Beyond the SDG 15.3.1 Good Practice Guidance 1.0 using the Google Earth Engine platform: developing a self-adjusting algorithm to detect significant changes in water use efficiency and net primary production

References

• Abatzoglou, J. T., Dobrowski, S. Z., Parks, S. A., & Hegewisch, K. C. (2018). Terraclimate, a high-resolution global dataset of monthly climate and climatic water balance from 1958–2015. Scientific Data, 5(1), 170191. doi:10.1038/sdata.2017.191
   PubMed Web of Science ®Google Scholar
• Bao, N., Li, W., Gu, X., & Liu, Y. (2019). Biomass estimation for semiarid vegetation and mine rehabilitation using worldview-3 and sentinel-1 SAR imagery. Remote Sensing, 11(23), 2855. doi:10.3390/rs11232855
   Web of Science ®Google Scholar
• Chasek, P., Akhtar-Schuster, M., Orr, B. J., Luise, A., Rakoto Ratsimba, H., & Safriel, U. (2019). Land degradation neutrality: The science-policy interface from the UNCCD to national implementation. Environmental Science & Policy, 92, 182–190. doi:10.1016/j.envsci.2018.11.017
   Web of Science ®Google Scholar
• Cowie, A. L., Orr, B. J., Castillo Sanchez, V. M., Chasek, P., Crossman, N. D., Erlewein, A., & Welton, S. (2018). Land in balance: The scientific conceptual framework for Land Degradation Neutrality Environ. Environmental Science & Policy, 79, 25–35. doi:10.1016/j.envsci.2017.10.011
   Web of Science ®Google Scholar
• Didan, K., Munoz, A. B., Solano, R., & Huete, A. (2015). MODIS vegetation index user’s guide (MOD13 Series) version 3.00, June 2015 (collection 6). Retrieved from https://lpdaac.usgs.gov/documents/103/MOD13_User_Guide_V6.pdf
   Google Scholar
• Ducoing, C., Peres-Cajías, J., Badia-Miró, M., Bergquist, A. -K., Contreras, C., Ranestad, K., & Torregrosa, S. (2018). Natural resources curse in the long run? Bolivia, Chile and Peru in the Nordic Countries’ mirror. Sustainability, 10(4), 965. doi:10.3390/su10040965
   Web of Science ®Google Scholar
• FAO. (2012). Conducting national feed assessments. In Michael B. Coughenour & Harinder P.S. Makkar. FAO Animal Production and Health Manual No. 15. Rome, Italy.
   Google Scholar
• Fensholt, R., Rasmussen, K., Kaspersen, P., Huber, S., Horion, S., & Swinnen, E. (2013). Assessing land degradation/recovery in the African Sahel from long-term earth observation based primary productivity and precipitation relationships. Remote Sensing, 5(2), 664. doi:10.3390/rs5020664
   Web of Science ®Google Scholar
• Funk, C., Peterson, P., Landsfeld, M., Pedreros, D., Verdin, J., Shukla, S., … Michaelsen, J. (2015). The climate hazards infrared precipitation with stations—a new environmental record for monitoring extremes. Scientific Data, 2(1), 150066. doi:10.1038/sdata.2015.66
   PubMed Web of Science ®Google Scholar
• GEO-LDN Initiative. (2020). Minimum data quality standards and decision trees for SDG Indicator 15.3.1: Proportion of land that is degraded over total land area. In Technical Note. Group on Earth observation land degradation neutrality (GEO-LDN). Geneva, Switzerland: Initiative. Retrieved from http://earthobservations.org/documents/ldn/20200703_GEOLDN_TechnicalNote_FINAL_SINGLE.pdf
   Google Scholar
• Giuliani, G., Chatenoux, B., Benvenuti, A., Lacroix, P., Santoro, M., & Mazzetti, P. (2020a). Monitoring land degradation at national level using satellite earth observation time-series data to support SDG15 – exploring the potential of data cube. Big Earth Data, 4(1), 3–22. doi:10.1080/20964471.2020.1711633
   Web of Science ®Google Scholar
• Giuliani, G., Mazzetti, P., Santoro, M., Nativi, S., Van Bemmelen, J., Colangeli, G., & Lehmann, A. (2020b). Knowledge generation using satellite earth observations to support Sustainable Development Goals (SDG): A use case on land degradation. International Journal of Applied Earth Observation and Geoinformation, 88, 102068. doi:10.1016/j.jag.2020.102068
   Web of Science ®Google Scholar
• Gorelick, N., Hancher, M., Dixon, M., Ilyushchenko, S., Thau, D., & Moore, R. (2017). Google earth engine: Planetary-scale geospatial analysis for everyone, remote sensing of environment. (Vol. 202, pp. 18–27). ISSN 0034-4257. doi:10.1016/j.rse.2017.06.031
   Google Scholar
• Hatfield, J. L., & Dold, C. (2019). Water-use efficiency: Advances and challenges in a changing climate. Frontiers in Plant Science. doi:10.3389/fpls.2019.00103
   Web of Science ®Google Scholar
• Huete, A. R., Liu, H. Q., Batchily, K., & van Leeuwen, W. (1997). A comparison of vegetation indices over a global set of TM images for EOS-MODIS. Remote Sensing of Environment, 59(3), 440–451. doi:10.1016/S0034-4257(96)00112-5
   Web of Science ®Google Scholar
• Huete, A., Didan, K., Miura, T., Rodriguez, E. P., Gao, X., & Ferreira, L. G. (2002). Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sensing of Environment, 83(1–2), 195–213. doi:10.1016/S0034-4257(02)00096-2
   Web of Science ®Google Scholar
• Ivits, E., & Cherlet, M. (2016). Land productivity dynamics: Towards integrated assessment of land degradation at global scales. Luxembourg: Joint Research Centre.
