2026/07/24 by Mingyue Sun, Ping Gong, Pengfei Li +3
Environmental Science · #Soil Moisture and Remote Sensing #Remote Sensing in Agriculture #Soil Geostatistics and Mapping
paper · doi:10.1016/j.geoderma.2026.117953
Satellite prediction of multiple soil attributes in arid irrigated croplands is limited by the spectral-domain gap between proximal spectra and satellite observations. Here, we developed a hierarchical, physics-guided, multimodal, multitask network (HPM 3 -Net) for laboratory–field–satellite soil spectral transfer. Using 1,250 topsoil samples from the Manas River Basin, laboratory spectra, field spectra, ZY1 Advanced Hyperspectral Imager (AHSI) data and Sentinel-2 (S2) observations, we predicted 12 soil attributes related to salinity–alkalinity, fertility and texture. The framework integrated cross-modal alignment, teacher–student distillation, hierarchical multitask learning, sensor-response-function (SRF) harmonization, and constraint-based regularization to improve the plausibility and internal consistency of multi-property predictions. Laboratory and field spectra were harmonized to satellite bands using SRF convolution rather than treated as direct substitutes for surface reflectance. The HPM 3 -Net achieved an overall mean R 2 of 0.755 ± 0.011. The 2025 temporal validation reduced the overall mean R 2 by 10.55%. The sensitivity analysis showed that the overall mean R 2 remained between 0.730 and 0.755. More importantly, satellite-scale observability, defined here as the extent to which a soil attribute retains stable spectral–spatial signals that can be sensed after transfer from laboratory spectra to satellite observations, was process-dependent. Texture fractions, soil organic matter, total salt content and calcium carbonate equivalent were more strongly expressed through stable spectral–spatial signals, whereas fertility-related attributes and some alkalinity indicators depended more on indirect coupling with soil texture, salinity and management-related soil conditions. These findings highlight the benefits and limits of physically constrained spectral transfer for satellite-domain soil prediction.