Enhancing MODIS land cover product with a spatial–temporal modeling algorithm

Achieving food security in particular in Africa continues to pose a major challenge to humankind. It is clear that the future agricultural potential of Africa plays a critical role in meeting this challenge. Although crop yield can be estimated with a degree of reliability using a limited sample of ground observations, the exact crop acreage and the spatial distribution are rarely available. Even though remote sensing offers the ability to produce a rapid and up-to-date land use and land cover database for agricultural monitoring, there are only a few countries in Africa where higher resolution satellite data such as Landsat have been used for land cover map production at the national level. However a number of global products have been produced which contain information on cropland extent. This paper will outline a comparison of four sources of land cover data to determine which product is the most suitable for agricultural monitoring and for the subsequent development of a crop mask. The land cover products used are the Global Land Cover Map (GLC-2000), the Moderate Resolution Imaging Spectroradiometer (MODIS) land cover product (MOD12V1), the SAGE (Center for Sustainability and the Global Environment) and the AFRICOVER dataset from the Food and Agriculture Organisation (FAO). Both the GLC-2000 and MODIS land cover products are at a resolution of 1 km2 while AFRICOVER, based on visual interpretation of 30-m resolution Landsat images, is available at a much finer resolution. The four land cover products are first aggregated to the same resolution so that they can be compared. The legend categories of the four land cover products in this study are reconciled using the method developed in Fritz and See [Fritz, S. and See, L., 2005, Comparison of land cover maps using fuzzy agreement. International Journal of Geographic Information Science, 19, pp. 787–807.] and See and Fritz [See, L.M. and Fritz, S., 2005, A user-defined fuzzy logic approach to comparing global land cover products. In 14th European Colloquium on Theoretical and Quantitative Geography, 9–12 September 2005, Lisbon, Portugal.] that allows overlap between legend definitions to be taken into account. Once the legend definitions between the different land cover products are reconciled, the maps are then compared with national and sub-national statistics. Analysis is undertaken at both continental and national scales as well as sub-national for Sudan and Eritrea. The study generally concludes that MODIS has the tendency to underestimate cropland cover when compared with FAO statistics or AFRICOVER data, whereas GLC-2000 tends to overestimate cropland cover in those countries that are located at the northern transition zone of subtropical shrubland and semi-desert areas. In this area MODIS and SAGE show a relatively similar cropland distribution. Even though the SAGE database has been calibrated with national statistics, it does not perform better than the other two datasets overall, and has highlighted the fact that the SAGE data show regional weaknesses and should be replaced in certain regions by more recent datasets such as GLC-2000 and MODIS, or ideally by a hybrid product that combines the best of the three products, depending upon the region and country.

In South Asia, key differences in annual land use and land cover (LULC) take place due to climate change, global warming, human activity, biodiversity, and hydrology. So, it is very important to get accurate land cover information for this region. An annual LULC map that covers a comprehensive period is a major dataset for climatologically study. While yearly worldwide maps of LULC are produced from Moderate Resolution Imaging Spectroradiometer (MODIS) dataset, in 2001, the first LULC map of MODIS is generated which restrictions the perspective climatologically analysis. This research work generated a time series of yearly LULC maps of South Asia from 2001 to 2015 by using random forest classification from AVHRR GIMMS NDVI3g data. The MODIS land cover product such as (MCD12Q1) was used as a reference data for the trained classifier. The result was validated by using time series of annual LULC maps, and the spatiotemporal dynamic of LULC maps was illustrated in the last 15years from 2001 to 2015. The simplified sixteen class versions of our 15-year overall accuracy of a land cover map are 86.70%, and 1.23% higher than that of MODIS maps. The change detection indicated that, for the last 15years, the class of closed shrublands, savannas, croplands, urban and built-up land, barren, and cropland per natural vegetation mosaics increase notably during the 2001 to 2015, and in contrast, the class of woody savannas, evergreen needleleaf forests, open shrublands, grasslands, mixed forests, permanent wetlands, permanent snow and ice, evergreen broadleaf forests, and water bodies decrease notably during 2001 to 2015. These yearly land cover maps will be an essential dataset for the upcoming climate study, where time series of LULC maps accessibility is restricted.

