Mountain glaciers are one of the major fresh water resources. Glacier mass balance on the Tibetan Plateau (TP) can directly reflect local climate change and plays a crucial role in the terrestrial water cycle and food security of local people. In this study, we improved the procedure to analyze Three-Line-Array (TLA) stereo images to estimate the glaciers mass balance in Kangri Karpo mountains using Zi Yuan-3 (ZY-3) TLA data and C-band Shuttle Radar Topography Mission (SRTM) Digital Elevation Model (DEM) in two periods, i.e. 2000-2013 and 2013-2017. The results showed that the mean mass balance of glaciers between 2000 and 2017 was -0.91 ± 0.02 m w.e. a^-1. The glaciers presented accelerated mass loss in recent years (2013-2017, -2.84 ±0.05 m w.e. a^-1), compared with the first decade in 21st (2000-2013, -1.59 ± 0.03 m w.e. a^-1), while the melt rate in debris-covered glaciers was larger than in clean-ice glaciers.

Land Surface Models which determine evapotranspiration (ET) by neglecting the sub-grid heterogeneity of land-atmosphere parameters will cause aggregation biases in spatially-averaged ET estimates, considering the nonlinear dependences of ET on the heterogeneous land-atmosphere parameters. One frequently adopted strategy clusters the heterogeneous surface within a model grid into several tiles, assumed to be homogeneous, usually based on high-resolution land cover data. While the differences in bulk-averaged parameters between different tiles are considered, the heterogeneity within each tile is neglected. This study evaluated in detail the aggregation biases in the tile mean ET estimates due to applying bulk-averages of the Saturation degree of surface Soil Moisture (SSM) and Leaf Area Index (LAI) for each tile through numerical experiments. Four types of Probability Distribution Function (PDF) were used to simulate different scenarios on the heterogeneity (within a tile) of SSM and LAI, i.e., from water scarcity to wet, and from sparse to dense vegetation covered surfaces. Aggregation bias was calculated by comparing ET estimates based on bulk-averaged SSM and LAI with the one obtained by aggregation of the flux estimates based on the PDFs, which complies with energy conservation. In addition, a wide range of meteorological conditions was applied in our numerical experiments and the impacts were evaluated by binning results according to the reference ET (ET ₀ ). We found that potentially significant bias can be found in semi-arid areas. Neglecting the actual spatial variability of both SSM and LAI within tiles can lead to both large relative error (> 20%) and absolute error (> 1 mm/day) in the estimated ET. A negative bias is expected at low ET / ET ₀ and a positive bias is expected at large ET / ET ₀ , regardless of climate conditions (i.e., ET ₀ ).

Observing and understanding changes in Africa is a hotspot in global ecological environmental research since the early 1970s. As possible causes of environmental degradation, frequent droughts and human activities attracted wide attention. Remote sensing of nighttime light provides an effective way to map human activities and assess their intensity. To identify settlements more effectively, this study focused on nighttime light in the northern Equatorial Africa and Sahel settlements to propose a new method, namely, the patches filtering method (PFM) to identify nighttime lights related to settlements from the National Polar-orbiting Partnership Visible Infrared Imaging Radiometer Suite (NPP-VIIRS) monthly nighttime light data by separating signal components induced by biomass burning, thereby generating a new annual image in 2016. The results show that PFM is useful for improving the quality of NPP-VIIRS monthly nighttime light data. Settlement lights were effectively separated from biomass burning lights, in addition to capturing the seasonality of biomass burning. We show that the new 2016 nighttime light image can very effectively identify even small settlements, notwithstanding their fragmentation and unstable power supply. We compared the image with earlier NPP-VIIRS annual nighttime light data from the National Oceanic and Atmospheric Administration (NOAA) National Center for Environmental Information (NCEI) for 2016 and the Sentinel-2 prototype Land Cover 20 m 2016 map of Africa released by the European Space Agency (ESA-S2-AFRICA-LC20). We found that the new annual nighttime light data performed best among the three datasets in capturing settlements, with a high recognition rate of 61.8%, and absolute superiority for settlements of 2.5 square kilometers or less. This shows that the method separates biomass burning signals very effectively, while retaining the relatively stable, although dim, lights of small settlements. The new 2016 annual image demonstrates good performance in identifying human settlements in sparsely populated areas toward a better understanding of human activities.

Mountain glaciers are sensitive to climate change and are thus relevant indicators of regional climate variability. In order to understand the dynamics of glaciers, retrieving glacier surface velocity is valuable in understanding physical processes in glaciers. We apply an image cross-correlation algorithm in the frequency domain to derive glacier velocity on the Yanong glacier between 2013 and 2018. The results indicate that the flow patterns are related to the terrain complexity. Yanong Glacier maximum velocity was 168 m/year at the elevation around 4700 meters. The surface velocity exceeded 100 m/year above the elevation of 4200 m, while above 5000 m the surface velocity fluctuated around 60 m/year along the main stream.

