stms 0.3.3

Spatiotemporal Filling and Multistep Smoothing for satellite time series reconstruction

🌿 STMS: Spatiotemporal and Multistep Smoothing for Time-Series Reconstruction

STMS is a Python package for reconstructing and smoothing cloud-affected or noisy time-series data, particularly from satellite-derived vegetation indices (VI) such as NDVI, EVI, MSAVI, NIRv, and other time-series environmental indicators

Although originally designed for Sentinel-2 data, STMS is data-agnostic and works with any temporal signal measured at geolocated sampling units.

---

✨ What STMS Does

STMS performs two complementary steps:

1️⃣ Spatiotemporal Filling

Reconstructs missing or low-quality values by:

• Detecting consecutive unreliable observations
• Searching for spatially nearby or structurally similar time series
• Selecting candidates based on correlation, distance, and/or grouping (nested IDs)
• Predicting values through polynomial regression and weighted aggregation

2️⃣ Multistep Smoothing

Applies iterative smoothing with increasing quality thresholds to:

• Smooth noisy observations
• Preserve phenological or seasonal shapes
• Adaptively reweight low-confidence points
• Produce a final smooth, continuous time-series signal

STMS is suitable for:

• Consecutive cloud or incomplete satellite vegetation indices
• Remote sensing environmental monitoring
• Agricultural time-series
• Ecological and climate data
• Any geotemporal dataset with consecutive missing or noisy values

---

📦 Installation

From PyPI:

pip install stms

---

⚙️ Basic Usage

from stms import stms

model = stms()

vi_filled = model.spatiotemporal_filling(
    id_sample=id_array,
    days_data=day_of_year,
    vi_data=vi_raw,
    long_data=longitude,
    lati_data=latitude,
    cloud_data=cloudscore
)

vi_smoothed = model.multistep_smoothing(
    id_sample=id_array,
    days_data=day_of_year,
    vi_data=vi_filled,
    cloud_data=cloudscore
)

Required Inputs

Name	Description
id_sample	ID for each time-series (one ID per pixel/plot/station)
days_data	Time axis as day-of-year or numeric timestamp
vi_data	Raw VI values (cloudy allowed)
long_data, lati_data	Spatial coordinates (used for candidate search)
cloud_data	CloudScore+ or quality weights

---

🔧 Parameter Overview

You can customize the STMS behavior when constructing the model:

model = stms(
    n_consecutive=5,
    threshold_cloudy=0.1,
    threshold_corr=0.9,
    n_candidate=10,
    n_tail=24,
    id_nested=None,
    n_candidate_nested=None,
    max_candidate_pool=None,
    candidate_sampling="distance"
)

Parameter	Description
n_consecutive	Minimum consecutive low-quality points considered a gap
n_tail	Padding before/after a gap
threshold_cloudy	Quality threshold to classify an observation as “cloudy”
threshold_corr	Minimum correlation required to accept a candidate
n_candidate	Maximum global number of candidate series used
id_nested	Optional grouping (e.g., pixel → field, station → region)
n_candidate_nested	Limit candidates per group
max_candidate_pool	Maximum groups selected
candidate_sampling	"distance" or "random" group sampling

---

🧪 Example with Simulated Data

import numpy as np
from stms import stms

# simulate VI curve
def sine_curve(t):
    return 0.3*np.sin(2*np.pi/100 * (t - 90)) + 0.5

x = np.arange(0, 365, 5)
vi = sine_curve(x) + np.random.normal(0, 0.02, len(x))
cloud = np.ones_like(vi)

# introduce synthetic cloud contamination
cloud[40:55] = 0.01
vi[40:55] = np.random.uniform(0.05, 0.1, 15)

model = stms()
vi_filled = model.spatiotemporal_filling(
    id_sample=np.array(["A"]*len(x)),
    days_data=x,
    vi_data=vi,
    long_data=np.repeat(110.0, len(x)),
    lati_data=np.repeat(-7.0, len(x)),
    cloud_data=cloud
)

vi_smooth = model.multistep_smoothing(
    id_sample=np.array(["A"]*len(x)),
    days_data=x,
    vi_data=vi_filled,
    cloud_data=cloud
)

---

📚 Citation

If STMS contributes to your research, please cite:

Suseno, B., Brunel, G., Wijayanto, H., Sadik, K., Afendi, F. M., & Tisseyre, B. (2025). Reconstructing satellite temporal series data under cloudy conditions: Application in predicting rice growth phases. Smart Agricultural Technology, 12, 101378. https://doi.org/10.1016/j.atech.2025.101378

---

📄 License

MIT License © Bayu Suseno

---

🤝 Contributing

Pull requests, bug reports, and feature suggestions are welcome! Please open an issue if you encounter any problem.