The existing medium-resolution land cover time series produced under the European Space Agency's Climate Change Initiative provides 29 years (1992–2020) of annual land cover maps at 300 m resolution, ...allowing for a detailed study of land change dynamics over the contemporary era. Because models need two-dimensional parameters rather than two-dimensional land cover information, the land cover classes must be converted into model-appropriate plant functional types (PFTs) to apply this time series to Earth system and land surface models. The first-generation cross-walking table that was presented with the land cover product prescribed pixel-level PFT fractional compositions that varied by land cover class but that lacked spatial variability. Here we describe a new ready-to-use data product for climate modelling: spatially explicit annual maps of PFT fractional composition at 300 m resolution for 1992–2020, created by fusing the 300 m medium-resolution land cover product with several existing high-resolution datasets using a globally consistent method. In the resulting data product, which has 14 layers for each of the 29 years, pixel values at 300 m resolution indicate the percentage cover (0 %–100 %) for each of 14 PFTs, with pixel-level PFT composition exhibiting significant intra-class spatial variability at the global scale. We additionally present an updated version of the user tool that allows users to modify the baseline product (e.g. re-mapping, re-projection, PFT conversion, and spatial sub-setting) to meet individual needs. Finally, these new PFT maps have been used in two land surface models – Organising Carbon and Hydrology in Dynamic Ecosystems (ORCHIDEE) and the Joint UK Land Environment Simulator (JULES) – to demonstrate their benefit over the conventional maps based on a generic cross-walking table. Regional changes in the fractions of trees, short vegetation, and bare-soil cover induce changes in surface properties, such as the albedo, leading to significant changes in surface turbulent fluxes, temperature, and vegetation carbon stocks. The dataset is accessible at https://doi.org/10.5285/26a0f46c95ee4c29b5c650b129aab788 (Harper et al., 2023).
Land-use and land-cover change (LULCC) impacts local energy and
water balance and contributes on global scale to a net carbon
emission to the atmosphere. The newly released annual ESA CCI (climate ...change initiative) land
cover maps provide continuous land cover changes at 300 m
resolution from 1992 to 2015, and can be used in land surface models
(LSMs) to simulate LULCC effects on carbon stocks and on surface
energy budgets. Here we investigate the absolute areas and gross and
net changes in different plant functional types (PFTs) derived from
ESA CCI products. The results are compared with other
datasets. Global areas of forest, cropland and grassland PFTs from
ESA are 30.4, 19.3 and 35.7 million km2 in the year 2000. The
global forest area is lower than that from LUH2v2h (Hurtt et al.,
2011), Hansen et al. (2013) or Houghton and Nassikas (2017) while
cropland area is higher than LUH2v2h (Hurtt et al., 2011), in which
cropland area is from HYDE 3.2 (Klein Goldewijk et al., 2016). Gross
forest loss and gain during 1992–2015 are 1.5 and
0.9 million km2 respectively, resulting in a net forest
loss of 0.6 million km2, mainly occurring in South and
Central America. The magnitudes of gross changes in forest, cropland
and grassland PFTs in the ESA CCI are smaller than those in other
datasets. The magnitude of global net cropland gain for the whole
period is consistent with HYDE 3.2 (Klein Goldewijk et al., 2016),
but most of the increases happened before 2004 in ESA and after
2007 in HYDE 3.2. Brazil, Bolivia and Indonesia are the countries
with the largest net forest loss from 1992 to 2015, and the
decreased areas are generally consistent with those from Hansen
et al. (2013) based on Landsat 30 m resolution
images. Despite discrepancies compared to other datasets, and
uncertainties in converting into PFTs, the new ESA CCI products
provide the first detailed long-term time series of land-cover change and
can be implemented in LSMs to characterize recent carbon dynamics,
and in climate models to simulate land-cover change feedbacks on
climate. The annual ESA CCI land cover products can be downloaded
from http://maps.elie.ucl.ac.be/CCI/viewer/download.php (Land
Cover Maps – v2.0.7; see details in Sect. 5). The PFT map
translation protocol and an example in 2000 can be downloaded from
https://doi.org/10.5281/zenodo.834229. The annual ESA CCI PFT maps from
1992 to 2015 at 0.5∘×0.5∘ resolution can
also be downloaded from https://doi.org/10.5281/zenodo.1048163.
