, but showed improved accuracy in agricultural areas and increased discrimination of small forest patches. Against lidar measurements, the Landsat-based estimates exhibited accuracy slightly less than that of the MODIS VCF (RMSE 016.8% for MODIS-based vs. 17.4% for Landsat-based estimates), but RMSE of Landsat estimates was 3.3 percentage points lower than that of the MODIS data in an agricultural region. The Landsat data retained the saturation artifact of the MODIS VCF at greater than or equal to 80% tree cover but showed greater potential for removal of errors through calibration to lidar, with post-calibration RMSE of 9.4% compared to 13.5% in MODIS estimates. Provided for free download at the Global Land Cover Facility (GLCF) website (www.landcover. org), the 30-m resolution GLCF tree cover dataset is the highest-resolution multitemporal depiction of Earth's tree cover available to the Earth science community.

Land use policies have turned southern China into one of the most intensively managed forest regions in the world, with actions maximizing forest cover on soils with marginal agricultural potential while concurrently increasing livelihoods and mitigating climate change. Based on satellite observations, here we show that diverse land use changes in southern China have increased standing aboveground carbon stocks by 0.11 ± 0.05 Pg C y −1 during 2002-2017. Most of this regional carbon sink was contributed by newly established forests (32%), while forests already existing contributed 24%. Forest growth in harvested forest areas contributed 16% and non-forest areas contributed 28% to the carbon sink, while timber harvest was tripled. Soil moisture declined significantly in 8% of the area. We demonstrate that land management in southern China has been removing an amount of carbon equivalent to 33% of regional fossil CO 2 emissions during the last 6 years, but forest growth saturation, land competition for food production and soil-water depletion challenge the longevity of this carbon sink service.

Spatiotemporally consistent data on global cropland extent is essential for tracking progress towards sustainable food production. In the present study, we present an analysis of global cropland area change for the first two decades of the twenty-first century derived from satellite data time-series. We estimate that, in 2019, the cropland area was 1,244 Mha with a corresponding total annual net primary production (NPP) of 5.5 Pg C year−1. From 2003 to 2019, cropland area increased by 9% and cropland NPP by 25%, primarily due to agricultural expansion in Africa and South America. Global cropland expansion accelerated over the past two decades, with a near doubling of the annual expansion rate, most notably in Africa. Half of the new cropland area (49%) replaced natural vegetation and tree cover, indicating a conflict with the sustainability goal of protecting terrestrial ecosystems. From 2003 to 2019, global per-capita cropland area decreased by 10% due to population growth. However, the per-capita annual cropland NPP increased by 3.5% as a result of intensified agricultural land use. The presented global, high-resolution, cropland map time-series supports monitoring of natural land appropriation at the local, national and international levels.

Deforestation is a major driver of climate change 1 and the major driver of biodiversity loss 1,2 . Yet the essential baseline for monitoring forest cover-the global area of forests-remains uncertain despite rapid technological advances and international consensus on conserving target extents of ecosystems 3 . Previous satellite-based estimates 4,5 of global forest area range from 32.1 × 10 6 km 2 to 41.4 × 10 6 km 2 . Here, we show that the major reason underlying this discrepancy is ambiguity in the term 'forest'. Each of the >800 o cial definitions 6 that are capable of satellite measurement relies on a criterion of percentage tree cover. This criterion may range from >10% to >30% cover under the United Nations Framework Convention on Climate Change 7 . Applying the range to the first global, high-resolution map of percentage tree cover 8 reveals a discrepancy of 19.3 × 10 6 km 2 , some 13% of Earth's land area. The discrepancy within the tropics alone involves a di erence of 45.2 Gt C of biomass, valued at US$1 trillion. To more e ectively link science and policy to ecosystems, we must now refine forest monitoring, reporting and verification to focus on ecological measurements that are more directly relevant to ecosystem function, to biomass and carbon, and to climate and biodiversity.Forests are the focus of efforts to mitigate harmful ecological and social impacts of land use, including agreements to reduce carbon dioxide emissions from deforestation and forest degradation (REDD+; refs 9-11). The goals are both scientific-to balance regional and global carbon budgets-as well as political, to reduce carbon emissions and stop species extinctions by defining national baselines and managing future anthropogenic change 12 .The Forest Resources Assessments (FRAs) of the United Nations Food and Agriculture Organization (FAO)-the authority for national and global accounting-recorded 40.8 × 10 6 km 2 of forest in 2000, equalling 31% of Earth's land area 13 . The FRAs rely on self-reporting by participating countries, raising concerns about subjectivity and consistency 14-16 . Although estimates from satellite images should provide a more objective base 9 , even these disagree significantly over the amount and distribution of forests worldwide. Figure 1 maps the consensus among eight global satellite data sets over the class 'forest' in or near the year 2000 (Methods). The densely canopied biomes of the tropical, temperate and boreal zones, and the treeless deserts, prairies and tundra show nearperfect agreement across all sources on the presence or absence of forests. Yet the data disagree over the planet's semi-arid savannahs, shrublands and woodlands, and over the northern limits of the boreal forest. Although 102.2 × 10 6 km 2 show perfect consensus among the eight data sets on either the presence or absence of forests, 9.4 × 10 6 km 2 were identified as forest by four out of the eight sources. These sparsely forested regions are the areas of greatest remaining uncertainty.There are two reasons for the uncertaint...

The compilation of global Landsat data-sets and the ever-lowering costs of computing now make it feasible to monitor the Earth's land cover at Landsat resolutions of 30 m. In this article, we describe the methods to create global products of forest cover and cover change at Landsat resolutions. Nevertheless, there are many challenges in ensuring the creation of high-quality products. And we propose various ways in which the challenges can be overcome. Among the challenges are the need for atmospheric correction, incorrect calibration coefficients in some of the data-sets, the different phenologies between compilations, the need for terrain correction, the lack of consistent reference data for training and accuracy assessment, and the need for highly automated characterization and change detection. We propose and evaluate the creation and use of surface reflectance products, improved selection of scenes to reduce phenological differences, terrain illumination correction, automated training selection, and the use of information extraction procedures robust to errors in training data along with several other issues. At several stages we use Moderate Resolution Spectroradiometer data and products to assist our analysis. A global working prototype product of forest cover and forest cover change is included.