The daily AMSR-E/NASA(the Advanced Microwave Scanning Radiometer-Earth Observing System/the National Aeronautics and Space Administration) and JAXA (the Japan Aerospace Exploration Agency) soil moisture (SM) products from 2002 to 2011 at 25 km resolution were developed and distributed by the NASA National Snow and Ice Data Center Distributed Active Archive Center (NSIDC DAAC) and JAXA archives, respectively. This study analyzed and evaluated the temporal changes and accuracy of the AMSR-E/NASA SM product and compared it with the AMSR-E/JAXA SM product. The accuracy of both AMSR-E/NASA and JAXA SM was low, with RMSE (root mean square error) > 0.1 cm³ cm^-3 against the in-situ SM measurements, especially the AMSR-E/NASA SM. Compared with the AMSR-E/JAXA SM, the dynamic range of AMSR-E/NASA SM is very narrow in many regions and does not reflect the intra- and inter-annual variability of soil moisture. We evaluated both data products by building a linear relationship between the SM and the Microwave Polarization Difference Index (MPDI) to simplify the AMSR-E/NASA SM retrieval algorithm on the basis of the observed relationship between samples extracted from the MPDI and SM data. We obtained the coe°cients of this linear relationship (i.e., A₀ and A₁) using in-situ measurements of SM and brightness temperature (T_B) data simulated with the same radiative transfer model applied to develop the AMSR-E/NASA SM algorithm. Finally, the linear relationships between the SM and MPDI were used to retrieve the SM monthly from AMSR-E T_B data, and the estimated SM was validated using the in-situ SM measurements in the Naqu area on the Tibetan Plateau of China. We obtained a steeper slope, i.e., A₁ = 8, with the in-situ SM measurements against A₁ = 1, when using the NASA SM retrievals. The low A₁ value is a measure of the low sensitivity of the NASA SM retrievals to MPDI and its narrow dynamic range. These results were confirmed by analyzing a data set collected in Poland. In the case of the Tibetan Plateau, the higher value A₁ = 8 gave more accurate monthly AMSR-E SM retrievals with RMSE = 0.065 cm₃ cm_-3. The dynamic range of the improved retrievals was more consistent with the in-situ SM measurements than with both the AMSR-E/NASA and JAXA SM products in the Naqu area of the Tibetan Plateau in 2011.

The ASCAT (Advanced SCATterometer) soil moisture product with 10-km spatial resolution was retrieved based on the soil water index (SWI) algorithm from the data acquired by the scatterometer on board the Meteorological OPerational (MetOP) satellites (MetOP-A, MetOP-B). In this study, the ASCAT product was downscaled from 10-km to 1-km spatial resolution based on the Apparent Thermal Inertia (ATI) estimated from MODIS Land Surface Temperature (LST) and Albedo retrievals in 54 grids (1 degree 1 degree) around 54 FLUXNET stations. First, the ATI was estimated at 1-km spatial resolution by using MODIS LST and Albedo data at the same spatial resolution and then resampled to 10-km. Second, the relationship between ASCAT soil moisture and ATI at 10-km spatial resolution was established. Finally, the spatiotemporally continuous soil moisture at 1-km spatial resolution was retrieved using the obtained relationship between ATI and ASCAT at 10-km spatial resolution, and the ATI data at 1-km spatial resolution. However, there were many missing values in the MODIS LST maps leading to spatiotemporal discontinuity in LST and calculated ATI data. To obtain spatiotemporal continuous ATI data, this study first reconstructed the MODIS LST data by finding similar points that had the same land cover type and similar NDVI (the Normalized Difference Vegetation Index) value. In this study, we found that the LST data of similar points in a pair of temporal adjacent LST images had a linear relationship. The LST data of these similar points in a pair of temporal adjacent LST images were used to establish a linear relationship and then used to reconstruct the pair of temporally adjacent LST images. The reconstructed LST data were used to obtain the spatiotemporal continuous ATI data at 1-km and 10-km spatial resolutions. In this study, downscaled 1-km spatial resolution soil moisture product within the 54 grids around the FLUXNET sites were obtained in 2013. Results indicated that the spatial distribution of the downscaled soil moisture using the reconstructed MODIS LST data is better than that using original MODIS LST data. Additionally, the downscaled soil moisture was evaluated against in-situ soil moisture measurements at 54 FLUXNET stations. The average of RMSE (the Root Mean Square Error) was 0.098 m³m^-3 and the average of MAE (the Mean Absolute Error) was 0.08 m³m^-3.