The mapping of water bodies at global scale has been undertaken primarily using multi-spectral optical Earth Observation data. Limitations of optical data associated with non-uniform and temporally ...variable spectral signatures suggested investigating alternative approaches towards a more consistent and reliable detection of water bodies. Multi-year (2005–2012) observations of SAR backscattered intensities at moderate resolution from the Envisat Advanced Synthetic Aperture Radar (ASAR) instrument were used in this study to generate an indicator of open permanent water bodies (SAR-WBI) for the year 2010 time frame and for all land surfaces excluding Antarctica and the Greenland ice sheet. A first map of potential water bodies with a spatial resolution of 150m was obtained with a global detection algorithm based on a set of thresholds applied to multi-temporal metrics of the SAR backscatter (temporal variability, TV, and minimum backscatter, MB). Local refinements were then used to reduce systematic commission and omission errors (4.6% of the total area mapped) due to the similarity of TV and MB over open water bodies and other land surface types primarily in cold and arid environments. The refinement rules are here explained by means of a detailed signature analysis of the SAR backscatter in such environments. The accuracy of the SAR-WBI was 80% when compared against 2078 manually interpreted footprints with a size of 150×150m2. Omission errors were primarily observed along coast- and shorelines whereas commission errors were associated with (i) ephemeral water bodies, (ii) seasonally inundated areas, and (iii) an incorrect choice of the local refinement.
•Global indicator of open permanent water bodies from multi-year Envisat ASAR data•Set of thresholding rules applied to multi-temporal metrics of ASAR backscatter•Similar metrics over water bodies and land surfaces of cold and arid environments•Need of auxiliary datasets to refine classification based on ASAR data only•Accuracy of the SAR-based indicator of water bodies: 80%
Land cover and land use maps derived from satellite remote sensing imagery are critical to support biodiversity and conservation, especially over large areas. With its 10 m to 20 m spatial ...resolution, Sentinel-2 is a promising sensor for the detection of a variety of landscape features of ecological relevance. However, many components of the ecological network are still smaller than the 10 m pixel, i.e., they are sub-pixel targets that stretch the sensor's resolution to its limit. This paper proposes a framework to empirically estimate the minimum object size for an accurate detection of a set of structuring landscape foreground/background pairs. The developed method combines a spectral separability analysis and an empirical point spread function estimation for Sentinel-2. The same approach was also applied to Landsat-8 and SPOT-5 (Take 5), which can be considered as similar in terms of spectral definition and spatial resolution, respectively. Results show that Sentinel-2 performs consistently on both aspects. A large number of indices have been tested along with the individual spectral bands and target discrimination was possible in all but one case. Overall, results for Sentinel-2 highlight the critical importance of a good compromise between the spatial and spectral resolution. For instance, the Sentinel-2 roads detection limit was of 3 m and small water bodies are separable with a diameter larger than 11 m. In addition, the analysis of spectral mixtures draws attention to the uneven sensitivity of a variety of spectral indices. The proposed framework could be implemented to assess the fitness for purpose of future sensors within a large range of applications.
Land cover is one of the essential climate variables of the ESA Climate Change Initiative (CCI). In this context, the Land Cover CCI (LC CCI) project aims at building global land cover maps suitable ...for climate modeling based on Earth observation by satellite sensors. The challenge is to generate a set of successive maps that are both accurate and consistent over time. To do so, operational methods for the automated classification of optical images are investigated. The proposed approach consists of a locally trained classification using an automated selection of training samples from existing, but outdated land cover information. Combinations of local extraction (based on spatial criteria) and self-cleaning of training samples (based on spectral criteria) are quantitatively assessed. Two large study areas, one in Eurasia and the other in South America, are considered. The proposed morphological cleaning of the training samples leads to higher accuracies than the statistical outlier removal in the spectral domain. An optimal neighborhood has been identified for the local sample extraction. The results are coherent for the two test areas, showing an improvement of the overall accuracy compared with the original reference datasets and a significant reduction of macroscopic errors. More importantly, the proposed method partly controls the reliability of existing land cover maps as sources of training samples for supervised classification.
Accurate maps of surface water extent are of paramount importance for water management, satellite data processing and climate modeling. Several maps of water bodies based on remote sensing data have ...been released during the last decade. Nonetheless, none has a truly (90 compfn N/90 compfn S) global coverage while being thoroughly validated. This paper describes a global, spatially-complete (void-free) and accurate mask of inland/ocean water for the 2000-2012 period, built in the framework of the European Space Agency (ESA) Climate Change Initiative (CCI). This map results from the synergistic combination of multiple individual SAR and optical water body and auxiliary datasets. A key aspect of this work is the original and rigorous stratified random sampling designed for the quality assessment of binary classifications where one class is marginally distributed. Input and consolidated products were assessed qualitatively and quantitatively against a reference validation database of 2110 samples spread throughout the globe. Using all samples, overall accuracy was always very high among all products, between 98% and 100% . The CCI global map of open water bodies provided the best water class representation (F-score of 89% ) compared to its constitutive inputs. When focusing on the challenging areas for water bodies' mapping, such as shorelines, lakes and river banks, all products yielded substantially lower accuracy figures with overall accuracies ranging between 74% and 89% . The inland water area of the CCI global map of open water bodies was estimated to be 3.17 million km 2 plus or minus 0.24 million km 2 . The dataset is freely available through the ESA CCI Land Cover viewer.
