Rodriguez-Veiga, PedroAvitabile, ValerioSantoro, MaurizioMitchard, Edward T. A....
16页查看更多>>摘要：Over the past decade, several global maps of above-ground biomass (AGB) have been produced, but they exhibit significant differences that reduce their value for climate and carbon cycle modelling, and also for national estimates of forest carbon stocks and their changes. The number of such maps is anticipated to increase because of new satellite missions dedicated to measuring AGB. Objective and consistent methods to estimate the accuracy and uncertainty of AGB maps are therefore urgently needed. This paper develops and demonstrates a framework aimed at achieving this. The framework provides a means to compare AGB maps with AGB estimates from a global collection of National Forest Inventories and research plots that accounts for the uncertainty of plot AGB errors. This uncertainty depends strongly on plot size, and is dominated by the combined errors from tree measurements and allometric models (inter-quartile range of their standard deviation (SD) = 30-151 Mg ha(-1)). Estimates of sampling errors are also important, especially in the most common case where plots are smaller than map pixels (SD = 16-44 Mg ha(-1)). Plot uncertainty estimates are used to calculate the minimum-variance linear unbiased estimates of the mean forest AGB when averaged to 0.1 degrees. These are used to assess four AGB maps: Baccini (2000), GEOCARBON (2008), GlobBiomass (2010) and CCI Biomass (2017). Map bias, estimated using the differences between the plot and 0.1 degrees map averages, is modelled using random forest regression driven by variables shown to affect the map estimates. The bias model is particularly sensitive to the map estimate of AGB and tree cover, and exhibits strong regional biases. Variograms indicate that AGB map errors have map-specific spatial correlation up to a range of 50-104 km, which increases the variance of spatially aggregated AGB map estimates compared to when pixel errors are independent. After bias adjustment, total pantropical AGB and its associated SD are derived for the four map epochs. This total becomes closer to the value estimated by the Forest Resources Assessment after every epoch and shows a similar decrease. The framework is applicable to both local and global-scale analysis, and is available at https://github.com/arnanaraza/PlotToMap. Our study therefore constitutes a major step towards improved AGB map validation and improvement.
原文链接:
NSTL
Elsevier
Dupiau, A.Briottet, X.Fabre, S.Viallefont-Robinet, F....
14页查看更多>>摘要：This paper presents MARMIT-2, a radiative transfer model that predicts the spectral reflectance of soils in the solar domain (0.4-2.5 mu m) as a function of their surface moisture. This is an improved version of MARMIT (multilayer radiative transfer model of soil reflectance) that represents a wet soil as a dry soil topped with a thin layer of liquid water. The changes brought in this article concern the mixing of the spectral reflectance of the dry and wet soil areas, the transmission of diffuse light in the water layer, and the inclusion of soil particles in the water layer. Wet soil reflectance is now expressed in terms of dry soil reflectance and three free parameters: the thickness of the water layer, the surface fraction of the wet soil, and a new parameter, the volume fraction of soil particles in the water layer. With more accurate physical modeling, MARMIT-2 simulates soil spectral reflectance with better accuracy than MARMIT. In particular, the fit of the soil reflectance spectra is much better for high water contents, both in the visible range (0.4-0.7 mu m) and in the water absorption bands around 1.45 mu m and 1.95 mu m. The average root mean square error between measured and predicted reflectance obtained on a set of 225 soil samples is about 0.8% with MARMIT-2 versus 1.8% with MARMIT.
原文链接:- NSTL
- Elsevier
Norton, Alexander J.Rayner, Peter J.Wang, Ying-PingParazoo, Nicholas C....
