A new global land productivity dynamic product based on the consistency of various vegetation biophysical indicators

Changes in land productivity have been endorsed by the Inter Agency Expert Group on Sustainable Development Goals(IAEGSDGs)as key indicators for monitoring SDG 15.3.1.Multiple vegeta-tion parameters from optical remote sensing techniques have been widely utilized across different land productivity decline processes and scales.However,there is no consensus on indicator selection and their effectiveness at representing land productivity declining at different scales.This study proposes a fusion framework that incorporates the trends and consistencies within the four com-monly used remote sensing-based vegetation indicators.We ana-lyzed the differences among the four vegetation parameters in different land cover and climate zones,finally producing a new global land productivity dynamics(LPD)product with confidence level degrees.The LPD classes indicated by the four vegetation indicators(VIs)showed that all three levels(low,medium,and high confidence)of increasing area account for 23.99%of the global vegetated area and declining area account for 7.00%.The Increase high-confidence(HC)area accounted for 2.77%of the total area,and the Decline-HC accounted for 0.35%of the total area.This study demonstrates the accuracy of the high-confidence(HC)area for the evaluation of land productivity decline and increase.The“forest”landcover type and“humid”climate zone had the largest increasing and declining area but had the lowest high-confidence proportion.The data product provides an important and optional reference for the assessment of SDG 15.3.1 at global and regional scales according to the specific application target.

HiLPD-GEE:high spatial resolution land productivity dynamicscal culation tool using Landsat and MODIS data

Land productivity is one of the sub-indicators for measuring SDG 15.3.1.Land Productivity Dynamics(LPD)is the most popular approach for reporting this indicator at the global scale.A major limitation of existing products of LPD is the coarse spatial resolution caused by remote sensing data input,which cannot meet the requirement offine-scale land degradation assessment.To resolve this problem,this study developed a tool(HiLPD-GEE)to calculate 30 m LPD by fusing Landsat and MODIS data based on Google Earth Engine(GEE).The tool generates high-quality fused Normalized Difference Vegetation Index(NDVI)dataset for LPD calculation through gapfilling and Savitzky–Golayfiltering(GF-SG)and then uses the method recommended by the European Commission Joint Research Centre(JRC)to calculate LPD.The tool can calculate 30 m LPD in any spatial range within any time window after 2013,supporting global land degradation monitoring.To demonstrate the applicability of this tool,the LPD product was produced for African Great Green Wall(GGW)countries.The analysis proves that the 30 m LPD product generated by HiLPD-GEE could reflect the land productivity change effectively and reflect more spatial details.The results also provide an important insight for the GGW initiative.