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Accurate monitoring of crop drought thresholds at different growth periods is crucial for drought monitoring. In this study, the canopy temperature (Tc) of winter wheat ('Weilong 169' variety) during the three main growth periods was extracted from high-resolution thermal and multispectral images taken by a complete unmanned aerial vehicle (UAV) system. Canopy-air temperature difference (ΔT) and statistic Crop Water Stress Index (CWSIsi) indicators were constructed based on Tc. Combined experiment data from the field and drought thresholds for the ΔT and CWSIsi indicators for different drought levels at three main growth periods were monitored. The results showed a strong correlation between the Tc extracted using the NDVI-OTSU method and ground-truth temperature, with an R2 value of 0.94. The CWSIsi was more stable than the ΔT index in monitoring the drought level affecting winter wheat. The threshold ranges of the CWSIsi for different drought levels of winter wheat at three main growth periods were as follows: the jointing–heading period, where the threshold ranges for normal, mild drought, moderate drought, and severe drought are <0.30, 0.30–0.42, 0.42–0.48, and >0.48, respectively; the heading–filling period, where the threshold ranges for normal, and mild, moderate, and severe drought are <0.33, 0.33–0.47, 0.44–0.53, and >0.53, respectively; and the filling–maturation period, where the threshold ranges for normal, mild drought, moderate drought, and severe drought are <0.41, 0.41–0.54, 0.54–0.59, and >0.59, respectively. The UAV thermal threshold method system can improve the accuracy of crop drought monitoring and has considerable potential in crop drought disaster identification. [ABSTRACT FROM AUTHOR]

Existing gauging networks are sparse and not readily available in real-time over the transboundary Lancang–Mekong River (LMR) basin, making it difficult to accurately identify drought. In this study, we aimed to build an operational real-time Lancang–Mekong drought monitor (LMDM), through combining satellite real-time data and the Variable Infiltration Capacity (VIC) hydrological model at a 0.25° spatial resolution. Toward this, three VIC runs were conducted: (1) a 60-year (1951–2010) historical simulation driven by Princeton's global meteorological forcing (PGF) for yielding 'normal' conditions (PGF-VIC), wherein the VIC was calibrated with 20-year observed streamflow at six hydrological stations; (2) a short-period (2011–2014) simulation to bridge the gap between the historical and the real-time modeling; (3) the real-time (2015–present) simulation driven by bias-corrected satellite data, wherein the real-time soil moisture (SM) estimate was expressed as percentile (relative to the 'normal') for drought monitoring. Results show that VIC can successfully reproduce the observed hydrographs, with the Nash–Sutcliffe efficiency exceeding 0.70 and the relative bias mostly within 15%. Assessment on the performance of LMDM shows that the real-time SM estimates bear good spatial similarity to the reference, with the correlation coefficient beyond 0.80 across >70% of the domain. In terms of drought monitoring, the LMDM can reasonably reproduce the two recorded droughts, implying extreme droughts covering the Lower LMR during 2004/05 and widespread severe 2009/10 drought across the upper domain. The percentage drought area implied by the LMDM and the reference is close, corresponding to 66% and 60%, 43% and 40%, and 44% and 36% for each typical drought month. Since January 2015, the LMDM was running in an operational mode, from which the 2016 unprecedented drought was successfully identified in Mekong Delta. This study highlights the LMDM's capability for reliable real-time drought monitoring, which can serve as a valuable drought early warning prototype for other data-poor regions. [ABSTRACT FROM AUTHOR]

A high-resolution and near real-time drought monitoring dataset has not been made readily available in drought-prone China, except for the low-resolution global product. Here we developed a set of near real-time meteorological drought data at a 0.25° spatial resolution over China, by seamlessly merging the satellite-based near real-time (RT) precipitation (3B42RTv7) into the high-quality gauge-based retrospective product (CN05.1) using the quantile-mapping (QM) bias-adjustment method. Comparing the standard precipitation index (SPI) from the satellite-gauge merged product (SGMP) with that from the retrospective ground product CN05.1 (OBS) shows that the SGMP reproduces well the observed spatial distribution of SPI and the pattern of meteorological drought across China, at both the 6-month and 12-month time scales. In contrast, the UN-SGMP generated by merging the unadjusted raw satellite precipitation into the gauging data shows systematical overestimation of the SPI, leaving less meteorological droughts to be identified. Furthermore, the SGMP is found to be able to capture the inter-annual variation of percentage area in meteorological droughts. These validation results suggest that the newly developed drought dataset is reliable for monitoring meteorological drought dynamics in near real-time. This dataset will be routinely updated as the satellite RT precipitation is made available, thus facilitating near real-time drought diagnosis in China. [ABSTRACT FROM AUTHOR]