14页查看更多>>摘要：Vegetation growth drives many of the interactions between the land surface and atmosphere including the uptake of carbon through photosynthesis and loss of water through transpiration. In arid and semi-arid regions water is the dominant driver of vegetation growth. However, few studies consider the fact that water can move laterally across the landscape as runoff via streams and floodplains, termed hydrologic connectivity. Using multiple observations alongside models and a hydromorphology dataset for Australia, we examine how ecosystems with high hydrologic connectivity differ in their vegetation response to water availability, soil properties, and interannual variability and extremes in vegetation productivity. We find that the average interannual variability of vegetation productivity is 21-34% higher in ecosystems with high hydrologic connectivity, with skewed annual anomalies showing larger extremes in carbon uptake. This is driven by a higher average and more variable surface soil moisture and significantly higher soil available water capacity and soil depth. These spatially small ecosystems, covering 14% of the study region, contribute 15-22% (median = 17%) to regionalscale carbon uptake through higher rates of gross photosynthesis, especially evident during wet years, and 3-37% (median = 19%) to annual anomalies. Current global land surface models do not reproduce the observed spatial patterns of interannual variability in carbon uptake over regions where hydrologic connectivity is high as they lack the mechanism of connectivity of water between discrete land surface elements. This study highlights the significant role of riparian and floodplain vegetation on the interannual variability and extremes of the regional carbon cycle.
原文链接:- NSTL
- Elsevier
Kuter, SemihBolat, KenanAkyurek, Zuhal
33页查看更多>>摘要：Snow is a major element of the cryosphere with significant impact on the Earth's water cycle and global energy budget. Acquiring consistent and long time series data on the spatial extent of snow cover doubtlessly plays a key role in our understanding and modeling of the current and future environmental dynamics. Remote sensing offers a powerful tool for continuous retrieval of snow cover information by utilizing snow's contrasting reflectance characteristics at optical wavelengths. The pre-operational H35 covers the Northern Hemisphere, and it is the successor of the operational Pan-European H12 daily fractional snow-covered area (fSCA) product at similar to 1 km. Both products are developed through the Satellite Application Facility on Support to Operational Hydrology and Water Management (H-SAF) project of EUMETSAT by exploiting AVHRR channels. This study is focused on developing an alternative fully data-driven H35 product with improved accuracy using a machine learning (ML) based approach. Multivariate adaptive regression splines (MARS) algorithm is trained by using AVHRR reflectance data as well as the well-known snow and vegetation indices (i.e., NDSI and NDVI) to generate the new version of H35 fSCA product. The reference fSCA maps required for the training of MARS models are obtained from the higher resolution Sentinel 2 multispectral imagery. The MARS-based fSCA models are validated against an initial test dataset composed of 15 Sentinel 2 scenes over European Alps, Tatra Mountain Range, and Turkey. The final MARS-H35 product is then rigorously assessed over the whole Northern Hemisphere within a temporal domain spanning from Nov 2018 to Nov 2019. The quantitative testing process involves the use of reference data in both continuous and dichotomous scales: i) Sentinel 2 derived reference fSCA maps, ii) ERA5-Land snow depth data, iii) MODIS MOD10A1 NDSI snow cover data, and finally iv) in-situ snow depth data. Additionally, qualitative assessment is also performed by visually comparing MARS-H35/MODIS false-color and MARS-H35/Sentinel 2-derived reference fSCA image pairs over various geographic regions. The overall results indicate that: i) the proposed MARS-H35 fSCA product overperforms the original H35, and ii) it has higher capability in detecting the fine variations in the extent of snow cover, especially across the fringes of the slopes in complex mountainous terrains.
原文链接:- NSTL
- Elsevier
Wang, DifengWang, YuxinHe, XianqiangBai, Yan...
