Improving irrigation water management is a key concern for the agricultural sector, and it requires extensive and comprehensive tools that provide a complete knowledge of crop water use and requirements. This study presents a novel methodology to explicitly estimate daily gross and net crop water requirements, actual crop water use, and irrigation efficiency of center pivot irrigation systems, by mainly utilizing the Sentinel-2 MultiSpectral Instrument (MSI) imagery at the farm scale. ETMonitor model is adapted to estimate actual water use (as the sum of canopy transpiration and evaporation of water intercepted by canopy and evaporation from soil) at daily/10-m resolution, benefiting from the high-resolution Sentinel-2 data and thus to assess the irrigation efficiency at the farm scale. The gross irrigation water requirement is estimated from the net crop water requirement and the water loss, including the water droplet evaporation directly into the air during application before droplets fall on the canopy and canopy interception loss. The method was applied to a pilot farmland with two major crops (wheat and potato) in the Inner Mongolia Autonomous Region of China, where modern equipment and appropriate irrigation methods are deployed for efficient water use. The estimated actual crop water use showed good agreement with the ground observations, e.g. the determination coefficients range from 0.67 to 0.81 and root mean square errors range from 0.56 mm/day to 1.24 mm/day for wheat and potato when comparing the estimated evapotranspiration with the measurement by the eddy covariance system. It also showed that the losses of total irrigated volume were 25.4% for wheat and 23.7% for potato, respectively, and found that the water allocation was insufficient to meet the water requirement in this irrigated area. This suggests that the amount of water applied was insufficient to meet the crop water requirement and the inherent water losses in the center pivot irrigation system, which imply the necessity to improve the irrigation practice to use the water more efficiently.

In the Tibetan Plateau (TP) region, the foreseeable increase in air temperature may have profound and complex effects on the local hydrological cycle, and is likely to increase water loss from the land surface to the atmosphere through evapotranspiration (ET). Quantifying ET and its regulatory mechanisms are major challenges for understanding the water cycle and land-atmosphere interactions in the TP region. We evaluated the performance of several Earth observation-based ET datasets in the TP region, and explored the spatiotemporal variation of ET in the same region. The accuracy of different global ET datasets was evaluated, and ETMonitor and PML-V2 provide the best accuracy with overall high correlation, low bias, and low root mean square error. ETMonitor ET is also the only product with both high spatial (~1 km) and temporal (daily) resolution. ETMonitor ET may reflect the effect of mountain topography on ET better than other global products, i.e., ET values are higher in the humid valleys with denser vegetation cover and higher soil moisture, and ET values are lower on the mountain slopes at higher elevations with less vegetation cover and colder climate. Other ET products failed to capture the spatial patterns of ET in the mountainous regions, and this suggests that the spatial resolution is not the only dominant factor leading to the poorer performance of these ET products in the mountain regions of the TP. The results show that multi-year average ET is 339 mm/yr in the TP region during 2000-2021, which accounts for about 51% of the total precipitation in the TP region. From 2000 to 2021, ET over the Tibetan Plateau shows an overall increasing trend with large spatial variability.

Flash droughts tend to cause severe damage to agriculture due to their characteristics of sudden onset and rapid intensification. Early detection of the response of vegetation to flash droughts is of utmost importance in mitigating the effects of flash droughts, as it can provide a scientific basis for establishing an early warning system. The commonly used method of determining the response time of vegetation to flash drought, based on the response time index or the correlation between the precipitation anomaly and vegetation growth anomaly, leads to the late detection of irreversible drought effects on vegetation, which may not be sufficient for use in analyzing the response of vegetation to flash drought for early earning. The evapotranspiration-based (ET-based) drought indices are an effective indicator for identifying and monitoring flash drought. This study proposes a novel approach that applies cross-spectral analysis to an ET-based drought index, i.e., Evaporative Stress Anomaly Index (ESAI), as the forcing and a vegetation-based drought index, i.e., Normalized Vegetation Anomaly Index (NVAI), as the response, both from medium-resolution remote sensing data, to estimate the time lag of the response of vegetation vitality status to flash drought. An experiment on the novel method was carried out in North China during March–September for the period of 2001–2020 using remote sensing products at 1 km spatial resolution. The results show that the average time lag of the response of vegetation to water availability during flash droughts estimated by the cross-spectral analysis over North China in 2001–2020 was 5.9 days, which is shorter than the results measured by the widely used response time index (26.5 days). The main difference between the phase lag from the cross-spectral analysis method and the response time from the response time index method lies in the fundamental processes behind the definitions of the vegetation response in the two methods, i.e., a subtle and dynamic fluctuation signature in the response signal (vegetation-based drought index) that correlates with the fluctuation in the forcing signal (ET-based drought index) versus an irreversible impact indicated by a negative NDVI anomaly. The time lag of the response of vegetation to flash droughts varied with vegetation types and irrigation conditions. The average time lag for rainfed cropland, irrigated cropland, grassland, and forest in North China was 5.4, 5.8, 6.1, and 6.9 days, respectively. Forests have a longer response time to flash droughts than grasses and crops due to their deeper root systems, and irrigation can mitigate the impacts of flash droughts. Our method, based on cross-spectral analysis and the ET-based drought index, is innovative and can provide an earlier warning of impending drought impacts, rather than waiting for the irreversible impacts to occur. The information detected at an earlier stage of flash droughts can help decision makers in developing more effective and timely strategies to mitigate the impact of flash droughts on ecosystems.