This paper evaluated the accuracy of multiple satellite-based precipitation products including the tropical rainfall measuring mission multisatellite precipitation analysis (TMPA) (TMPA 3B42RT and TMPA 3B42 version 7) and the Climate Prediction Center MORPHing technique (CMORPH) (CMORPH RAW and CMORPH BLD version 1.0) datasets and investigated the impact of the accuracy and temporal coverage of these data products on the reliability of the standardized precipitation index (SPI) estimates. The satellite-based SPI was compared with the SPI estimate using in situ precipitation observations from 2221 meteorological observation sites across China from 1998 to 2014. The SPI values calculated from the products calibrated with rain gauge measurements (TMPA 3B42 and CMORPH BLD) are generally more consistent with the SPI obtained with in situ measurements than those obtained using noncalibrated products (TMPA 3B42RT and CMORPH RAW products). The short data record of satellite precipitation data products is not the primary source of large errors in the SPI estimates, suggesting that the SPI estimate using satellite precipitation data products can be applied to drought assessment and monitoring. Satellite-based SPI estimates are more accurate in eastern China than in western China because of larger uncertainties in precipitation retrievals in western China, characterized by arid and semiarid climate conditions and complex landscapes. The satellite-based SPI can capture typical drought events throughout China, with the limitation that it is based on precipitation only and that different durations of antecedent precipitation are only suitable for specific drought conditions.

The regional surface soil heat flux (G0) estimation is very important for the large-scale land surface process modeling. However, most of the regional G0 estimation methods are based on the empirical relationship between G0 and the net radiation flux. A physical model based on harmonic analysis was improved (referred to as "HM model") and applied over the Heihe River Basin northwest China with multiple remote sensing data, e.g., FY-2C, AMSR-E, and MODIS, and soil map data. The sensitivity analysis of the model was studied as well. The results show that the improved model describes the variation of G0 well. Land surface temperature (LST) and thermal inertia (Γ) are the two key input variables to the HM model. Compared with in situ G0, there are some differences, mainly due to the differences between remote-sensed LST and the in situ LST. The sensitivity analysis shows that the errors from-7 to-0.5K in LST amplitude and from-300 to 300J m-2 K-1 s-0.5 in Γ will cause about 20% errors, which are acceptable for G0 estimation.

We present in this paper a polynomial fitting method applicable to segments of footprints measured by the Geoscience Laser Altimeter System (GLAS) to estimate glacier thickness change. Our modification makes the method applicable to complex topography, such as a large mountain glacier. After a full analysis of the planar fitting method to characterize errors of estimates due to complex topography, we developed an improved fitting method by adjusting a binary polynomial surface to local topography. The improved method and the planar fitting method were tested on the accumulation areas of the Naimona’nyi glacier and Yanong glacier on along-track facets with lengths of 1000 m, 1500 m, 2000 m, and 2500 m, respectively. The results show that the improved method gives more reliable estimates of changes in elevation than planar fitting. The improved method was also tested on Guliya glacier with a large and relatively flat area and the Chasku Muba glacier with very complex topography. The results in these test sites demonstrate that the improved method can give estimates of glacier thickness change on glaciers with a large area and a complex topography. Additionally, the improved method based on GLAS Data and Shuttle Radar Topography Mission-Digital Elevation Model (SRTM-DEM) can give estimates of glacier thickness change from 2000 to 2008/2009, since it takes the 2000 SRTM-DEM as a reference, which is a longer period than 2004 to 2008/2009, when using the GLAS data only and the planar fitting method.

The time lag between anomalies in precipitation and vegetation activity plays a critical role in early drought detection as agricultural droughts are caused by precipitation shortages. The aim of this study is to explore a new approach to estimate the time lag between a forcing (precipitation) and a response (NDVI) signal in the frequency domain by applying cross-spectral analysis. We prepared anomaly time series of image data on TRMM3B42 precipitation (accumulated over antecedent durations of 10, 60, and 150 days) and NDVI, reconstructed and interpolated MOD13A2 and MYD13A2 to daily interval using a Fourier series method to model time series affected by gaps and outliers (iHANTS) for a dry and a wet year in a drought-prone area in the northeast region of China. Then, the cross-spectral analysis was applied pixel-wise and only the phase lag of the annual component of the forcing and response signal was extracted. The 10-day antecedent precipitation was retained as the best representation of forcing. The estimated phase lag was interpreted using maps of land cover and of available soil water-holding capacity and applied to investigate the difference in phenology responses between a wet and dry year. In both the wet and dry year, we measured consistent phase lags across land cover types. In the wet year with above-average precipitation, the phase lag was rather similar for all land cover types, i.e., 7.6 days for closed to open grassland and 14.5 days for open needle-leaved deciduous or evergreen forest. In the dry year, the phase lag increased by 7.0 days on average, but with specific response signals for the different land cover types. Interpreting the phase lag against the soil water-holding capacity, we observed a slightly higher phase lag in the dry year for soils with a higher water-holding capacity. The accuracy of the estimated phase lag was assessed through Monte Carlo simulations and presented reliable estimates for the annual component.