| Sumario: | Soil organic carbon (SOC) is a crucial component related to various processes that
ensure soil health and function. Its modeling is vital for assessing and monitoring soil
degradation caused by the potential impact of agricultural activities. This study aimed
to model SOC in the Northern highlands of Peru, characterized by a high amount of
SOC, which is being affected by crop expansion. Crop rotation (CR) was incorporated
into a modeling exercise using remote sensing data, fieldwork, and farmer surveys. A
multi-year classification model with seven cropland classes was developed using data
collected from 534 fields across 2022-2024, including 189 soil samples. Each cropland
field was represented as a polygon delineating its boundaries and indicating its
dominant crop cover. Time series of multispectral Sentinel-2 Level-2A Top of Canopy
imagery were used to derive phenological features—such as the timing of maximum
canopy cover and the length of the growing period—based on Normalized Difference
Vegetation Index (NDVI) time series. A Random Forest classifier was used as the
baseline model. The cropland classification model demonstrated strong overall
performance, with F1 scores ranging from 0.81 to 0.98 across the different classes. The
model performed well for lupin and pasture but scored lower for beans and potatoes.
Predictions of cropland classes from 2019 to 2022 were created, resulting in frequency
layers that represent crop rotations. Four feature configurations were evaluated: (i)
including all features as a benchmark, (ii) excluding climatology, (iii) excluding crop
rotation history, and (iv) excluding soil properties. Configurations including all features
and excluding crop rotation history showed the highest performance (R2 = 0.63), while
those excluding climatology or soil properties performed worse (R2 ≈ 0.52–0.53).
Although soil features were the most important, fallow frequency emerged as the most
critical predictor of SOC in crop rotations. When soil data were excluded, fallow
frequency, combined with climatic features, explained over half of the SOC variability.
The findings emphasize the importance of incorporating CR into SOC mapping efforts.
|
|---|