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Gross primary production (GPP) and evapotranspiration (ET) are the key variables in global carbon and water cycle, respectively. Therefore, it is important to understand how they respond to land use and land cover change (LUCC). Northern China has experienced dramatic LUCC because of a large‐scale ecological restoration project implemented since 1999. This study uses a diagnostic model (PML‐V2) driven by satellite data to quantify LUCC‐driven changes in GPP and ET, at 500 m resolution in northern China from 2004 to 2017. The results indicate that the GPP and ET of northern China has increased by 164 TgC year−1 and 13 km3 year−1. The GPP and ET are strongly increased in the Loess Plateau and Northeast China Plain in the research period, especially after 2009. Cropland has the highest GPP change of 184 gC m−2 year−1 was in Loess Plateau, followed by the Northeast China Plain (128 gC m−2 year−1) and then the Inner Mongolia Plateau (82 gC m−2 year−1). The highest ET increase of 45 mm year−1 occurs in shrubland in the Loess Plateau, followed by the increase of 20 mm year−1 in the Northeast China Plain. The increase in leaf area index is the major cause of GPP and ET increases for these regions. Our results suggest that it is necessary to carefully plan the afforestation and other land use change patterns for sustainable water resources management. Key Points: LUCC‐driven GPP and ET changes estimated using PML‐V2 and continuous annual MODIS land cover data setThe Loess Plateau shows the strongest increase in GPP and ET that are mainly driven by increase in leaf area indexStronger increases in GPP and ET are found for the post‐2009 period, compared to the pre‐2009 period [ABSTRACT FROM AUTHOR]

Accurate and up-to-date land use land cover (LULC) mapping has long been a challenge in Africa. Recently, three LULC maps with moderate spatial resolution (20 m to 100 m) have been developed using multiple Earth observation datasets for 2015–2016 for the whole continent, which provide unprecedented spatial detail of the land surface for Africa. This study aimed to compare these three recent African LULC maps (i.e. the Copernicus Global Land Service Land Cover (CGLS-LC100, 100 m), European Space Agency Sentinel-2A Land Cover (ESA-S2-LC20, 20 m) and Finer Resolution Observation and Monitoring of Global Land Cover for Africa version 2 (FROM-GLC-Africa30, 30 m)) using a validation sample set and statistics from the FAO. The results indicated that the accuracy of the three datasets was unevenly distributed in spatial extent and area estimation. All the three datasets achieve an accuracy of above 60% and the fraction layer of CGLS-LC100 showed the best consistency with FAO statistics in the area. However, great disagreement in spatial details was found among three products, with 43.12% of the total area in Africa was in low agreement. The LULC mapping regions with the highest uncertainty were southeast Africa, the Sahel region and the Eastern Africa Plateau. Uncertainty was most closely related to elevation and precipitation changes along latitude/longitude. [ABSTRACT FROM AUTHOR]

A technically transparent and freely available reference sample set for validation of global land cover mapping was recently established to assess the accuracies of land cover maps with multiple resolutions. This sample set can be used to estimate areas because of its equal-area hexagon-based sampling design. The capabilities of these sample set-based area estimates for cropland were investigated in this paper. A 30-m cropland map for China was consolidated using three thematic maps (cropland, forest and wetland maps) to reduce confusion between cropland and forest/wetland. We compared three area estimation methods using the sample set and the 30 m cropland map. The methods investigated were: (1) pixel counting from a complete coverage map, (2) direct estimation from reference samples, and (3) model-assisted estimation combining the map with samples. Our results indicated that all three methods produced generally consistent estimates which agreed with cropland area measured from an independent national land use dataset. Areas estimated from the reference sample set were less biased by comparing with a National Land Use Dataset of China (NLUD-C). This study indicates that the reference sample set can be used as an alternative source to estimate areas over large regions. [ABSTRACT FROM AUTHOR]

High-quality training and validation samples are critical components of land-cover and land-use mapping tasks in remote sensing. For large area mapping it is much more difficult to build such sample sets due to the huge amount of work involved in sample collection and image processing. As more and more satellite data become available, a new trend emerges in land-cover mapping that takes advantage of images acquired beyond the greenest season. This has created the need for constructing sample sets that can be used in classifying images of multiple seasons. On the other hand, seasonal land-cover information is also becoming a new demand in land and climate change studies. Here we produce the first training and validation data sets with seasonal labels in order to support the production of seasonal land-cover data for entire Africa. Nonetheless, for the first time, two classification systems were created for the same set of samples. We adapted the finer resolution observation and monitoring of global land cover (FROM-GLC) and the Food and Agriculture Organization (FAO) Land Cover Classification System legends. Locations of training-sample units of FROM-GLC were repurposed here. Then we designed a process to enlarge the training-sample units to increase the density of samples in the feature space of spectral characteristics of Moderate Resolution Imaging Spectroradiometer (MODIS) time-series and Landsat imagery. Finally, we obtained 15,799 training-sample units and 7430 validation-sample units. The land-cover type at each point was recorded at the time of maximum greenness in addition to four seasons in a year. Nearly half of the sample units were also suitable for 500 m resolution MODIS data. We analysed the representativeness of the training and validation sets and then provided some suggestions about their use in improving classification accuracies of Africa. [ABSTRACT FROM AUTHOR]

