the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
CROPGRIDS: A global geo-referenced dataset of 173 crops circa 2020
Fiona H. M. Tang
Thu Ha Nguyen
Giulia Conchedda
Leon Casse
Francesco N. Tubiello
Federico Maggi
Abstract. Despite recent advancements in cloud processing and modelling and the increasing availability of high spectral- and temporal- resolution satellite imagery, mapping the spatial distribution of crop types remains a challenging task. Here, we present CROPGRIDS – a comprehensive global, geo-referenced dataset providing information on areas for 173 crops circa the year 2020, at a resolution of 0.05° (~5.55 km at the equator). It represents a major update of the Monfreda et al. (2008) dataset, the most widely used geospatial dataset previously available, covering 175 crops with reference year 2000 at 10 km spatial resolution. CROPGRIDS updates Monfreda et al. (2008) through the careful evaluation of 26 published gridded datasets covering more recent crop information at regional, national, and global levels, largely over the period 2015–2020. The new product successfully updates the area extent for 80 of the 175 crops originally covered, representing an update to 1.2 billion hectares of crop area (i.e., 81 % of the total cropland included in CROPGRIDS). CROPGRIDS carries forward the crop type maps originally in Monfreda et al. (2008) for 93 crops as more recent information for these crops is not available. We compared CROPGRIDS harvested area of individual crops against independent national and subnational data from 36 National Statistical Offices (NSOs), national-level crop area data for more than 180 countries and territories from FAOSTAT, as well as geospatially, against a newly available high-resolution (30 m) cropland agreement map (Tubiello et al., 2023). Results indicated robustness against the available independent information, with CROPGRIDS world total harvested and crop areas around 1.5 billion hectares. To the best of our knowledge, CROPGRIDS represents the most comprehensive update of previous work on the subject area, offering a new benchmark of global gridded harvested and crop area data for the year circa 2020. CROPGRIDS dataset can be downloaded at https://doi.org/10.6084/m9.figshare.22491997 (Tang et al., 2023).
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Fiona H. M. Tang et al.
Status: open (until 22 Jun 2023)
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RC1: 'Comment on essd-2023-130', Anonymous Referee #1, 16 May 2023
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See the attached file
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RC2: 'Comment on essd-2023-130', Anonymous Referee #2, 29 May 2023
reply
Spatial-explicit crop distribution maps are important for biogeochemical cycle modelings. This study produced a dataset with 173 crop maps using a data harmonization approach. I found the data useful, but the details of the approaches used were not clear. For the top-down method, a commonly seen challenge is to appropriately define the allocation priority. This often happens when allocating a few types of crops to a region while lacking sub-national distribution information. The authors have described the details of the rules built in using the data (i.e. the ranking of the data). However, it is unclear what rules were applied in allocating each of the crop areas to maps. In other words, which crop type was given the priority to be allocated first? The sequence matters because when a specific crop was allocated in a grid, other crop types will have to be allocated to the rest available area in the grid. When the grid is fully occupied, other candidate types need to be allocated to other grids. Moreover, there is also the case when a grid can potentially be allocated to many types of crops, but the total area of these crops exceeds the available cropland area in the grid. Then, the allocation sequence also matters. Therefore, the sequence of the crop to be allocated greatly determines product reliability. I think this is the most challenging work in data harmonization and these mechanisms need to be clarified.
Other suggestions:
1. The author claimed that the data was updated from Monfreda et al. (2008) dataset. Does it mean that the cropland area was the same as the dataset? How about the cropland expansion/abandonment from 2000 to 2020?
2. I checked the data and found that "no data" and "no crop" are both 0. Better to use a fixed value to flag the no data area.
Citation: https://doi.org/10.5194/essd-2023-130-RC2
Fiona H. M. Tang et al.
Data sets
CROPGRIDS Fiona H. M. Tang, Thu Ha Nguyen, Giulia Conchedda, Leon Casse, Francesco N. Tubiello, and Federico Maggi https://doi.org/10.6084/m9.figshare.22491997
Fiona H. M. Tang et al.
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