An inclusive approach to crop soil moisture estimation: Leveraging satellite thermal infrared bands and vegetation indices on Google Earth engine
Bibliographic record
Abstract
Soil moisture estimation is critical for environmental and agricultural sustainability, with its spatial and temporal variation playing a key role in drought monitoring and understanding climate change. The region of Prince Edward Island (PEI), Atlantic Canada's largest potato producer, is facing irregular precipitation patterns that stress crop water supplies. This study aims to estimate field-scale soil moisture utilizing satellite-based reflective and thermal infrared bands from Landsat-8 Operational Land Imager (OLI)/Thermal Infrared Sensor (TIRS) and Moderate-resolution Imaging Spectroradiometer (MODIS) over the cloud-based Google Earth Engine (GEE) platform. The GEE data catalog's pre-processed data endured to calculate various indicators for the agricultural seasons of 2021 and 2022 across three designated plots: A, B, and C. The indicators are land surface temperature (LST), normalized difference vegetation index (NDVI), normalized difference water index (NDWI), and normalized difference moisture index (NDMI). NDVI and LST were used to calculate the soil moisture index (SMI), representing the real-time soil moisture at the field scale. The soil moisture data was validated using in situ measurements. The analysis showed good Root Mean Square Error values of 1.43 % (Plot A), 2.12 % (Plot B), and 2.60 % (Plot C). A weak negative association between LST and NDVI was noticed in the study, with R² values of 0.25, 0.38 and 0.26 for Plots A, B and C, respectively. As the LST rises, vegetation declines due to the elevated temperatures in the study area. Second, a significant (p < 0.05) negative correlation (R 2 =1) existed between SMI and LST in both the 2021 and 2022 seasons, showing a decrease in the top layer soil moisture with LST. The NDWI exhibited a significant inverse correlation with soil moisture, while NDMI and NDVI are effective predictors. Hence, based on the current study, optical and thermal remote sensing offers valuable insights into soil moisture dynamics and can be a good tool for irrigation control and water conservation. • Used thermal/optical indices to estimate soil moisture in potato crops via remote sensing. • Combined SMI, NDVI, NDMI, NDWI, and LST for soil moisture quantification. • Used Landsat 8 OLI/TIRS and MODIS imagery for soil moisture data collection. • Validated soil moisture with satellite data and in situ measurements over two seasons. • Achieved high accuracy with low RMSE, confirming the effectiveness of indices for moisture prediction.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".