   Google Scholar
• Jarchow, C. J., Didan, K., Barreto-Muñoz, A., Nagler, P. L., & Glenn, E. P. (2018). Application and comparison of the MODIS-derived enhanced vegetation index to VIIRS, Landsat 5 TM and Landsat 8 OLI Platforms: A case study in the Arid Colorado River Delta, Mexico. Sensors (Basel), 18(5), 1546. doi:10.3390/s18051546
   PubMed Web of Science ®Google Scholar
• Jay, S., Potter, C., & Crabtree, R. (2016). Evaluation of modelled net primary production using MODIS and Landsat satellite data fusion. Carbon Balance and Management, 11(1). doi:10.1186/s13021-016-0049-6
   PubMedGoogle Scholar
• Jiang, Z., Huete, A. R., Didan, K., & Miura, T. (2008). Development of a two-band enhanced vegetation index without a blue band. Remote Sensing of Environment, 112(10), 3833–3845. doi:10.1016/j.rse.2008.06.006
   Web of Science ®Google Scholar
• Jönsson, P., & Eklundh, L. (2004). Timesat—A program for analyzing time-series of satellite sensor data. Computers & Geosciences, 30(8), 833–845. doi:10.1016/j.cageo.2004.05.006
   Web of Science ®Google Scholar
• Le Houerou, H. N. (1984). Rain use efficiency: A unifying concept in arid-land ecology. Kidlington, ROYAUME-UNI: Elsevier.
   Google Scholar
• Liang, T., Cui, X., Feng, Q., Wang, Y., & Xia, W. (2009). Remotely sensed dynamics monitoring of grassland aboveground biomass and carrying capacity during 2001–2008 in Gannan pastoral area. Acta Prataculturae Sinica, 18, 12–22.
   Google Scholar
• Ma, X., Huete, A., Moran, S., Ponce Campos, G., & Eamus, D. (2015). Abrupt shifts in phenology and vegetation productivity under climate extremes. Journal of Geophysical Research: Biogeosciences, 120(10), 2036–2052. doi:10.1002/2015JG003144
   Web of Science ®Google Scholar
• McNally, A., Arsenault, K., Kumar, S., Shukla, S., Peterson, P., Wang, S., … Verdin, J. P. (2017). A land data assimilation system for sub-Saharan Africa food and water security applications. Scientific Data, 4(1), 170012. doi:10.1038/sdata.2017.12
   PubMedGoogle Scholar
• Muller, R., Pacheco, P., & Montero, J.C. (2014). The context of deforestation and forest degradation in Bolivia: Drivers, agents and institutions. In Occasional paper 108. Bpogor, Indonesia: CIFOR.