Major forest changes and land cover transitions based on plant functional types derived from the ESA CCI Land Cover product

Abstract. 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.

Land cover type is a crucial parameter that is required for various land surface models that simulate water and carbon cycles, ecosystem dynamics, and climate change. Many land use/land cover maps used in recent years have been derived from field investigations and remote-sensing observations. However, no land cover map that is derived from a single source (such as satellite observation) properly meets the needs of land surface simulation in China. This article presents a decision-fuse method to produce a higher-accuracy land cover map by combining multi-source local data based on the Dempster–Shafer (D–S) evidence theory. A practical evidence generation scheme was used to integrate multi-source land cover classification information. The basic probability values of the input data were obtained from literature reviews and expert knowledge. A Multi-source Integrated Chinese Land Cover (MICLCover) map was generated by combining multi-source land cover/land use classification maps including a 1:1,000,000 vegetation map, a 1:100,000 land use map for the year 2000, a 1:1,000,000 swamp-wetland map, a glacier map, and a Moderate-Resolution Imaging Spectroradiometer land cover map for China in 2001 (MODIS2001). The merit of this new map is that it uses a common classification system (the International Geosphere-Biosphere Programme (IGBP) land cover classification system), and it has a unified 1 km resolution. The accuracy of the new map was validated by a hybrid procedure. The validation results show great improvement in accuracy for the MICLCover map. The local-scale visual comparison validations for three regions show that the MICLCover map provides more spatial details on land cover at the local scale compared with other popular land cover products. The improvement in accuracy is true for all classes but particularly for cropland, urban, glacier, wetland, and water body classes. Validation by comparison with the China Forestry Scientific Data Center (CFSDC)–Forest Inventory Data (FID) data shows that overall forest accuracies in five provinces increased to between 42.19% and 88.65% for our MICLCover map, while those of the MODIS2001 map increased between 27.77% and 77.89%. The validation all over China shows that the overall accuracy of the MICLCover map is 71%, which is higher than the accuracies of other land cover maps. This map therefore can be used as an important input for land surface models of China. It has the potential to improve the modeling accuracy of land surface processes as well as to support other aspects of scientific land surface investigations in China.

Nepal, located in a unique transition zone spanning from plains to mountains and then to the plateau, is characterized by diverse and complex land cover. Based on an object-oriented method and decision tree classifier, a land cover product covering the whole of Nepal in 2010 (hereinafter referred to as the NepalCover-2010) was produced using 30 m-resolution Landsat TM images, consisting of 8 classes at Level I and 31 classes at Level II. The accuracy of the NepalCover-2010 product at Level II was validated using samples collected from high-resolution Google Earth images. The result showed that the overall accuracy of the product was 87.17%, with a Kappa coefficient of 0.85, making it the most accurate product among similar land cover products. The product can accurately reflect the spatial patterns of land cover in Nepal. Forests are the main land cover classes, accounting for 41% of the land, followed by croplands covering about 25%. The areal proportion of paddy fields to dry farmlands was approximately two to three. Topographical and meteorological factors presented as the determining effects on the spatial patterns of land cover in Nepal. With elevation uplift from south to north, land cover classes showed a vertical zonality ordered thus: paddy fields, evergreen broadleaf forests, dry farmlands, evergreen broadleaf shrubs, evergreen needleleaf forests, grasslands, sparse vegetation, and permanent ice/snow. Land cover mapping in Nepal contributes significantly to the basic data collection in this country, and can also be of a benefit to China’s international regional economic cooperation strategy entitled “the Belt and Road Initiative”.

