Method and system for dynamic estimation of vertical distribution of soil organic carbon (soc)

A system, comprising:

• a memory storing instructions;
• one or more communication interfaces;
• and
• one or more hardware processors coupled to the memory via the one or more communication interfaces, wherein the one or more hardware processors are configured by the instructions to:
• receive (i) a plurality of spectral embeddings derived from a satellite data, (ii) a plurality of temporal embeddings derived from a plurality of management proxies, and (iii) an attention score from a soil spectral library (SSL) based on one or more soil spectral signatures derived from the SSL for a region of interest (ROI);
• integrate the plurality of spectral embeddings, the plurality of temporal embeddings, and the SSL attention score into a joint feature space to obtain a domain-aware machine learning (ML) model;
• apply a loss function to the domain-aware ML model, wherein the loss function employs the plurality of temporal embeddings and the one or more soil spectral signatures to minimize an error in a surface soil organic carbon (SOC) prediction for the ROI;
• generate a temporal SOC for the ROI by a process-based model, wherein the process-based model utilizes the plurality of spectral embeddings, the plurality of temporal embeddings and the SSL data to analyze a historical surface SOC and a historical vertical SOC over a period to generate the temporal SOC;
• obtain a correction factor by subtracting the temporal SOC generated by the process-based model and the surface SOC estimated from the domain-aware ML model;
• estimate a corrected surface SOC by applying the correction factor to the surface SOC estimates obtained from the domain-aware ML model;
• obtain one or more depth specific SOC fractions of the ROI, by:
• receiving a static vertical SOC profile at a plurality of depths from a global soil information system;
• generating a temporally weighted SOC profile by the process-based model, wherein the process-based model calculates the temporal SOC at the plurality of depths;
• estimating a plurality of SOC fractions at the plurality of depths from the SSL, wherein the plurality of SOC fractions represents one or more general trends of one or more soil properties in the ROI;
• and
• combining the static vertical SOC fraction received from the global soil information system, the temporally weighted SOC derived from the process-based model, and the plurality of SOC fractions estimated from the SSL to obtain the one or more depth specific SOC fractions;
• and
• estimate a vertically distributed SOC for the ROI by combining the corrected surface SOC of the domain-aware ML model and the one or more depth specific SOC fractions of the ROI.

One or more non-transitory machine-readable information storage mediums comprising one or more instructions which when executed by one or more hardware processors cause:

• receiving (i) a plurality of spectral embeddings derived from a satellite data, (ii) a plurality of temporal embeddings derived from a plurality of management proxies, and (iii) an attention score from a soil spectral library (SSL) based on one or more soil spectral signatures derived from the SSL for a region of interest (ROI);
• integrating the plurality of spectral embeddings, the plurality of temporal embeddings, and the SSL attention score into a joint feature space to obtain a domain-aware machine learning (ML) model;
• applying a loss function to the domain-aware ML model, wherein the loss function employs the plurality of temporal embeddings and the one or more soil spectral signatures to minimize an error in a surface soil organic carbon (SOC) prediction for the ROI;
• generating a temporal SOC for the ROI by a process-based model, wherein the process-based model utilizes the plurality of spectral embeddings, the plurality of temporal embeddings and the SSL data to analyze a historical surface SOC and a historical vertical SOC over a period to generate the temporal SOC;
• obtaining a correction factor by subtracting the temporal SOC generated by the process-based model and the surface SOC estimated from the domain-aware ML model;
• estimating a corrected surface SOC by applying the correction factor to the surface SOC estimates obtained from the domain-aware ML model;
• obtaining (414), via the one or more hardware processors, one or more depth specific SOC fractions of the ROI, by:
• receiving a static vertical SOC profile at a plurality of depths from a global soil information system;
• generating a temporally weighted SOC profile by the process-based model, wherein the process-based model calculates the temporal SOC at the plurality of depths;
• estimating a plurality of SOC fractions at the plurality of depths from the SSL, wherein the plurality of SOC fractions represents one or more general trends of one or more soil properties in the ROI;
• and
• combining the static vertical SOC fraction received from the global soil information system, the temporally weighted SOC derived from the process-based model, and the plurality of SOC fractions estimated from the SSL to obtain the one or more depth specific SOC fractions;
• and
• estimating a vertically distributed SOC for the ROI by combining the corrected surface SOC of the domain-aware ML model and the one or more depth specific SOC fractions of the ROI.