GCI30: a global dataset of 30 m cropping intensity using multisource remote sensing imagery

Abstract

Abstract. The global distribution of cropping intensity (CI) is essential to our understanding of agricultural land use management on Earth. Optical remote sensing has revolutionized our ability to map CI over large areas in a repeated and cost-efficient manner. Previous studies have mainly focused on investigating the spatiotemporal patterns of CI ranging from regions to the entire globe with the use of coarse-resolution data, which are inadequate for characterizing farming practices within heterogeneous landscapes. To fill this knowledge gap, in this study, we utilized multiple satellite data to develop a global, spatially continuous CI map dataset at 30 m resolution (GCI30). Accuracy assessments indicated that GCI30 exhibited high agreement with visually interpreted validation samples and in situ observations from the PhenoCam network. We carried out both statistical and spatial comparisons of GCI30 with six existing global CI estimates. Based on GCI30, we estimated that the global average annual CI during 2016–2018 was 1.05, which is close to the mean (1.09) and median (1.07) CI values of the existing six global CI estimates, although the spatial resolution and temporal coverage vary significantly among products. A spatial comparison with two satellite-based land surface phenology products further suggested that GCI30 was not only capable of capturing the overall pattern of global CI but also provided many spatial details. GCI30 indicated that single cropping was the primary agricultural system on Earth, accounting for 81.57 % (12.28×106 km2) of the world's cropland extent. Multiple-cropping systems, on the other hand, were commonly observed in South America and Asia. We found large variations across countries and agroecological zones, reflecting the joint control of natural and anthropogenic drivers on regulating cropping practices. As the first global-coverage, fine-resolution CI product, GCI30 is expected to fill the data gap for promoting sustainable agriculture by depicting worldwide diversity of agricultural land use intensity. The GCI30 dataset is available on Harvard Dataverse: https://doi.org/10.7910/DVN/86M4PO (Zhang et al., 2020).

The neglected roles of adjacent natural ecosystems in maintaining bacterial diversity in agroecosystems

“…Considering the substantial effect of agricultural intensification on ecological communities and biodiversity, we collected data about the total number of cropping cycles during 2016–2018 from GCI30 dataset (Zhang et al., 2021). GCI30 dataset provides the 30 m resolution cropping intensity dataset covering global extent.…”

Grassland ecosystems resilience to drought: The role of surface water ponds

“…For this study, we calculated the SVI using multi‐source remote sensing imagery. All available images of top‐of‐atmosphere (TOA) reflectance from LANDSAT‐7 ETM+, LANDSAT‐8 O.L.I., and SENTINEL‐2 5 MSI during 2001–2022 were used via the Google Earth Engine (GEE) platform, following the procedure described in Zhang et al (2021). Invalid observations, including clouds, cloud shadows, snow, and saturated values, were identified and masked based on the algorithms by Zhu and Woodcock (2012) and Qiu et al (2019).…”

“…Invalid observations, including clouds, cloud shadows, snow, and saturated values, were identified and masked based on the algorithms by Zhu and Woodcock (2012) and Qiu et al (2019). Following the work of Zhang et al (2021), we used the MODIS vegetation index (MOD13Q1) version 6 products (NDVI and EVI) and LSWI derived from MODIS surface reflectance (MOD09A1) version 6 products to fill data gaps caused by the lack of LANDSAT and SENTINEL‐2 observations. If there is no valid data from either LANDSAT and SENTINEL‐2 or MODIS, temporally adjacent (within 48 days) cloud‐free LANDSAT and SENTINEL‐2 observations were used to determine the filling value.…”

CROPGRIDS: A global geo-referenced dataset of 173 crops circa 2020

“…Finally, we set a threshold for CA and HA values, i.e., both were set to zero whenever CA < 1 m 2 . Consistency diagnostics checked that CA ≤ HA, CA ≤ GA, and CI ≤ 3 (i.e., CI commonly less than 3, Zhang et al, 2021) were satisfied in all grid cells. In building CROPGRIDS, we harmonized crop names in the input datasets, including performing aggregations where needed, to correspond to the crop names in MRF, thus ensuring internal consistency and alignment with FAO crop classifications (Supplementary Table 1).…”