MIRANDA 🍀
MId-feature RANk-adversarial
Domain Adaptation
toward climate change-robust ecological forecasting with deep learning

Yuchang Jiang, Jan Dirk Wegner, Vivien Sainte Fare Garnot
DM3L, University of Zurich, Zurich, Switzerland
CVPR EarthVision 2026
Paper Code arXiv

Abstract

Plant phenology modelling aims to predict the timing of seasonal phases, such as leaf-out or flowering, from meteorological time series. Reliable predictions are crucial for anticipating ecosystem responses to climate change. While phenology modelling has traditionally relied on mechanistic approaches, deep learning methods have recently been proposed as flexible, data-driven alternatives with often superior performance. However, mechanistic models tend to outperform deep networks when data distribution shifts are induced by climate change. Domain Adaptation (DA) techniques could help address this limitation. Yet, unlike standard DA settings, climate change induces a tem- poral continuum of domains and involves both a covariate and label shift, with warmer records and earlier start of spring. To tackle this challenge, we introduce Mid-feature Rank-adversarial Domain Adaptation (MIRANDA). Whereas conventional adversarial methods enforce domain invariance on final latent representations, an approach that does not explicitly address label shift, we apply adversarial regularization to intermediate features. Moreover, instead of a binary domain-classification objective, we employ a rank-based objective that enforces year-invariance in the learned meteorological representations. On a country-scale dataset spanning 70 years and comprising 67,800 phenological observations of 5 tree species, we demonstrate that, unlike conventional DA approaches, MIRANDA improves robustness to climatic distribution shifts and narrows the performance gap with mechanistic models.

Phenology Forecasting

phenology modeling

What is phenology modelling? A phenology model takes a set of climatic and environmental covariates and predicts phenological dates. In this paper we consider models operating on daily meteorological time series to predict the date of 50% leaf development of 5 different tree species. For phenology forecasting, we use historic data to predict phenological stages in the future.



phenology forecasting

Challengs for phenology forecasting with deep learning? Deep learning models trained on historical climatic conditions face a distribution shift when applied to future climate projections. In this paper we consider models that predict plant phenological dates from meteorological time series and devise a domain adaptation method to enhance their robustness to such shifts.

Our proposed method: MIRANDA 🍀

method overview

MIRANDA

Our framework addresses domain shifts in phenology modelling via two key components:
rank-based adversarial training on intermediate features and hybrid layer normalization


Purple elements correspond to the main phenology prediction pathway: the learnable tokens L are concatenated with the input time series Ei and processed by two transformer encoder layers (t1 and t2). The resulting global embeddings of the learnable tokens, denoted as Gi, are then fed into the decoder d to predict the target date using the regression loss LMSE. We apply rank-based adversarial learning on the mid-level features Zi (green), where a discriminator p is trained with a ranking loss Lrank through a gradient reversal layer to encourage domain-invariant mid-level features. Meanwhile, we replace the standard layer normalization in t2 with our hybrid layer normalization (blue), which preserves domain-dependent variations in high-level representations.

Datasets

datasets

Datasets: we have three datasets with domain distribution shifts. We show the histograms of input (top) and predicted (bottom) variables on the train, validation, and test sets for each of the three dataset splits. We plot the histogram of annual temperature in the top row to represent the climatic distribution, and the date of Larch needle emergence in the bottom row to illustrate the shift in predicted variables.

Results

BibTeX


        @article{jiang2026miranda,
          title={MIRANDA: MId-feature RANk-adversarial Domain Adaptation toward climate change-robust ecological forecasting with deep learning},
          author={Jiang, Yuchang and Wegner, Jan Dirk and Garnot, Vivien Sainte Fare},
          journal={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops},
          year={2026},
        }