Estimating Aboveground Carbon Dynamic of China Using Optical and Microwave Remote-Sensing Datasets from 2013 to 2019

Over the past 2 to 3 decades,Chinese forests are estimated to act as a large carbon sink,yet the magnitude and spatial patterns of this sink differ considerably among studies.Using 3 microwave(L-and X-band vegetation optical depth[VOD])and 3 optical(normalized difference vegetation index,leaf area index,and tree cover)remote-sensing vegetation products,this study compared the estimated live woody aboveground biomass carbon(AGC)dynamics over China between 2013 and 2019.Our results showed that tree cover has the highest spatial consistency with 3 published AGC maps(mean correlation value R=0.84),followed by L-VOD(R=0.83),which outperform the other VODs.An AGC estimation model was proposed to combine all indices to estimate the annual AGC dynamics in China during 2013 to 2019.The performance of the AGC estimation model was good(root mean square error=0.05 Pg C and R^(2)=0.90 with a mean relative uncertainty of 9.8% at pixel scale[0.25°]).Results of the AGC estimation model showed that carbon uptake by the forests in China was about+0.17 Pg C year^(-1) from 2013 to 2019.At the regional level,provinces in southwest China including Guizhou(+22.35 Tg C year^(-1)),Sichuan(+14.49 Tg C year^(-1)),and Hunan(+11.42 Tg C year^(-1))provinces had the highest carbon sink rates during 2013 to 2019.Most of the carbon-sink regions have been afforested recently,implying that afforestation and ecological engineering projects have been effective means for carbon sequestration in these regions.

A review of the estimation of downward surface shortwave radiation based on satellite data:Methods, progress and problems

The estimation of downward surface shortwave radiation(DSSR)is important for the Earth’s energy budget and climate change studies.This review was organised from the perspectives of satellite sensors,algorithms and future trends,retrospects and summaries of the satellite-based retrieval methods of DSSR that have been developed over the past 10 years.The shortwave radiation reaching the Earth’s surface is affected by both atmospheric and land surface parameters.In recent years,studies have given detailed considerations to the factors which affect DSSR.It is important to improve the retrieval accuracy of cloud microphysical parameters and aerosols and to reduce the uncertainties caused by complex topographies and high-albedo surfaces(such as snow-covered areas)on DSSR estimation.This review classified DSSR retrieval methods into four categories:empirical,parameterisation,look-up table and machine-learning methods,and evaluated their advantages,disadvantages and accuracy.Further efforts are needed to improve the calculation accuracy of atmospheric parameters such as cloud,haze,water vapor and other land surface parameters such as albedo of complex terrain and bright surface,organically combine machine learning and other methods,use the new-generation geostationary satellite and polar orbit satellite data to produce highresolution DSSR products,and promote the application of radiation products in hydrological and climate models.

Upscaling in situ albedo for validation of coarse scale albedo product over heterogeneous surfaces

One of the essential steps for satellite albedo validation is upscaling in situ measurements to corresponding pixel scale over relatively heterogeneous land surfaces.Although the multi-scale validation strategy is applicable for heterogeneous surfaces,the calibration of the high-resolution imagery during upscaling process is never perfect,and thus the upscaling results suffer from errors.The regression-kriging(RK)technique can compensate the calibration part by applying kriging to upscale residuals and produce more accurate upscaling results.In this paper,in situ measurements and high spatial resolution albedo imagery combined with RK technique was proposed.This method is illustrated by upscaling surface albedo from in situ measurements scale to the coarse pixel scale in the core experimental area of HiWATER,where 17 WSN nodes were deployed at heterogeneous area.The upscaling results of this method were compared with the upscaling results from multi-scale strategy.The results show that the upscaling method based on in situ measurements and high-resolution imagery combined with RK technique can capture the spatial characteristics of surface albedo better.Further,an attempt was made to expand this method in time series.Finally,a preliminary validation of the Moderate Resolution Imaging Spectroradiometer albedo product was performed as the tentative application.

A web-based land surface remote sensing products validation system (LAPVAS): application to albedo product

Quantitative remote sensing product(QRSP)validation is a complex process to assess the accuracy and uncertainty independently using reference data with multiple land cover types and long-time series.A web-based system named as LAnd surface remote sensing Product VAlidation system(LAPVAS)is described in this paper,which is used to implement the QRSPs validation process automatically.The LAPAVS has two subsystems,the Validation Databases Subsystem and the Accuracy Evaluation Subsystem.Three functions have been implemented by the two subsystems for a comprehensive QRSP validation:(1)a standardized processing of reference data and storage of these data in validation databases;(2)a consistent and comprehensive validation procedure to assess the QRSPs’accuracy and uncertainty;and(3)a visual process customization tool with which the users can register new validation data,host new reference data,and readjust the validation workflows for the QRSP accuracy assessment.In LAPVAS,more than 100 GB of reference data warehoused in validation databases for 13 types of QRSPs’validation.One of the key QRSPs,land surface albedo,is selected as an example to illustrate the application of LAPVAS.It is demonstrated that the LAPVAS has a good performance in the land surface remote sensing product validation.

Evaluation of several recently developed sampling strategies within the coarse pixel scale for validation of coarse-resolution satellite albedo products

Due to the spatial heterogeneity and the spatial scale mismatch between in situ and satellite-based measurements,optimal ground sampling should be made to increase the representativeness of in situ observations. Therefore,many ground sampling strategies have been proposed,but their performance within the coarse pixel has not been evaluated. Hence,this study evaluated four typical methods regarding their ability to obtain pixel scale ground ‘truth’. Random combination (RC) performs best,with the always fewest samples to satisfy representativeness errors (REs) of 3% in the case of a small number of samples. When the goal of sampling is to obtain in situ measurements with REs close to 0 at the expense of increasing the number of samples,cumulative representativeness sampling (CRS) is more effective than RC in less heterogeneous areas. Geo-statistical model-based sampling (GSS) does not work well because the number of samples within the coarse pixel scale cannot support a robust semi-variogram model. Stratified sampling (SS) is highly dependent on spatial heterogeneity and does not work well in the case of small sample sizes. This study gives important guidance for ground sample deployment within the coarse pixel for validation of coarse-resolution satellite albedo products over a heterogeneous surface.

Forward a spatio-temporal trend surface for long-term groundmeasured albedo upscaling over heterogeneous land surface

Upscaling ground albedo is challenged by the serious discrepancy between the heterogeneity of land surfaces and the small number of ground-based measurements.Conventional ground-based measurements cannot provide sufficient information on the characteristics of surface albedo at the scale of coarse pixels over heterogeneous land surfaces.One method of overcoming this problem is to introduce high-resolution albedo imagery as ancillary information for upscaling.However,due to the low frequency of updating of high-resolution albedo maps,upscaling time series of ground-based albedo measurements is difficult.This paper proposes a method that is based on the idea of conceptual universal scaling methodology for establishing a spatiotemporal trend surface using very few high-resolution images and time series of ground-based measurements for spatial-temporal upscaling of albedo.The construction of the spatiotemporal trend surface incorporates the spatial information provided by auxiliary remote sensing images and the temporal information provided by long time series of ground observations.This approach was illustrated by upscaling ground-based fine-scale albedo measurements to a coarse scale over the core study area in HiWATER.The results indicate that this method can characterize the spatiotemporal variations in surface albedo well,and the overall correlation coefficient was 0.702 during the study period.