This article aims at investigating the hidden Markov model (HMM) approach for the automated processing of classified satellite images for land cover and land-use change (LCLUC). HMM's account for ...transitions between classes at the same location, but that cannot be directly observed due to classification errors. Using a set of transition and emission probabilities, HMM's allow filtering out errors and recovering the actual sequence of LCLUC, which are typically overestimated when directly estimated from the classified images. After presenting the HMM framework, the methodology is illustrated on three 300-m annual time series of classified images from 2003 to 2019 over <inline-formula> <tex-math notation="LaTeX">756\times756 </tex-math></inline-formula> km 2 areas in Brazil, People's Republic of China, and Mali. It is shown how the emission and transition probabilities can be estimated from these time series using a simple Viterbi training, alleviating computationally demanding algorithms. Special attention is paid to the processing of missing observations caused by clouds. Combining these three datasets with a simulation study, it is concluded that the HMM emission and transition probabilities can be estimated with low biases and variances thanks to the vast number (hundreds of thousands) of pixels at hand. The speed of the Viterbi training and decoding steps makes it possible to consider large-scale land cover mapping at moderate or even high spatial resolution as long as the legend of the LCLUC involves a reasonable number of classes like the six main Intergovernmental Panel on Climate Change (IPCC) land categories.
Global land cover information is required to initialize land surface and Earth system models. In recent years, new land cover (LC) datasets at finer spatial resolutions have become available while ...those currently implemented in most models are outdated. This study assesses the applicability of the Climate Change Initiative (CCI) LC product for use in the Canadian Land Surface Scheme (CLASS) through comparison with finer resolution datasets over Canada, assisted with reference sample data and a vegetation continuous field tree cover fraction dataset. The results show that in comparison with the finer resolution maps over Canada, the 300 m CCI product provides much improved LC distribution over that from the 1 km GLC2000 dataset currently used to provide initial surface conditions in CLASS. However, the CCI dataset appears to overestimate needleleaf forest cover especially in the taiga-tundra transition zone of northwestern Canada. This may have partly resulted from limited availability of clear sky MEdium Resolution Imaging Spectrometer (MERIS) images used to generate the CCI classification maps due to the long snow cover season in Canada. In addition, changes based on the CCI time series are not always consistent with those from the MODIS or a Landsat-based forest cover change dataset, especially prior to 2003 when only coarse spatial resolution satellite data were available for change detection in the CCI product. It will be helpful for application in global simulations to determine whether these results also apply to other regions with similar landscapes, such as Eurasia. Nevertheless, the detailed LC classes and finer spatial resolution in the CCI dataset provide an improved reference map for use in land surface models in Canada. The results also suggest that uncertainties in the current cross-walking tables are a major source of the often large differences in the plant functional types (PFT) maps, and should be an area of focus in future work.
The global expansion of agricultural land is a leading driver of climate change and biodiversity loss. However, the spatial resolution of current global land change models is relatively coarse, which ...limits environmental impact assessments. To address this issue, we developed global maps representing the potential for conversion into agricultural land at a resolution of 10 arc-seconds (approximately 300 m at the equator). We created the maps using artificial neural network (ANN) models relating locations of recent past conversions (2007–2020) into one of three cropland categories (cropland only, mosaics with >50% crops, and mosaics with <50% crops) to various predictor variables reflecting topography, climate, soil, and accessibility. Cross-validation of the models indicated good performance with area under the curve (AUC) values of 0.88–0.93. Hindcasting of the models from 1992 to 2006 revealed a similar high performance (AUC of 0.83–0.91), indicating that our maps provide representative estimates of current agricultural conversion potential provided that the drivers underlying agricultural expansion patterns remain the same. Our maps can be used to downscale projections of global land change models to more fine-grained patterns of future agricultural expansion, which is an asset for global environmental assessments.
Different aspects of the Earth's surface, including land cover and land cover change, are now mapped at a global scale regularly. The endorsement of one cartographic product among all by users ...depends notably on their quality. Recommendations on their validation were established by the Committee on Earth Observation Satellites Working Group on Calibration and Validation. These have been applied commonly to the validation of global land cover products. The validation of land cover change at the global scale is still in its early stages. Clear recommendations on the systematic comparison of products against each other have yet to be defined and recognized as standards. Here, we share the lessons learned from the European Space Agency Climate Change Initiative Medium and High-Resolution land cover projects in applying these recommendations and tailoring sampling schemes to the validation of the land cover itself, the land cover change and inter-product comparisons.
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