Mapping critical zone thickness using remote sensing and artificial neural network in northeast India

Abstract The Earth’s Critical Zone (CZ) is a dynamic and essential layer extending from the canopy top to the unweathered bedrock, where interactions among the atmosphere, lithosphere, hydrosphere, and biosphere sustain ecosystems. This study focuses on mapping the critical zone thickness in northeastern India. To achieve this, the CZ was delineated into two key components: canopy height and depth to bedrock. Canopy height was estimated using the ALT08 data product of the ICESat-2 satellite using the laser altimetry principle, while depth-to-bedrock modelling incorporated environmental factors such as temperature, precipitation, humidity, canopy height, groundwater levels, rock type and soil type. An artificial neural network (ANN) was employed to predict the spatial variation of bedrock depth using publicly available soil profiles, borehole data, and remote sensing-derived environmental covariates. The estimated depth to bedrock (DTB) varies from 12.75 to 460.12 m across Northeast India. CZ thickness (CZT) was determined as the sum of canopy height and bedrock depth, ranging from 20.08 to 485.78 m with an increase northward from the Bramhaputra River and decreases southward, indicating a general trend of increase in DTB from south to north direction across the Kamrup district. The findings of this research provide an assessment of how human activities, land use changes, and climate shifts impact the CZ and the ecosystems it sustains. This knowledge can be instrumental in improving CZ management and contributing to the sustainability of ecosystems in a rapidly changing world.