The long record of Landsat imagery, which is the cornerstone of Earth observation, provides an opportunity to monitor land use and land cover (LULC) change and understand the interactions between the climate and earth system through time. A few change detection algorithms such as Continuous Change Detection and Classification (CCDC) have been developed to utilize all available Landsat images for change detection and characterization at local or global scales. However, the reliable, rapid, and reproducible collection of training samples have become a challenge for time series land cover classification at a large scale. To meet the challenge, we proposed an automatic phenology learning (APL) method with the assumption that the temporal profiles of samples within the same land cover type are the same or similar at a local scale to generate evenly distributed training samples automatically. We designed the method to build land cover patterns for each category based on consensus samples derived from multiple existing scientific datasets including LANDFIRE's (LF) Existing Vegetation Type (EVT), USGS National Land Cover Database (NLCD), National Agricultural Statistics Service (NASS) Cropland Data Layer (CDL), and National Wetlands Inventory (NWI). Then we calculated the Time-Weighted Dynamic Time Warping (twDTW) distance between any undefined samples and land cover patterns in the same geographical region as prior knowledge. Finally, we selected the optimal land cover category for each undefined sample from the land cover products based on the designed criteria iteratively using the twDTW distance as an indicator. The method was applied in the footprint of 10 selected Landsat Analysis Ready Data (ARD) tiles in the eastern and western conterminous United States (CONUS) to produce annual land cover maps from 1985 to 2017. The accuracy assessment and visual comparison revealed that the APL method can generate reliable training samples without any manual interpretation, producing better land cover results especially for the grass/shrub and wetland land cover classes. Applying the APL method, the overall accuracy of the annual land cover maps was improved by 2% over the accuracy of Land Change Monitoring, Assessment, and Projection (LCMAP) Collection 1.0 Science Products in the research regions. Our results also indicate that the APL method provides an approach for best use of different land cover products and meets the requirement of intensive sampling for training data collection. • We developed a phenology learning method for generating training data automatically. • The method incorporates time-series analysis and multiple scientific products. • The method can generate reliable training samples without any manual interpretation. • The method meets the requirement of intensive sampling for training data collection. • Annual land cover maps achieved by the method are better than LCMAP C1.0 Products. [ABSTRACT FROM AUTHOR]

An integrated simulation-optimization modeling system (ISOMS) approach was developed for assessing adaptive strategies in response to coupled impacts of climate and landuse variations. The ISOMS can not only reflect future hydrological trends under changing environment, but also provide water-allocation plans under various uncertainties expressed as random or fuzzy feature systematically. A case study of Anning River Basin with dry and warm characteristics in the upper Yangtze River was applied to inspect the model's applicability. A range of alternatives to adaptive strategies were generated under combinations of climate and landuse scenarios with different satisfaction levels. Results reveal that: (i) there exist increasing trend of temperature and precipitation in future period (2021–2050); (ii) the streamflow variation is more sensitive to climate change than landuse change; (iii) the hydrologic system uncertainties would lead to changes in water resources allocation; (iv) a low level of uncertainty satisfaction or a high level of violation risk would reduce the system reliability for the water resources system. The ISOMS approach has tremendous significance for evaluating hydrologic variations with complicated uncertainties, and providing optimal water allocation schemes responding to the coupled impacts of climate and landuse variations among society, economy and environment. • An integrated simulation-based optimization modeling on MIKE SHE and MFSCP method. • A set of 31 GCMs and three CA-Markov modules for climate and land use projections. • Hydrological process and water availability vary under changing conditions. • Adaptive strategies for water resources generated under multiple scenarios. [ABSTRACT FROM AUTHOR]

Land use change and climate change are the main drivers affecting hydrological regime in Anning river basin, a tributary with the largest sand generation in the upper Yangtze River. However, their contribution to the variation of flow in the subbasin keeps unclear. Firstly, the gauged precipitation, streamflow and sand data were used to analyze their trends of variation since 1980s and land use and land cover change in four representative years were studied. Then semi-distributed hydrological model MIKESHE was used to simulate the ecological and hydrological status of several scenarios reflecting the different changes of climate and land use. The results showed that the runoff of Anning River increased and the its variation decreased, which was consistent to the loss of forest cover. The comparison among scenarios showed that both climate change and land cover change contributed to the variation of flow. The land cover change exerted more influence on the variation during 1990s, which might be the main reason why the Anning River became the one that had the largest sand generation in that time. As hydrological impacts of land use change and climate change may be temporally varied, it is requisite to manage water resources adaptively to address future climate change and water resources shortage. [ABSTRACT FROM AUTHOR]