18页查看更多>>摘要：Under the background of global change, increasing attention has been paid to the changes of benthic habitats in shallow ocean ecosystems (e.g., seagrass beds and coral reefs). Optical satellite remote sensing via both active and passive methods plays an important role in monitoring the health of benthic habitats by retrieving benthic reflectance spectra, but it remains difficult to accurately retrieve benthic reflectance spectra from only active or passive remote sensing because of the coupling between water column scattering and benthic reflectance. Here, we developed a semi-analytical model to retrieve benthic reflectance spectra in Case-I waters by combining active lidar and passive high-resolution imagery. Based on two-stream radiative transfer theory, the analytical relationship among the remote sensing reflectance (R-rs) and water column reflectance (R-w), benthic reflectance (R-b), diffuse attenuation coefficient (K-d), and water depth was established. The lidar data at a certain wavelength were applied to derive the water depth and the chlorophyll concentration (chl) along the lidar track. Then, the values of K-d at different wavelengths were estimated from the derived chl. In addition, we established a look-up table (LUT) for the relationship between R-w and chl and water depth using Hydrolight simulation, and the R-w values at different wavelengths were then estimated by the lidar-derived chl and water depth. Finally, R-b(lambda) values at different wavelengths along the lidar track were retrieved from the R-rs(lambda) values observed by passive high-resolution imagery and the values of R-w(lambda), K-d(lambda), and water depth derived by lidar observation. The accuracy of the model was verified by using the Hydrolight simulated datasets, and the high correlation coefficient (R) revealed promising model performance for different benthic habitats, e.g., R > 0.9 for typical clean shallow water (chl = 0.5 mg/m(3), H < 4 m) for the wavelength range of 400-640 nm. The model was further applied to real satellite data from ICESat-2 lidar and passive high-resolution satellite imagery (multispectral imagery of Sentinel-2 and hyperspectral imagery of Zhuhai-1) at two different benthic habitat sites (seagrass beds in Xincun Bay and coral reefs in the Huaguang Reef) in the South China Sea, and the results revealed that the model could reproduce the benthic spectra in both magnitude and shape. Overall, the proposed model can reliably yield benthic reflectance spectra along the lidar track without any requirement on prior knowledge, which should be beneficial for further benthic habitat health monitoring.
原文链接:- NSTL
- Elsevier
Pickens, Amy H.Hansen, Matthew C.Stehman, Stephen, VTyukavina, Alexandra...
14页查看更多>>摘要：Seasonal changes of temperature and precipitation cause inland open surface water and ice cover extents to vary dramatically through the year from local to global scales. These dynamics of land, water, and ice have a significant impact on climate and often are critical to natural ecosystem functioning. However, global seasonal dynamics of both water and ice extent have not been well quantified. Here, we present the quantification of monthly surface water and ice areas for 2019 with associated uncertainties. Time-series reference data were created for a probability sample of 10 m grid cells by interpreting the entire 2019 time-series of 10 m Sentinel-2 data and a subset of 3 m PlanetScope data in selected places with a mix of land and water. From the probability sample reference data, we estimate that 4.86 +/- 0.16 million km(2) had inland water presence at some point during the year. Globally, only 23% of the total area with water was permanent water that remained open year-round (1.13 +/- 0.19 million km(2)). Permanent water with seasonal ice cover extended 1.97 +/- 0.21 million km(2), comprising 41% of the total area with water. Seasonal water-land transitions (both with and without ice/snow cover) covered the remaining 36% of the total area with water (1.76 +/- 0.19 million km(2)). February had the maximum extent of ice over areas of inland permanent and seasonal water, totaling 2.49 +/- 0.25 million km(2), and January - March had a larger global extent of ice cover than of open water. To investigate the spatiotemporal distribution of ice cover and the suitability of Landsat, prototype maps of surface water ice cover phenology were created by integrating the ice/snow and no data labels from the quality assurance layer of the GLAD ARD of Potapov et al. (2020) with the monthly surface water layers of Pickens et al. (2020), both of which are Landsat- based. While limited by data availability, these maps reveal the high spatiotemporal variability of ice phenology. The near-daily observations near the poles and the 10 m resolution bands of Sentinel-2 provide unprecedented potential to examine surface water and ice dynamics for 2016 forward and to investigate the drivers and impacts of this variability.
原文链接:- NSTL
- Elsevier
Han, YingWang, TianheTang, JingyiWang, Chengyun...