Mapping the annual dynamics of land cover in Beijing from 2001 to 2020 using Landsat dense time series stack

This paper provides a methodology for comparing global land cover maps that allows for differences in legend definitions between products to be taken into account. The legends of the two maps are first reconciled by creating a legend lookup table that shows how the legends map onto one another. Where there is overlap, the specific definitions for each legend class are used to calculate the degree of overlap between legend classes. In this way, one‐to‐many mappings are accounted for unlike in most methods where the legend definitions are often forced into place. Another advantage over previous map comparison methods is that application‐specific requirements are captured using expert input, whereby the user rates the importance of disagreement between different legend classes based on the needs of the application. This user‐defined matrix in conjunction with the degree of overlap between legend classes is applied on a pixel‐by‐pixel basis to create maps of spatial disagreement and uncertainty. The user can then highlight the areas of highest thematic uncertainty and disagreement between the different land cover maps allowing for areas that require further detailed examination to be readily identified. It would also be possible for several users to input their knowledge into the process, leading to a potentially more robust comparison of land cover products. The methodology of map comparison is illustrated using different land cover products including Global Land Cover 2000 (GLC‐2000) and the MODIS land cover data set. Two diverse applications are provided including the estimation of global forest cover and monitoring of agricultural land. In the case of global forest cover, an example was provided for Columbia, which showed that the MODIS land cover map overestimates forest cover in comparison with the GLC‐2000. The agricultural example, on the other hand, served to illustrate that for Sudan, MODIS tends to underestimate crop areas while GLC‐2000 overestimates them.

<p>The non-availability of annual land cover maps for Switzerland at the national and local levels hampers setting a Land Degradation Neutrality (LDN) baseline (2000 – 2015) and monitoring until the year 2030. Land cover change is an LDN sub-indicator required to assess the proportion of degraded land to total land area (Sustainable Development Goal 15 indicator — SDG15.3.1). The Swiss version of the European CORINE Land Cover (CLC), which is available for the years 1990, 2000, 2006, 2012 and 2018, is often used to analyse land cover and land cover change. However, the approximately 6-year production cycle of the CLC is only partially in line with the internationally agreed LDN baseline and monitoring periods.</p><p>Yet, annual land cover maps are required not only for baseline setting but crucial for monitoring the SDG15.3.1 on an annual basis until 2030. Further, most studies evaluating the effects of land cover and change often do not consider the impact of differing annual land cover configurations and sizes on their analysis results. Doing so is important, especially for the LDN, because of the pervasive influence land cover and its changes have on other sub-indicators such as Land Productivity Dynamics (LPD) and the change in Soil Organic Carbon stock, whose computation is currently based on land cover change.</p><p>Aimed at contributing to the scientific basis for operationalizing LDN for Switzerland, this study developed and applied a remote sensing-based method for generating land cover data. It took advantage of the availability of big spatial data (e.g., Sentinel-1 and Sentinel-2 satellite images, digital elevation model — DEM, CLC) to map land cover for the years 2015 and 2020, with reference datasets created with high resolution images. For 2015 and 2020, 100 training and validation points were created for each of the seven land cover classes (Forest, Grassland, Cropland, Wetland, Artificial Surfaces, and Otherland). Land cover change was calculated for the baseline period of 2000 – 2015 and 2016 – 2020 for the monitoring period. DEM was used in land cover classification, as topography was one of the main limiting factors for identifying land use types, particularly the need to reduce misclassifications of shady mountainous areas.</p><p>The methodology and results provide a basis for periodic assessments of the land cover dimension of the LDN and for automating the production of annual land cover maps for Switzerland. Evaluating the changes in land cover both for the baseline and monitoring periods also enabled us to separately analyze how changes in land cover differed between both periods. The land cover change for the monitoring period until 2020 was compared to the baseline, based on which we identify and discuss some challenges relating to land degradation in Switzerland.</p>

