Forest above-ground biomass estimates across three decades from spaceborne scatterometer observations

Author(s):  
Maurizio Santoro ◽  
Oliver Cartus ◽  
Nuno Carvalhais ◽  
Simon Besnard ◽  
Naixin Fan

<p>The large uncertainty characterizing the terrestrial carbon (C) cycle is a consequence of the sparse and irregular observations on the ground. In terms of observations, spaceborne remote sensing has been achieving global, repeated coverages of the Earth since the late 1970s, with a continuous increase in terms of density of observations in time and spatial resolution, thus potentially qualifying as data source to fill such gap in knowledge. Above-ground biomass is a baseline for quantifying the terrestrial C pool; however, remote sensing observations do not measure the organic mass of vegetation. Above-ground biomass (AGB) of forests can only be inferred by inverting numerical models relating and combining multiple remote sensing observations. One of the longest time record of observations from space is represented by the backscattered intensity from the European Remote Sensing Wind Scatterometer (ERS WindScat) and the MetOp Advanced Scatterometer (ASCAT), both operating at C-band (wavelength of 6 cm). An almost unbroken time series of backscatter observations at 0.25° spatial resolution exists since 1991 and data continuity is guaranteed in the next decades. In spite of the weak sensitivity of C-band backscatter to AGB, wall-to-wall estimates of AGB have been derived from high-resolution SAR observations by exploiting multiple observations acquired in a relatively short time period  (Santoro et al., Rem. Sens. Env., 2011; Santoro et al., Rem. Sens. Env., 2015). We have now applied this approach to generate a global time series of AGB estimates for each year between 1992 and 2018 from the C-band scatterometer data at 0.25° spatial resolution. The spatial patterns of AGB match known patterns from in situ records and other remote sensing datasets. The uncertainty of our AGB estimates is between 30% and 40% of the estimated value at the pixel level, providing strong confidence in multi-decadal AGB trends. We identify a constant increase of biomass across most boreal and temperate forests of the northern hemisphere. In contrast, we detect severe loss of biomass throughout the wet tropics during the 1990s and the beginning of the 2000 decade in consequence of massive deforestation. This loss in biomass is followed by a steady increase during the 2000s and the beginning of the most recent decade, coming more recently into saturation. Overall, we find that the global AGB density at 0.25° steadily increased by 9% from 71.8 Mg ha<sup>-1</sup> Pg in the 1990s to 78.1 Mg ha<sup>-1</sup> in the 2010s. Combining our AGB density estimates with the annual maps of the Climate Change Initiative (CCI) Land Cover dataset, we show that total AGB in forests decreased slightly from 566 Pg in the 1990s to 560 Pg in the 2000s, then increased to 593 Pg in the 2010s, resulting in an almost 5% net increase during the last three decades.</p>

2019 ◽  
Vol 11 (11) ◽  
pp. 1266 ◽  
Author(s):  
Mingzheng Zhang ◽  
Dehai Zhu ◽  
Wei Su ◽  
Jianxi Huang ◽  
Xiaodong Zhang ◽  
...  

Continuous monitoring of crop growth status using time-series remote sensing image is essential for crop management and yield prediction. The growing season of summer corn in the North China Plain with the period of rain and hot, which makes the acquisition of cloud-free satellite imagery very difficult. Therefore, we focused on developing image datasets with both a high temporal resolution and medium spatial resolution by harmonizing the time-series of MOD09GA Normalized Difference Vegetation Index (NDVI) images and 30-m-resolution GF-1 WFV images using the improved Kalman filter model. The harmonized images, GF-1 images, and Landsat 8 images were then combined and used to monitor the summer corn growth from 5th June to 6th October, 2014, in three counties of Hebei Province, China, in conjunction with meteorological data and MODIS Evapotranspiration Data Set. The prediction residuals ( Δ P R K ) in NDVI between the GF-1 observations and the harmonized images was in the range of −0.2 to 0.2 with Gauss distribution. Moreover, the obtained phenological curves manifested distinctive growth features for summer corn at field scales. Changes in NDVI over time were more effectively evaluated and represented corn growth trends, when considered in conjunction with meteorological data and MODIS Evapotranspiration Data Set. We observed that the NDVI of summer corn showed a process of first decreasing and then rising in the early growing stage and discuss how the temperature and moisture of the environment changed with the growth stage. The study demonstrated that the synthesized dataset constructed using this methodology was highly accurate, with high temporal resolution and medium spatial resolution and it was possible to harmonize multi-source remote sensing imagery by the improved Kalman filter for long-term field monitoring.


2020 ◽  
Vol 12 (4) ◽  
pp. 688 ◽  
Author(s):  
Jacky Lee ◽  
Jeffrey A. Cardille ◽  
Michael T. Coe

Landsat 5 has produced imagery for decades that can now be viewed and manipulated in Google Earth Engine, but a general, automated way of producing a coherent time series from these images—particularly over cloudy areas in the distant past—is elusive. Here, we create a land use and land cover (LULC) time series for part of tropical Mato Grosso, Brazil, using the Bayesian Updating of Land Cover: Unsupervised (BULC-U) technique. The algorithm built backward in time from the GlobCover 2009 data set, a multi-category global LULC data set at 300 m resolution for the year 2009, combining it with Landsat time series imagery to create a land cover time series for the period 1986–2000. Despite the substantial LULC differences between the 1990s and 2009 in this area, much of the landscape remained the same: we asked whether we could harness those similarities and differences to recreate an accurate version of the earlier LULC. The GlobCover basis and the Landsat-5 images shared neither a common spatial resolution nor time frame, But BULC-U successfully combined the labels from the coarser classification with the spatial detail of Landsat. The result was an accurate fine-scale time series that quantified the expansion of deforestation in the study area, which more than doubled in size during this time. Earth Engine directly enabled the fusion of these different data sets held in its catalog: its flexible treatment of spatial resolution, rapid prototyping, and overall processing speed permitted the development and testing of this study. Many would-be users of remote sensing data are currently limited by the need to have highly specialized knowledge to create classifications of older data. The approach shown here presents fewer obstacles to participation and allows a wide audience to create their own time series of past decades. By leveraging both the varied data catalog and the processing speed of Earth Engine, this research can contribute to the rapid advances underway in multi-temporal image classification techniques. Given Earth Engine’s power and deep catalog, this research further opens up remote sensing to a rapidly growing community of researchers and managers who need to understand the long-term dynamics of terrestrial systems.


2010 ◽  
Vol 53 (S1) ◽  
pp. 176-183 ◽  
Author(s):  
Min Xu ◽  
ChunXiang Cao ◽  
QingXi Tong ◽  
ZengYuan Li ◽  
Hao Zhang ◽  
...  

2002 ◽  
Vol 11 (5) ◽  
pp. 393-399 ◽  
Author(s):  
Michael A. Lefsky ◽  
Warren B. Cohen ◽  
David J. Harding ◽  
Geoffrey G. Parker ◽  
Steven A. Acker ◽  
...  

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