15页查看更多>>摘要：A full understanding of the Asian dust cycle can help with evaluation of the profound impact of mineral dust on human health, the ecosystem, the terrestrial and oceanic biogeochemical cycles, and the weather and climate. The Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP)-based 3-D dust detection and routine sampling capability, with the accurate dust mass extinction efficiency from Dust Constraints from joint Observational Modelling-experimental analysis (DustCOMM) dataset, has made it possible to estimate the climatology of Asian dust mass loading (DML) and its transport flux. This study draws on this to provide new insights into the Asian dust cycle, especially the variability of its mass-weighted dust transport central axis (TCA), the contribution of different desert sources to its downstream effects, and the resulting dust budgets in terrestrial and oceanic regions. Dust aerosols emitted from the East Asian and Central Asian deserts together form a heavy dust transport belt stretching from the Taklimakan Desert (TD) and Gobi Desert (GD) to the Pacific Ocean. South Asian dust from the Thar Desert (ThD) can also affect southern China by crossing the Hengduan Mountains and the Yunnan-Guizhou Plateau. The dust TCA is controlled by the terrain of northwest inland China, but shifts in remote regions in the range of 35-50 degrees N due to the western Pacific subtropical high and Aleutian low, and it trend towards a zonal straight line as the altitude increases. The dust transport contribution of the East Asian deserts to the mainland of China and adjacent sea is about 7 times than that of South Asia, with the annual transport fluxes being 214.28 and 30.43 Tg, respectively. The GD dominates the contribution of Asian deserts to the downstream effects and accounts for about 60% of the dust. This can be attributed to its maximum transport flux being near the surface, while the dust transport of the TD and ThD is above 3 km because of the blocking effect of the surrounding terrain. The deposition of Asian dust in the adjacent seas decreases significantly along the dust TCA, with the annual deposition rates being about 40.12, 20.41, and 4.01 g m(-2) in the Yellow Sea, Japan Sea and the Northwest Pacific Ocean, respectively. These new findings and quantification of the Asian dust cycle will help with validation of the simulations provided by global and regional climate models and enable further evaluation of the impact of Asian dust on various related Earth systems.
原文链接:- NSTL
- Elsevier
Williams, Mark L.Mitchell, Anthea L.Milne, Anthony K.Danaher, Tim...
19页查看更多>>摘要：L-band synthetic aperture radar (SAR) backscatter intensity is sensitive to land cover and can be used to estimate vegetation measures such as basal area (BA) and biomass. However, the estimation of BA, and especially change in BA, can be hampered by the influences upon backscatter of external factors such as imaging geometry, terrain topology, prevailing moisture conditions and even SAR sensor characteristics. This paper describes a method of reducing the adverse effects of such extraneous influences on vegetation and change estimates derived from single-channel SAR data. Empirical corrections for terrain slope and cross-track tendencies were applied and linear least squares difference minimization used to normalize the backscatter differences between scenes. The method was applied to state-wide coverage of L-band, fine-mode, HV polarization Advanced Land Observing Satellite (ALOS) Phased Array L-band SAR (PALSAR) data over New South Wales (NSW), Australia. The data were acquired with different sensors over two "observational epochs": ALOS PALSAR in 2009 and ALOS-2 PALSAR-2 in 2016/17. The SAR datasets presented significant variations in backscatter intensity beyond those attributable to changes in vegetation cover. The corrective procedures resulted in improved uniformity of observed backscatter dependence on vegetation. Variations in backscattering coefficient between swaths were reduced by as much as 1.75 dB and 25% of the standard deviation in mean backscattering coefficients in common areas and at near- and far-range. This corresponded to a correction in BA estimate of 4.4 m2 ha-1. The method was observed to reduce ambiguities in regrowth estimates at swath boundaries and correct estimates of BA change by as much as 30% over large areas. The resulting estimates of 7-year change in BA provide spatially explicit forest structural information that is assisting in monitoring changes in woody vegetation across NSW.