The special environment conditions in high altitude areas leads to serious cracking and peeling of road hot-melt marking coatings. In order to improve the durability of marking paint, a durable hot-melt marking paint was designed by modifying the paint with toughening-agent, rheological agent, and anti-aging agent. The modification mechanism of the modified coating was revealed through TG and FTIR analysis. The low-temperature anti cracking, adhesion, and anti-aging properties of the modified coating were studied by low-temperature bending test, interlaminar shear test and UV aging test. Besides this, the workability of modified coatings was tested onsite. The results showed that the mass loss rate of the rheological agent is 0.15% at 240 °C, the modified coating had good thermal stability within the mixing temperature range of the coating. There was no chemical change between the modifier and the coating, which was a physical blend. The modified marking paint had good fluidity, softening point and drying time, but its compressive strength was slightly reduced. With the increase of the content of the toughener, the low temperature crack resistance and adhesion of the marking coating gradually increase. When the toughener is 5%, the flexural tensile strain of the marking coating beam increased by 79.1%, and the adhesion strength of asphalt concrete increased by 53.4%. The anti-aging agent can shield most of the UV radiation and improve the anti-aging property of the coating by about 30%. The field process validation achieved the expected results. The modifier coating has excellent low temperature crack resistance, adhesion, UV aging resistance, and other properties, and has good application prospects in harsh environment areas.

A drought-induced water storage deficit index (D-WSDI) is proposed to quantify the response of GRACE-based terrestrial water storage change to meteorological drought and the impact of drought on water storage deficit. D-WSDI is defined as the normalized residual component of GRACE time-series data after removing the long-term trend and seasonal components. The evaluation based on the Emergency Events Database (EM-DAT) showed that more than 90% of global drought events from 2002 to 2019 led to a water storage deficit, which can be detected by the proposed D-WSDI. The severity of the water storage deficit caused by drought increases with the extending drought duration. An average of 73% of water storage deficit months at the global scale is related to precipitation shortages. The cumulative precipitation deficit in relatively short periods of less than 9 months can lead to the water storage deficit in low-latitude regions, whereas a longer time scale is required to lead to a water storage deficit in high-latitude regions. The negative monthly precipitation anomaly of about -20% can lead to a water storage deficit in high rainfall regions, whereas the negative precipitation anomaly can reach -80% in arid and semiarid areas. D-WSDI holds the capability to quantify the water storage deficit caused by drought, especially in the regions with terrestrial water storage change influenced by the long-term trends in climate and anthropogenic activities, and can be used as an index of drought monitoring with similar or superior performance compared to some traditional drought indices.

