Aim of the study:
In rural and agricultural development, land plays a crucial role in driving productivity. To understand the impact of specific causes or combinations of causes on outcomes, it is essential to identify and establish clear causal relationships. Our study investigates the causal effects of different land use systems against Land Surface Temperature (LST) in Henan Province. We further make land use predictions based on current trends. Understanding these dynamics is essential for enhancing agricultural informatization, environmental management, and climate-smart choices of local districts, counties and villages across China’s agriculturally important regions and beyond.

Material and methods:
The study utilized integrated remote sensing data, techniques and a causality approach to investigate land use systems (LUS) and LST in Henan Province. We further used Modules for Land Use Change Evaluation (MOLUSCE) and Cellular Automata-Artificial Neural Network (CA-ANN) to predict LUS for the near future (2023–2053).

Results and conclusions:
Results revealed that built-up areas (+500%), forests (+50.88%) and water bodies (+83.56%) have expanded massively during the past 40 years. In contrast, cultivated (–20.81%) and barren areas (–60.53%) declined steadily. The temporal causal inference analysis demonstrated a strong convergence between built-up areas and land surface temperature (LST), which substantiates built-up areas’ profound impact on LST intensity. The spatial causal inference analysis shows moderate to robust positive indirect cross-mapping relationships between built-up areas (ρ = 0.63) and bare land (ρ = 0.32) against LST. Land use predictions (2023–2053) show a reduction in areas covered by forests and water bodies, and a reversed trend in cultivated lands. These are particularly important when formulating targeted policy-directives needed to regulate unsustainable land-use processes and undesirable economic trade-offs.