   Google Scholar
• Ponce Campos, G. E., Moran, M. S., Huete, A., Zhang, Y., Bresloff, C., Huxman, T., … Starks, P. J. (2013). Ecosystem resilience despite large-scale altered hydroclimatic conditions. Nature, 494(7437), 349–352. doi:10.1038/nature11836
   PubMed Web of Science ®Google Scholar
• Potter, C., Gross, P., & Genovese, V. (2007). Net primary productivity of forest stands in New Hampshire estimated from Landsat and MODIS satellite data. Carbon Balance and Management, 2(1). doi:10.1186/1750-0680-2-9
   PubMedGoogle Scholar
• Rodell, M., Houser, P. R., Jambor, U., Gottschalck, J., Mitchell, K., Meng, C. J., … Toll, D. (2004). The global land data assimilation system. Bulletin of the American Meteorological Society, 85(3), 381–394. doi:10.1175/BAMS-85-3-381
   Web of Science ®Google Scholar
• Sims, N., Green, C., Newnham, G.J., England, J.R., Held, A., Wulder, M., … McKenzie, N. 2017 Good practice guidance SDG indicator 15.3.1 proportion of land that is degraded over total land area. Version 1. Retrieved from https://www.unccd.int/sites/default/files/relevant-links/2017-0/Good%20Practice%20Guidance_SDG%20Indicator%2015.3.1_Version%201.0.pdf
   Google Scholar
• Sims, N. C., Barger, N. N., Metternicht, G. I., & England, J. R. (2020). A land degradation interpretation matrix for reporting on UN SDG indicator 15.3.1 and land degradation neutrality. Environmental Science & Policy, 114, 1–6. doi:10.1016/j.envsci.2020.07.015
   Web of Science ®Google Scholar
• Sims, N. C., Newnham, G. J., England, J. R., Guerschman, J., Cox, S. J. D., Roxburgh, S. H., … Wheeler, I. (2021). Good practice guidance. SDG indicator 15.3.1, proportion of land that is degraded over total land area. version 2.0. Bonn, Germany: United Nations Convention to Combat Desertification. Retrieved from https://www.unccd.int/publications/good-practice-guidance-sdg-indicator-1531-proportion-land-degraded-over-total-land
   Google Scholar
• Steduto, P., Hsiao, T. C., Fereres, E., & Raes, D. (2012). Crop yield response to water. FAO Irrigation and Drainage Paper Nr. 66. Rome, Italy. Retrieved from http://www.fao.org/docrep/016/i2800e/i2800e00.htm
   Google Scholar
• Steininger, M. K., Tucker, C. J., Ersts, P., Killeen, T. J., Villegas, Z., & Hecht, S. B. (2002). Clearance and fragmentation of tropical deciduous forest in the Tierras Bajas. Santa Cruz, Bolivia. doi:10.1046/j.1523-1739.2001.015004856
   Google Scholar
• Tan, B., Morisette, J. T., Wolfe, R. E., Gao, F., Ederer, G. A., Nightingale, J., & Pedelty, J. A. (2011). An enhanced TIMESAT algorithm for estimating vegetation phenology metrics from MODIS data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 4(2), 361–371. doi:10.1109/JSTARS.2010.2075916.
   Web of Science ®Google Scholar
• Tucker, C. J. (1979). Red and photographic infrared linear combinations for monitoring vegetation. Remote Sensing of Environment, 8(2), 127–150. doi:10.1016/0034-4257(79)90013-0
   Web of Science ®Google Scholar
• Verdum, R., Gamboa, C., & Bebbington, A.J. (2018). Assessment and scoping of extractive industries and infrastructure in relation to deforestation: Amazonia. Retrieved from http://www.climateandlandusealliance.org/wp-content/uploads/2020/03/Amazonia-Report-EN-FINAL-w-cover.pdf
   Google Scholar
• Wang, L. A., Niu, K. C., Yang, Y. H., & Zhou, P. (2010). Patterns of above- and belowground biomass allocation in China’s grasslands: Evidence from individual-level observations. Science China Life Sciences, 53(7), 851–857. doi:10.1007/s11427-010-4027-z
   PubMed Web of Science ®Google Scholar
• Wang, J., Xiao, X., Bajgain, R., Starks, P., Steiner, J., Doughty, B. R., & Chang, Q. (2019). Estimating leaf area index and aboveground biomass of grazing pastures using Sentinel-1, Sentinel-2 and Landsat images. ISPRS Journal of Photogrammetry and Remote Sensing, 154, 189–201. doi:10.1016/j.isprsjprs.2019.06.007
   Web of Science ®Google Scholar
• Wessels, K. J., Prince, S. D., Malherbe, J., Small, J., Frost, P. E., & VanZyl, D. (2007). Can human-induced land degradation be distinguished from the effects of rainfall variability? A case study in South Africa. Journal of Arid Environments, 68(2), 271–297. doi:10.1016/j.jaridenv.2006.05.015
   Web of Science ®Google Scholar
• Yang, Y. H., Fang, J. Y., Ji, C. J., & Han, W. X. (2009a). Above- and belowground biomass allocation in Tibetan grasslands. Journal of Vegetation Science, 20(1), 177–184. doi:10.1111/j.1654-1103.2009.05566.x
   Web of Science ®Google Scholar
• Yang, Y. H., Fang, J. Y., Pan, Y. D., & Ji, C. J. (2009b). Aboveground biomass in Tibetan grasslands. Journal of Arid Environments, 73(1), 91–95. doi:10.1016/j.jaridenv.2008.09.027
   Web of Science ®Google Scholar