Precise global/regional land cover mapping is of fundamental importance in studies of land surface processes and modelling. Quantitative assessments of the map quality and classification accuracy for existing land cover maps will help to improve accuracy in future land cover mapping. We compare and evaluate four land cover datasets over China. The datasets include the Version 2 global land cover dataset of IGBP, MODIS land cover map 2001, a global land cover map produced by the University of Maryland, and the land cover map produced by the global land cover for the year 2000 (GLC 2000) project coordinated by the Global Vegetation Monitoring Unit of the European Commission Joint Research Centre. The four maps used different classification systems, which made the comparison difficult. So we first aggregated these maps by reclassifying them using a unified legend system. A large-scale, i.e. 1:100 000 land cover map of China was used as the reference data to validate the four maps. The results show that the GLC2000 land cover map represents the highest accuracy. However, it has obvious local labelling errors and a zero labelling accuracy for the wetland type. The MODIS land cover map ranks second for type area consistency and third for sub-fraction overall accuracy compared with reference data, which may be affected by the local labelling error. The IGBP land cover map has good labelling accuracy, although it has a local labelling error and third consistency for type area. The labelling accuracy and type area consistency for the reference data of UMd land cover map is low. We conclude that the accuracies of all the datasets cannot meet the requirements of land surface modelling. For the reference data, i.e. the 1:100 000 land cover map, the classification system needs to be transferred to a well recognized one that has been used commonly in land surface modelling. In addition, we propose an information fusion strategy to produce a more accurate land cover map of China whose classification system should be compatible with the well-accepted classification system used in land surface modelling.

China has experienced intense land use and land cover changes during the past several decades, which have exerted significant influences on climate change. Previous studies exploring related climatic effects have focused mainly on one or two specific land use changes, or have considered all land use and land cover change types together without distinguishing their individual impacts, and few have examined the physical processes of the mechanism through which land use changes affect surface temperature. However, in this study, we considered satellite-derived data of multiple land cover changes and transitions in China. The objective was to obtain observational evidence of the climatic effects of land cover transitions in China by exploring how they affect surface temperature and to what degree they influence it through the modification of biophysical processes, with an emphasis on changes in surface albedo and evapotranspiration (ET). To achieve this goal, we quantified the changes in albedo, ET, and surface temperature in the transition areas, examined their correlations with temperature change, and calculated the contributions of different land use transitions to surface temperature change via changes in albedo and ET. Results suggested that land cover transitions from cropland to urban land increased land surface temperature (LST) during both daytime and nighttime by 0.18 and 0.01 K, respectively. Conversely, the transition of forest to cropland tended to decrease surface temperature by 0.53 K during the day and by 0.07 K at night, mainly through changes in surface albedo. Decreases in both daytime and nighttime LST were observed over regions of grassland to forest transition, corresponding to average values of 0.44 and 0.20 K, respectively, predominantly controlled by changes in ET. These results highlight the necessity to consider the individual climatic effects of different land cover transitions or conversions in climate research studies. This short-term analysis of land cover transitions in China means our estimates should represent local temperature effects. Changes in ET and albedo explained <60% of the variation in LST change caused by land cover transitions; thus, additional factors that affect surface climate need consideration in future studies.

A time series of annual land use and land cover maps of China from 1982 to 2013 generated using AVHRR GIMMS NDVI3g data