Satellite-based Earth Observation systems archived a variety of vegetation products during the last 50 years, which can reveal regional to global ecosystem dynamics across diverse spatiotemporal scales. The anomaly metrics such as Vegetation Condition Index (VCI) defined by comparing the current vegetation growth condition to historical average status based on long-term EO-based vegetation products were widely used to delineate abnormal vegetation variation exerted by either climatic or anthropogenic factors (e.g., droughts, wildfires). However, currently available long-term vegetation products may differ from each other in terms of sensors (observational platform or spectral bands), bio-physical definitions (e.g., NDVI, EVI, LAI, and VOD), spatiotemporal resolution, as well as the time-spans, which results in inconsistency across these vegetation products. Taking the VCI as an example, this study evaluated the uncertainty of vegetation anomalies detected based on different vegetation products over the middle reach of the Yangtze River by explicitly considering the effect of sensors, biophysical definitions, and time-spans. The preliminary results showed that VCI derived from NDVI products from different sensors (AVHRR vs. MODIS) induced significant inconsistent anomalies over most landscapes. The differences resulting from products with different biophysical definitions (NDVI vs. EVI, LAI, and VOD) are much lower than those from different sensors but still significant over specific areas. As for the time-spans, the 20-year NDVI based VCI presented a considerable reduction in variance over the study area on average compared to VCI calculated based on 5-year NDVI. In summary, caution should be taken when applying EO-based vegetation products for vegetation anomalies mapping, especially for quantitative assessment.

Rainfall is a key driver of terrestrial vegetation. Clarifying the response mechanism of vegetation to rainfall can advance the understanding of expected changes in ecosystems under projected rainfall scenarios. Besides the rainfall amount over a period of time, the frequency, duration and intensity are of importance in driving ecosystem processes. The pulsating nature of rainfall forcing and of vegetation response applies particularly to arid and semi – arid regions and led to conceptualize the “pulse-reserve” paradigm, which we used to explain the approach we propose in this study. We introduce the Transfer Function Analysis (TFA) method in the frequency domain to capture the response of vegetation to rainfall at multiple temporal scales. The TFA method determines coherence, gain, and phase to characterize the existence, strength, and time-lag, respectively, of the vegetation response to rainfall at different temporal scales. Specifically, the coherence measures the existence of the response and only with a significant response are the gain and phase values significant and valuable for further analysis. The gain measures the strength of the relationship between fluctuations in vegetation growth and fluctuations in rainfall, while the phase value (i.e. the time-lag) measures how fluctuations in vegetation growth lag (or lead) fluctuations in rainfall. The TFA method was applied to the 34-years (1982–2015) NDVI3g and CHIRPS precipitation dataset in the Sahel-Sudano-Guinean region (20°W ~ 60°E, 0–25°E). The Sahelian zone was characterized by a significant vegetation response to rainfall across all inter- and intra- annual time-scales, while the Sudano-Guinean zone was dominated by significant response at annual or 6-month scales. The negative phase lag indicated that rainfall variation normally led NDVI change for most areas and across timescales. However, a positive phase observed in part of the tropical rainforest area indicated that NDVI changes led rainfall variations, which may be caused by the strong vegetation-rainfall feedback through recycling of precipitation by evapotranspiration. In summary, these results suggested that the TFA method is a powerful tool to quantify the vegetation-rainfall response regime across a range of timescales, as conceptualized by the “pulse-reserve” paradigm. Unraveling the response of fluctuations in vegetation growth to separate components of the forcing by precipitation might improve our understanding of environmental change in the past decades in the Sahel - Sudan - Guinean region.

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.

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 Pan-Eurasian and African Continents are characterized by large ranges of climates varying from humid, semi-humid, semi-arid and arid regions, and great challenges exist in water allocation for different sectors that related to water resource and food security, which depends strongly on the water use information. Quantitative information on water use is also important to understand the effectiveness of water allocation and further to prevent from water stress resulted by drought in water-scarce regions. Explosive development of satellite remote sensing observations provide great chance to provide useful spatiotemporal information for quantifying the water use at regional to global scales. In this paper, a process-based model ETMonitor was used in combination with biophysical and hydrological parameters retrieved from earth observations to estimate the actual evapotranspiration, i.e. the agricultural and ecological water use. The total water use is also partitioned into beneficial part, e.g. plant transpiration, and non-beneficial part, e.g. soil evaporation and canopy rainfall interception, according to the water accounting framework. The estimated water use show good agreements with the ground observation, indicating the ability of ETMonitor for global and continental scale water use estimation. The spatial and temporal patterns of the water use in the Pan-Eurasian and African Continents were further analysed, while large spatial variation of water use was convinced. Current study also highlights the great capability of satellite observations in studying the regional water resource and continental water cycle.

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.