Improved mapping and understanding of land system states for achieving land degradation neutrality using remote sensing surface endmembers-temperature space: A case-study in Minqin, China

The identification and understanding of land system states are critical for achieving land degradation neutrality (LDN). However, present conventional approaches can hardly generate a state space with both complex linear and abrupt shift relationships. Therefore, with the remote sensing surface endmembers-temperature space, this study developed a framework to identify, map and understand land system states and their associated trajectories based on the alternative state theory. Combined with three variables of land cover and landscape, our proposed vegetation productivity index (VPI) can highly improve the mapping accuracy of land system states in Minqin, without considering the variable of soil type. We built the three-dimensional state catastrophe space with variables of soil organic matter (SOM), VPI, and cover function index (LCI). Thanks to the ball-and-cup model, two alternative stable states and five unstable states were identified at the landscape level in Minqin; then, we further explored the desired threshold of VPI and LCI were 13.11 and 0.39, while their undesired threshold were 5.15 and 0.76, respectively. The state space can reveal the linear or non-linear relationship between VPI/LCI and SOM; besides, it can also present the hysteresis of land system state. Moreover, our established model in 2015 proved to be stable and thus can be used to accurately estimate the states in 2018. Therefore, our proposed framework can be an effective way of mapping and understanding present and future land system states, which can provide useful information on proactive conservation and restorative interventions for LDN management.