原文链接:- NSTL
- Elsevier
Zhu, QiqiLei, YangSun, XiongliGuan, Qingfeng...
26页查看更多>>摘要：Accurate urban land-use maps, which reflect the complicated land-use pattern implied in the function and distribution of land-cover types, play an important role in urban analysis. In recent years, data-driven deep learningbased land-use mapping methods have made great breakthroughs due to their strong feature extraction ability. Meanwhile, multisource geographic data, such as open street map (OSM), has been applied in land-use mapping with high spatial resolution remote sensing (HSR) imagery. Nevertheless, given the intraclass visual inconsistency and interclass label ambiguity, there are enormous challenges in OSM-based land-use pattern depiction: 1) the significant size variability of land-use parcel generated by OSM; 2) the weak interpretability of the datadriven based features; and 3) the neglect of intrinsically hierarchical and nested relationships between landcover and land-use. In this paper, to bridge the "knowledge gap" for urban land-use mapping, a knowledgeguided land pattern depicting (KGLPD) framework is proposed. The proposed KGLPD framework mainly contains four parts. Land-use parcels with various scales are generated based on OSM. An adaptive gradient perceptive (AGP) mechanism is proposed to provide patch distribution prior knowledge for guiding the datadriven based visual feature extraction. To effectively cognize the layout of different land-cover types as the knowledge-driven information, a land pattern cognitive (LPC) model is designed to capture the inner and outer relationships (i.e., direction, distance and co-frequency) of different land-cover types. The fully sparse topic model (FSTM) is then used to extract the critical land pattern information from the data-driven and knowledgedriven information. Four typical Chinese urban cities are selected to evaluate the proposed framework. Experimental results on three cities with four regions of distinctive characteristics in different years, have achieved high classification accuracies of about 80%, with 10% improvement compared with other methods. This demonstrates the effectiveness and robustness of the proposed knowledge-guided urban land use mapping framework. Experimental results on the whole city of Shenzhen in China imply that the proposed framework perform well with small training samples. The results on different cities validate the generalizability and transferability of KGLPD. The typical land-use maps and the corresponding land-cover maps help understanding the relationship between them.
原文链接:- NSTL
- Elsevier
Shuai, GuanyuanBasso, Bruno
13页查看更多>>摘要：In-season prediction of crop yield is a topic of research studied by several scientists using different methods. Seasonal forecasts provide critical insights to different stakeholders who use the information for strategic and tactical decisions. In this study, we propose a novel scalable method to forecast in season subfield crop yield through a machine learning model based on remotely sensed imagery and data from a process-based crop model on a cumulative crop drought index (CDI) designed to capture the impact of in-season crop water deficit on crops. To evaluate the performance of our proposed model, we used 352 growers' fields of different sizes across the states of Michigan, Indiana, Iowa, and Illinois, with 2520 respective yield maps generated by combine harvesters equipped with precise high-resolution yield monitor sensor, over multiple years (from 2006 up to 2019). We obtained high resolution digital elevation model, climate, and soil data to execute the SALUS model, a processbased crop model, to calculate the CDI for each field used in the study. We used Landsat Analysis Ready Dataset (ARD) products generated by USGS as image source to calculate the green chlorophyll vegetation index (GCVI). We found that the inclusion of the CDI in remote sensing-based random forest models substantially improved inseason subfield corn yield prediction. The addition of the CDI in the yield prediction model showed that the greatest improvements in predictions were observed in the driest year (2012) in our case study. The proposed approach also showed that the subfield spatial variations of corn yield are better captured with the inclusion of CDI for most fields. The earliest prediction in the growing season with GCVI and CDI together outperformed the latest prediction with GCVI alone, highlighting the potential of CDI for predicting spatial variability of maize yield around grain filling period, which is on average close to two months before typical crop harvest in the US Midwest.
原文链接:- NSTL
- Elsevier
