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Chinese pine has tremendous applications in many fields. Mapping the distribution of Chinese pine is of great importance for government decision-making and forest management. In order to extract Chinese pine on a large scale, efficient algorithms and open remote-sensing datasets are needed. It is widely believed that machine learning algorithms and medium-resolution remote-sensing datasets can work well for this purpose. Unfortunately, their performance for Chinese pine extraction has remained unclear until now. Therefore, this study aims to explore the ability of the different machine learning algorithms and open remote-sensing datasets for Chinese pine extraction over large areas on Google Earth Engine (GEE). So, based on the combination of three typical machine learning algorithms, namely deep neural network (DNN), support vector machine (SVM), random forest (RF), and three open medium-resolution remote-sensing datasets, namely Sentinel-2, Gaofen-1, and Landsat-8 OLI, 27 models are constructed and GEE, with its powerful computing ability, is used. The main findings are as follows: (1) DNN has the highest accuracy for Chinese pine extraction, followed by SVM and RF; DNN is more sensitive to spatial geometric information, while SVM and RF algorithms are more sensitive to spectral information. (2) Spectral indexes are helpful for improving the extraction accuracy of Chinese pine. The extraction accuracy by using Gaofen-1 dataset increases 7.6% after adding spectral indexes, while the accuracies by using Sentinel-2 and Landsat-8 datasets increase 1.8% and 1.9% after adding spectral indexes, respectively. (3) The extraction accuracy by using DNN and Sentinel-2 dataset with spectral indexes is the highest, with an overall accuracy of 94.4%. (4) The area of Chinese pine is 153.73 km2, accounting for 5.06% of the administrative area of Karaqin Banner, and it is convenient to extract Chinese pine on a large scale by using GEE.

A preliminary version of a global automatic burned-area (BA) algorithm at medium spatial resolution was developed in Google Earth Engine (GEE), based on Landsat or Sentinel-2 reflectance images. The algorithm involves two main steps: initial burned candidates are identified by analyzing spectral changes around MODIS hotspots, and those candidates are then used to estimate the burn probability for each scene. The burning dates are identified by analyzing the temporal evolution of burn probabilities. The algorithm was processed, and its quality assessed globally using reference data from 2019 derived from Sentinel-2 data at 10 m, which involved 369 pairs of consecutive images in total located in 50 20 × 20 km2 areas selected by stratified random sampling. Commissions were around 10% with both satellites, although omissions ranged between 27 (Sentinel-2) and 35% (Landsat), depending on the selected resolution and dataset, with highest omissions being in croplands and forests; for their part, BA from Sentinel-2 data at 20 m were the most accurate and fastest to process. In addition, three 5 × 5 degree regions were randomly selected from the biomes where most fires occur, and BA were detected from Sentinel-2 images at 20 m. Comparison with global products at coarse resolution FireCCI51 and MCD64A1 would seem to show to a reliable extent that the algorithm is procuring spatially and temporally coherent results, improving detection of smaller fires as a consequence of higher-spatial-resolution data. The proposed automatic algorithm has shown the potential to map BA globally using medium-spatial-resolution data (Sentinel-2 and Landsat) from 2000 onwards, when MODIS satellites were launched. This research was funded by the Vice-Rectorate for Research of the University of the Basque Country (UPV/EHU) through a doctoral fellowship (contract no. PIF17/96).

