A key challenge in training AI models is lack of labeled or ground truth data. This is especially true in the remote sensing field where seasonal changes and differences between label characteristics makes difficult creating a common labeled dataset. With the emergence of self-supervised learning the amount and quality of labeled data can be relaxed but model performance is still of paramount importance. This is especially true for quantifying the sources and sinks of greenhouse gases that drive climate change. In this talk, we present how state of the art AI technologies such as generative AI and Foundation Models can be used to estimate Above Ground Biomass (AGBD) changes due to extraction of CO2 from the atmosphere by vegetation. We demonstrate how these tools can be used by companies with NetZero pledge to quantify, monitor, validate and report their offsetting methodologies and sustainability practices.