Update a description

components/map/constants.js

@@ -1052,7 +1052,7 @@ exports.LAYERS = {
         spatialResolution: '250 m',
         contentDate: '2000-2018',
         description:
-          'The soil organic carbon (SOC) stock maps shows predictions of the 0-30 cm SOC stock in t C/ha for the 2000-2018 period. The grid maps cover a landmask of the entire globe, have 250 m spatial resolution and annual frequency.\n\nThe 0-30 cm SOC stock maps were created using machine learning algorithms. First, a baseline SOC stock map for the year 2000 was taken from soilgrids.org. Next, SOC stock change was modelled from the baseline year onward by taking into account SOC change factors related to land use change. See below for methodological details.\n### Methodology\nWe used the UNCCD modified IPCC Tier 1 method (UNCCD, 2018) to model the SOC stock change from the 2000 baseline year. This method considers three change factors:\n- A land use factor that reflects carbon stock changes associated with land use change.\n- A management factor that represents the effect of management practice on SOC stock.\n- An input factor representing the effect of different levels of carbon input to soil on SOC stock.\n\nFor global applications there currently are no suitable data sources to assess the management and input factors. These were therefore ignored. The land use factor was derived from the European Space Agency (ESA) CCI-LC 300m dataset (ESA, 2017). This dataset provides annual time series of land cover from 1992 until 2018. It distinguishes 36 land cover classes, which were aggregated to 7 land cover classes used by the UNCCD.\n\nA land cover change leads to an increase or decrease of the SOC stock, depending on whether the land use factor that is associated with the change is bigger or smaller than 1. Some of these change factors depend on climate zone. For this the Global Agro-Ecological Zones (IIASA/FAO, 2012) map was used, which has 12 classes and was assumed constant during the time period considered. The effect of land cover change on the 0-30 cm SOC stock as modelled in the UNCCD-modified IPCC Tier 1 approach is not immediate but may take up to 20 years.\n\nWe used GRASS GIS for pre-processing land cover maps, data storage and tiling of predictions. The modelling was done with R software. The procedure was implemented in parallel using a High Performance Computing facility of Wageningen University.',
+          "The soil organic carbon (SOC) stock maps shows predictions of the 0-30 cm SOC stock in t C/ha for the 2000-2018 period. The grid maps cover a landmask of the entire globe, have 250 m spatial resolution and annual frequency.\n\nThe 0-30 cm SOC stock maps were created using machine learning algorithms. First, a baseline SOC stock map for the year 2000 was taken from soilgrids.org. Next, SOC stock change was modelled from the baseline year onward by taking into account SOC change factors related to land use change. See below for methodological details.\n### Methodology\nWe used the UNCCD modified IPCC Tier 1 method (UNCCD, 2018) to model the SOC stock change from the 2000 baseline year. This method considers three change factors:\n- A land use factor that reflects carbon stock changes associated with land use change.\n- A management factor that represents the effect of management practice on SOC stock.\n- An input factor representing the effect of different levels of carbon input to soil on SOC stock.\n\nFor global applications there currently are no suitable data sources to assess the management and input factors. These were therefore ignored. The land use factor was derived from the European Space Agency (ESA) CCI-LC 300m dataset (ESA, 2017). This dataset provides annual time series of land cover from 1992 until 2018. It distinguishes 36 land cover classes, which were aggregated to 7 land cover classes used by the UNCCD.\n\nA land cover change leads to an increase or decrease of the SOC stock, depending on whether the land use factor that is associated with the change is bigger or smaller than 1. Some of these change factors depend on climate zone. For this the Global Agro-Ecological Zones (IIASA/FAO, 2012) map was used, which has 12 classes and was assumed constant during the time period considered. The effect of land cover change on the 0-30 cm SOC stock as modelled in the UNCCD-modified IPCC Tier 1 approach is not immediate but may take up to 20 years.\n\nWe used GRASS GIS for pre-processing land cover maps, data storage and tiling of predictions. The modelling was done with R software. The procedure was implemented in parallel using a High Performance Computing facility of Wageningen University.\n\nSee internal report ['ISRIC Product 1'](/files/isric-product-1.pdf) for more details about the methodology and implementation.",
         cautions:
           '- The UNCCD-modified IPCC Tier 1 method requires detailed input information, much of which is not globally available at high accuracy and high spatial resolution. For this reason SOC stock change due to management and input factors was not included. Estimated changes over time are therefore underestimations of real change.\n- The Tier 1 method strongly simplifies SOC dynamics. More elaborate approaches are the Tier 2 method, which includes more refined datasets, and the Tier 3 method, which makes use of mechanistic SOC change models.\n- The baseline SOC stock map for the year 2000 is not error-free and has considerable uncertainty, as shown using cross-validation statistics and prediction interval widths. These uncertainties propagate through the UNCCD-modified IPCC Tier 1 method and will affect the SOC stock change maps.',
         sources: null,