In contrast with current operational products of burned area, which are generally available one month after the fire, active fires are readily available, with potential application for early evaluation of approximate fire perimeters to support fire management decision making in near real time. While previous coarse-scale studies have focused on relating the number of active fires to a burned area, some local-scale studies have proposed the spatial aggregation of active fires to directly obtain early estimate perimeters from active fires. Nevertheless, further analysis of this latter technique, including the definition of aggregation distance and large-scale testing, is still required. There is a need for studies that evaluate the potential of active fire aggregation for rapid initial fire perimeter delineation, particularly taking advantage of the improved spatial resolution of the Visible Infrared Imaging Radiometer (VIIRS) 375 m, over large areas and long periods of study. The current study tested the use of convex hull algorithms for deriving coarse-scale perimeters from Moderate Resolution Imaging Spectroradiometer (MODIS) and Visible Infrared Imaging Radiometer Suite (VIIRS) active fire detections, compared against the mapped perimeter of the MODIS collection 6 (MCD64A1) burned area. We analyzed the effect of aggregation distance (750, 1000, 1125 and 1500 m) on the relationships of active fire perimeters with MCD64A1, for both individual fire perimeter prediction and total burned area estimation, for the period 2012–2108 in Mexico. The aggregation of active fire detections from MODIS and VIIRS demonstrated a potential to offer coarse-scale early estimates of the perimeters of large fires, which can be available to support fire monitoring and management in near real time. Total burned area predicted from aggregated active fires followed the same temporal behavior as the standard MCD64A1 burned area, with potential to also account for the role of smaller fires detected by the thermal anomalies. The proposed methodology, based on easily available algorithms of point aggregation, is susceptible to be utilized both for near real-time and historical fire perimeter evaluation elsewhere. Future studies might test active fires aggregation between regions or biomes with contrasting fuel characteristics and human activity patterns against medium resolution (e.g., Landsat and Sentinel) fire perimeters. Furthermore, coarse-scale active fire perimeters might be utilized to locate areas where such higher-resolution imagery can be downloaded to improve the evaluation of fire extent and impact Funding for this study was provided by CONAFOR/CONACYT Projects “CO2-2014-3-252620” and “CO-2018-2-A3-S-131553” for the development and enhancement of a Forest Fire Danger Prediction System for Mexico, funded by the Sectorial Fund for forest research, development and technological innovation “Fondo Sectorial para la investigación, el desarrollo y la innovación tecnológica forestal” SI

Urban areas play a very important role in global climate change. There is increasing need to understand global urban areas with sufficient spatial details for global climate change mitigation. Remote sensing imagery, such as medium resolution Landsat daytime multispectral imagery and coarse resolution Defense Meteorological Satellite Program/Operational Linescan System (DMSP/OLS) nighttime light imagery, has provided a powerful tool for characterizing and mapping cities, with advantages and disadvantages. Here we propose a framework to merge cloud and cloud shadow-free Landsat Normalized Difference Vegetation Index (NDVI) composite and DMSP/OLS Night Time Light (NTL) to characterize global urban areas at a 30 m resolution, through a Normalized Difference Urban Index (NDUI) to make full use of them while minimizing their limitations. We modify the maximum NDVI value multi-date image compositing method to generate the cloud and cloud shadow-free Landsat NDVI composite, which is critical for generating a global NDUI. Evaluation results show the NDUI can effectively increase the separability between urban areas and bare lands as well as farmland, capturing large scale urban extents and, at the same time, providing sufficient spatial details inside urban areas. With advanced cloud computing facilities and the open Landsat data archives available, NDUI has the potential for global studies at the 30 m scale.

Funding for Open Access provided by the UMD Libraries Open Access Publishing Fund., Monitoring surface albedo at medium-to-fine resolution (

Biomass burning is a worldwide phenomenon, which emits large amounts of carbon into the atmosphere and strongly influences the environment. Burned area is an important parameter in modeling the impacts of biomass burning on the climate and ecosystem. The Medium Resolution Spectral Imager (MERSI) onboard FengYun-3C (FY-3C) has shown great potential for burned area mapping research, but there is still a lack of relevant studies and applications. This paper describes an automated burned area mapping algorithm that was developed using daily MERSI data. The algorithm employs time-series analysis and multi-temporal 1000-m resolution data to obtain seed pixels. To identify the burned pixels automatically, region growing and Support Vector Machine) methods have been used together with 250-m resolution data. The algorithm was tested by applying it in two experimental areas, and the accuracy of the results was evaluated by comparing them to reference burned area maps, which were interpreted manually using Landsat 8 OLI data and the MODIS MCD64A1 burned area product. The results demonstrated that the proposed algorithm was able to improve the burned area mapping accuracy at the two study sites.

Forests play an important role in maintaining ecosystem services, especially in ecologically fragile areas such as the Loess Plateau (LP) in China. However, there is still great uncertainty in the spatial extent and distribution of forests in such a fragmented region. In order to examine the advantages and disadvantages of existing forest mapping products, we conducted a thorough accuracy assessment on the eight recent, medium resolution (30–50 m) products by using the LP in 2010 as the region of interest. These mapping products include Landsat and/or PALSAR images (including the forest products from GlobeLand30), FROM-GLC, Hansen, ChinaCover, NLCD-China, GLCF VCF, OU-FDL, and JAXA. The same validation data were used to assess and rank the accuracy of each product. Additionally, the spatial consistency of the different forest products and their dependence on the terrain were analyzed. The results showed that the overall accuracies of the eight forest products on the LP in 2010 were between 0.93 ± 0.003 and 0.97 ± 0.002 with a 95% confidence interval, and GlobeLand30 presented the highest overall accuracy (0.97 ± 0.002). Among them, the PALSAR-based products (OU-FDL and JAXA) indicated relatively high accuracies, while the six Landsat-based products showed a large diversity in the accuracy. According to the eight products, the total estimated forest area of the LP varied from 7.627 ± 0.077 to 10.196 ± 0.1 million ha with a 95% confidence interval. We also found that the consistency in the spatial distribution of forests between these maps: 1) increased substantially with increasing elevation until 2000m, but then decreased at higher elevations, and 2) showed mild variation along increasing slope, but had a slight rate of increase. Our findings implied that future forest mapping studies should consider topographical attributes such as elevation and slope in their final products. Our results are fundamental in guiding future applications of these existing forest maps.

