaptive Radiotherapy](https://mlsanity.com/paper/2204.12423/) (2022)
- [Can Foundation Models Really Segment Tumors? A Benchmarking Odyssey in Lung CT Imaging](https://mlsanity.com/paper/2505.01239/) (2025)
- [VegeDiff: Latent Diffusion Model for Geospatial Vegetation Forecasting](https://mlsanity.com/paper/2407.12592/) (2024)
- [Deep Learning for Human Locomotion Analysis in Lower-Limb Exoskeletons: A Comparative Study](https://mlsanity.com/paper/2503.16904/) (2025)
- [Context-Gated Cross-Modal Perception with Visual Mamba for PET-CT Lung Tumor Segmentation](https://mlsanity.com/paper/2510.27508/) (2025)
- [MATNet: Multi-Level Fusion Transformer-Based Model for Day-Ahead PV Generation Forecasting](https://mlsanity.com/paper/2306.10356/) (2023)

## Built on by (most cited first, 3 of 3)

- [Hybrid Quantum Neural Network for Multivariate Clinical Time Series Forecasting](https://mlsanity.com/paper/2603.08072/) (2026)
- [VegSim: A Geospatial World Model for Scenario-Conditioned Vegetation Simulation](https://mlsanity.com/paper/2606.21961/) (2026)
- [Learning to Forecast Crop Growth from Earth Observation Data](https://mlsanity.com/paper/2608.14281/) (2026)

## Abstract (arXiv)

Short-term forecasting of vegetation dynamics is a key enabler for data-driven decision support in precision agriculture. Normalized Difference Vegetation Index (NDVI) forecasting from satellite observations, however, remains challenging due to sparse and irregular sampling caused by cloud masking, as well as the heterogeneous climatic conditions under which crops evolve. In this work, we propose a probabilistic forecasting framework for field-level NDVI prediction under sparse, irregular clear-sky acquisitions. The architecture separates the encoding of historical NDVI and meteorological observations from future exogenous covariates, fusing both representations for multi-step quantile prediction. To address irregular revisit patterns and horizon-dependent uncertainty, we introduce a temporal-distance weighted quantile loss that aligns the training objective with the effective forecasting horizon. In addition, we incorporate cumulative and extreme-weather feature engineering to capture delayed meteorological effects relevant to vegetation response. Experiments on European satellite data show that the proposed approach outperforms statistical, deep learning, and time-series baselines on both pointwise and probabilistic evaluation metrics. Ablation studies confirm that target history is the primary driver of performance, with meteorological covariates providing additional gains in the full multimodal setting. The code is available at https://github.com/arco-group/ndvi-forecasting.