The inherent spatial mismatch between satellite-derived and ground-observed near-surface soil moisture (SM) data necessitates cautious interpretation of point-to-pixel comparisons. Though data-driven upscaling of point-scale SM may enable statistically sound comparisons, the uncertainty across a spatial domain was less explored in previous studies. This gap underscores the need of addressing the spatial prediction uncertainties when extrapolating SM information to a broader spatial scale. Accordingly, this study presents a spatial prediction approach integrating machine learning (ML) and spatiotemporal fusion, which enables the characterisation of SM variability at the Landsat satellite footprint. Spatially clustered SM from 28 in-situ stations was extrapolated to a 100 km × 100 km area at 100 m resolution over a cross-validation period (2016-2019) and an independent test period (2020-2021). The area of applicability (AOA), which represents the spatial extent within which a prediction model is considered reliable, was determined for two ML models; Random Forests (RF) and eXtreme Gradient Boosting (XGB). The AOA of RF and XGB models encompassed 43.1% and 41.5% of the study area, respectively. The spatial SM predictions were further evaluated against multiple independent datasets, including field campaign data, in-situ SM from different networks, and satellite retrievals. Specifically, RF-predicted SM achieved a spatial R of 0.62-0.64 against field campaign data, temporal R of 0.84-0.91 against network-recorded data, and spatiotemporal R of 0.87 against SMAP L2 data during the cross-validation period. SM predictions within the AOA showed markedly lower uncertainties, which were further validated across an extended area (300 km × 300 km) with diverse physiographic conditions. Overall, this study demonstrated the use of AOA in delineating the statistically reliable spatial extent for ML-based SM predictions.

Soil moisture (SM) estimation from active microwave data remains challenging due to the complex interactions between radar backscatter and surface characteristics. While the water cloud model (WCM) provides a semi-physical approach for understanding these interactions, its empirical component often limits performance across diverse agricultural landscapes. This research presents preliminary efforts for developing a knowledge-guided deep learning approach, which integrates WCM principles into a long short-term memory (LSTM) model, to estimate field SM using Sentinel1 Synthetic Aperture Radar (SAR) data. Our proposed approach leverages LSTM’s capacity to capture spatiotemporal dependencies while maintaining physical consistency through a modified dual-component loss function, including a WCMbased semi-physical component and a boundary condition regularisation. The proposed approach is built upon the soil backscatter coefficients isolated from the total backscatter, together with Landsat-resolution vegetation information and surface characteristics. A four-fold spatial cross-validation was performed against in-situ SM data to assess the model performance. Results showed the proposed approach reduced SM retrieval uncertainties by 0.02 m³/m³ and achieved correlation coefficients (R) of up to 0.64 in areas with varying vegetation cover and surface conditions, demonstrating the potential to address the over-simplification in WCM.

Farmers are among the most vulnerable groups that need to adapt to climate change. Correct perception is a prerequisite for farmers to adopt adaptation strategies, which plays a crucial guiding role in the development of adaptation plans and the improvement of the security of livelihoods. This study aimed to compare farmers’ perceptions of temperature and precipitation change with meteorological data in two regions of the Tibetan Plateau, analyzed how irrigation affects farmers’ perceptions. Data were obtained from local meteorological stations and household questionnaires (N = 1005). The study found that, since 1987, the climate warming trend was significant (p < 0.01), and the temperature increase was faster in winter. Precipitation had no significant change trend, but the seasonal variations indicated that the precipitation concentration period moved forward in the Pumqu River Basin and was delayed a month in the Yellow River-Huangshui River valley. The farmers’ perception of temperature change was consistent with meteorological data, but there was an obvious difference in precipitation perception between the two regions. We noticed that irrigation facilities played a mediating role on precipitation perception and farmers having access to irrigation facilities were more likely to perceive increased precipitation. Finally, this study suggested that meteorological data and farmers’ perceptions should be integrated when developing policies, rather than just considering actual climate trends. Simultaneously, while strengthening irrigation investment, the government should also pay attention to publicizing the consequences of climate change and improving farmers’ abilities of risk perception.

The evaluation of modelled or satellite-derived soil moisture (SM) estimates is usually dependent on comparisons against in-situ SM measurements. However, the inherent mismatch in spatial support (i.e., scale) necessitates a cautious interpretation of point-to-pixel comparisons. The upscaling of the in-situ measurements to a commensurate resolution to that of the modelled or retrieved SM will lead to a fairer comparison and statistically more defensible evaluation. In this study, we presented an upscaling approach that combines spatiotemporal fusion with machine learning to extrapolate point-scale SM measurements from 28 in-situ sites to a 100 m resolution for an agricultural area of 100 km × 100 km. We conducted a four-fold cross-validation, which consistently demonstrated comparable correlation performance across folds, ranging from 0.6 to 0.9. The proposed approach was further validated based on a cross-cluster strategy by using two spatial subsets within the study area, denoted as cluster A and B, each of which equally comprised of 12 in-situ sites. The cross-cluster validation underscored the capability of the up-scaling approach to map the spatial variability of SM within areas that were not covered by in-situ sites, with correlation performance ranging between 0.6 and 0.8. In general, our proposed upscaling approach offers an avenue to extrapolate point measurements of SM to a spatial scale more akin to climatic model grids or remotely sensed observations. Future investigations should delve into a further evaluation of the upscaling approach using independent data, such as model simulations, satellite retrievals or field campaign data.