The complex interactions among optically active substances in Case II waters make it difficult to associate the variability in spectral radiance (or reflectance) to any single component. In the present study, we developed a four end-member spectral decomposition model to estimate chlorophyll-a concentrations in a eutrophic shallow lake—Lake Taihu. The new model was constructed by simulated spectral data from Hydrolight and was successfully validated using both of simulated reflectance and in situ reflectance data. Using MEdium Resolution Imaging Spectrometer (MERIS) images, the accuracy of the new model was estimated and compared with other published models. According to the MERIS retrieved results, the spatial distribution of chlorophyll-a concentrations and its relationship with environment factors were analyzed. The application of the new model and its limits to estimate water surface chlorophyll-a concentrations in turbid lakes is also discussed.

Time series of vegetation biophysical variables (leaf area index (LAI), fraction canopy cover (FCOVER), fraction of absorbed photosynthetically active radiation (FAPAR), canopy chlorophyll content (CCC), and canopy water content (CWC)) were estimated from interpolated Sentinel-2 (S2-LIKE) surface reflectance images, for an agricultural region located in central Canada, using the Simplified Level 2 Product Prototype Processor (SL2P). S2-LIKE surface reflectance data were generated by blending clear-sky Sentinel-2 Multispectral Imager (S2-MSI) images with daily BRDF-adjusted Moderate Resolution Imaging Spectrometer images using the Prediction Smooth Reflectance Fusion Model (PSFRM), and validated using thirteen independent S2-MSI images (RMSE ≤ 6%). The uncertainty of S2-LIKE surface reflectance data increases with the time delay between the prediction date and the closest S2-MSI image used for training PSFRM. Vegetation biophysical variables from S2-LIKE products are validated qualitatively and quantitatively by comparison to the corresponding vegetation biophysical variables from S2-MSI products (RMSE = 0.55 for LAI, ~10% for FCOVER and FAPAR, and 0.13 g/m2 for CCC and 0.16 kg/m2 for CWC). Uncertainties of vegetation biophysical variables derived from S2-LIKE products are almost linearly related to the uncertainty of the input reflectance data. When compared to the in situ measurements collected during the Soil Moisture Active Passive Validation Experiment 2016 field campaign, uncertainties of LAI (0.83) and FCOVER (13.73%) estimates from S2-LIKE products were slightly larger than uncertainties of LAI (0.57) and FCOVER (11.80%) estimates from S2-MSI products. However, equal uncertainties (0.32 kg/m2) were obtained for CWC estimates using SL2P with either S2-LIKE or S2-MSI input data.

Based on simulated reflectance, deep convective clouds (DCC) can be used as an invariant target to monitor the radiometric response degradation of the FY-3A/MERSI (Medium Resolution Spectral Imager) reflective solar bands (RSBs). The long-term response of the MERSI RSBs can easily be predicted using a quadratic fit of the monthly DCC mean reflectance, except for bands 6 and 7, which suffer from instrument anomalies. DCC-based degradations show that the blue bands (λ < 500 nm) and water-vapor bands have degraded significantly, whereas for near-infrared bands, the total degradations in four years are within 3% (excluding bands 3 and 20). For most bands, the degradation rates are greatest during the first year in orbit and decrease over time. The FY-3A/MERSI degradation results derived from DCC are consistent within 2.5%, except for bands, 11, 18 and 19, when compared with Aqua/MODIS(Moderate Resolution Imaging Sepetroradiometer) inter-calibration, multi-site invariant earth target calibration and the CRCS(Chinese Radiometric Calibration Site) Dunhuang desert vicarious calibration methods. Overall, the 2σ/mean degradation uncertainty for most MERSI bands was within 3%, validating the temporal stability of the DCC monthly mean reflectances. The DCC method has reduced the degradation uncertainties for MERSI water vapor bands over other methods. This is a significant advantage of the DCC calibration method. The saturation of some MERSI bands may hinder the effectiveness of the DCC calibration approach.