Agroforestry, the integration of trees on productive agricultural land (Mosquera-Losada et al., 2018), can be identified through remote sensing methods by the combination of land cover and tree cover maps. Previous work has classified agroforestry as agricultural land with greater than 5% tree cover (Lawson et al., 2025; Zomer et al., 2016).However, there exist several regional, European, and global land cover and tree cover products that could be suitable for agroforestry identification, but these products vary in resolution, data inputs, and methodology of production. Our work benchmarked the performance of four land cover maps(Büttne et al., 2021; Karvatte et al., 2021; Schultz et al., 2025; UKCEH, 2022) and nine tree cover maps(Brandt et al., 2024; Copernicus, 2023, 2025; Hunter et al., 2025; Lang et al., 2023; Tolan et al., 2024; Weinstein et al., 2020) that were capable of mapping trees outside of woodlands. We evaluated the datasets’ ability to identify agroforestry on 25 agroforestry sites across the United Kingdom, including a mix of silvoarable and silvopastoral systems, as well as planting ages, densities, and species, as well as nearby agricultural (no trees) and woodland (no agriculture) control fields.We found that a number of datasets used in previous studies underperformed when distinguishing agroforestry from control fields as well as the previously utilised pixel-based approaches being unsuitable to identify agroforestry fields as a whole. Datasets with coarse resolutions (&gt;10m) often confused proximal small woodlands for trees within agricultural fields. Many datasets struggled to map trees in silvoarable systems, likely due to their linear arrangement differing from that of other trees outside of woodlands.In addition, the majority of datasets were unable to identify agroforestry sites planted since 2000, suggesting a 20-year lag in identification. The only tree identification method capable of identifying young sites was the fine-tuning of the DeepForest tree detection model (Weinstein et al., 2020) with local data, suggesting a need for tree cover datasets that are capable of identifying seedlings and saplings.We used the best-performing datasets (balanced accuracy 83-87%) to create a map of agroforestry, with quality flags to signify agreement between datasets. We conclude that there is 517,300 ha (3% of UAA) of area under agroforestry in the United Kingdom. Our map could have further use cases for calculating the uptake of agroforestry, as well as its benefits to people and nature, such as carbon storage, biodiversity impacts, farm income, and health of crops and livestock. We also conclude the need for model training on agroforestry trees, to both identify young trees and those with complex planting arrangements.

Limitations of mapping land surface properties and their conversion into climate model boundary conditions are major sources of uncertainty in climate simulations. In this paper, the range of the largest possible uncertainty in satellite-derived land cover (LC) map is estimated and its impact on climate simulations is quantified with the Earth System Model of the Max-Planck Institute for Meteorology utilizing prescribed sea surface temperature and sea ice. Two types of uncertainty in the LC map are addressed: (i) uncertainty due to classification algorithm of spectral reflectance into LC classes, and (ii) uncertainty due to conversion of LC classes into the climate model vegetation distribution. For forest cover, each of them is about the same order of magnitude as the uncertainty range in recent observations (∼± 700 Mha). Superposing two sources of uncertainty results in LC maps that feature the range of vegetation deviation that is about the same order of magnitude as the recent (since year 1700) forest loss due to agriculture (forest cover uncertainty range ∼± 1700 Mha). These uncertainties in vegetation distribution lead to noticeable variations in near-surface climate variables, local, regional, and global climate forcing. Temperature does not show significant uncertainty in global mean, but rather exhibits regional deviations with an opposite response to LC uncertainty that compensate each other in the global mean (e.g., albedo feedback controls temperature in boreal North America resulting in cooling (warming) with decrease (increase) of vegetation while evaporative cooling controls temperature in South America and sub-Saharan Africa resulting in cooling (warming) with increase (decrease) of vegetation). Large-scale circulation is also affected by the LC uncertainty, and consequently precipitation pattern as well. It is demonstrated that precipitation uncertainty in the monsoonal regions are about the same order of magnitude as in previous studies with idealized perturbations of vegetation. These findings indicate that the range of uncertainty in satellite-derived vegetation maps for climate models is about the same order of magnitude as the uncertainty in recent observations of forest cover or as the forest lost due to agriculture. Consequently, climate simulations have a similar range of uncertainty in variables representing near-surface climate as the observed climate change due to land use. Hence, more accurate methods are needed for mapping and converting LC properties into model vegetation in order to increase reliability of climate model simulations.

Difference and consistency of multi-sources land cover products in global large river estuaries