This paper describes the combination of terrestrial vegetation observations from two sensors, providing a historical dataset used for an in-depth analysis of the corresponding spatio-temporal patterns. The Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) is an important variable suitable for regional to large-scale monitoring of climate impacts on vegetation. In this work, we create an extensive dataset of FAPAR using a 10-day product at $\sim$1 km resolution from September, 1997, to April, 2012, combining information from two sensors: the NASA/Sea-viewing Wide Field-of-view Sensor (SeaWiFS) and the European Space Agency (ESA)/Medium Resolution Imaging Spectrometer Instrument (MERIS). The proposed methodology reduces the noise, fills the gaps and corrects for the spurious trends in the data, providing a time-consistent coverage of FAPAR. We develop a fast merging method and evaluate its performance over Europe and the Horn of Africa., JRC.H.7-Climate Risk Management

A two stage burned scar detection approach is applied to produce a burned scar map for Mediterranean Europe using IRS-AWiFS imagery acquired at the end of the 2009 fire season. The first stage identified burned scar seeds based on a learning algorithm (Artificial Neural Network) coupled with a bootstrap aggregation process. The second stage implemented a region growing process to extend the area of the burned scars. Several ancillary datasets were used for the accuracy assessment and a final visual check was performed to refine the burned scar product. Training data for the learning algorithm were obtained from MODIS-based polygons, which were generated by the Rapid Damage Assessment module of the European Forest Fire Information System. The map produced from this research is the first attempt to increase the spatial detail of current burned scar maps for the Mediterranean region. The map has been analyzed and compared to existing burned area polygons from the European Forest Fire Information System. The comparison showed that the IRS-AWiFS-based burned scar map improved the delineation of burn scars; in addition the process identified a number of small burned scars that were not detected on lower resolution sensor data. Nonetheless, the results do not clearly support the improved capability for the detection of smaller burned scars. A number of reasons can be provided for the under-detection of burned scars, these include: the lack of a full coverage and cloud free imagery, the time lag between forest fires and image acquisition date and the occurrence of fires after the image acquisition dates. On the other hand, the limited spectral information combined with the presence of undetected cloud shadows and shaded slopes are reasons for the over-estimation of small burned scars.

The chlorophyll-a (Chla) products of seven processors developed for the Medium Resolution Imaging Spectrometer (MERIS) sensor were evaluated. The seven processors, based on a neural network and band height, were assessed over an optically complex water body with Chla concentrations of 8.10–187.40 mg∙m−3 using 10-year MERIS archival data. These processors were adopted for the Ocean and Land Color Instrument (OLCI) sensor. Results indicated that the four processors of band height (i.e. the Maximum Chlorophyll Index (MCI_L1); and Fluorescence Line Height (FLH_L1)); neural network (i.e. Eutrophic Lake (EUL); and Case 2 Regional (C2R)) possessed reasonable retrieval accuracy with root mean square error (R2) in the range of 0.42–0.65. However, these processors underestimated the retrieved Chla > 100 mg∙m−3, reflecting the limitation of the band height processors to eliminate the influence of non-phytoplankton matter and highlighting the need to train the neural network for highly turbid waters. MCI_L1 outperformed other processors during the calibration and validation stages (R2 = 0.65, Root mean square error (RMSE) = 22.18 mg∙m−3, the mean absolute relative error (MARE) = 36.88%). In contrast, the results from the Boreal Lake (BOL) and Free University of Berlin (FUB) processors demonstrated their inadequacy to accurately retrieve Chla concentration > 50 mg∙m−3, mainly due to the limitation of the training datasets that resulted in a high MARE for BOL (56.20%) and FUB (57.00%). Mapping the spatial distribution of Chla concentrations across Lake Kasumigaura using the seven processors showed that all processors—except for the BOL and FUB—were able to accurately capture the Chla distribution for moderate and high Chla concentrations. In addition, MCI_L1 and C2R processors were evaluated over 10-years of monthly measured Chla as they demonstrated the best retrieval accuracy from both groups (i.e. band height and neural network, respectively). The retrieved Chla of MCI_L1 was more accurate at tracking seasonal and annual variation in Chla than C2R, with only slight overestimation occurring during the springtime.