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Hetao Irrigation District (HTID) has been the largest self-flowing irrigation district with one water intake in Asia, serving an important commercial grain and oil production base in China. The annual grain production in the HTID reached 2.55 million tons in 2018, accounting for 3.9‰ of the total crop cultivation area in China. Therefore, accurate and rapid extraction of crop structure can be of great practical significance in agricultural production for the food security of the HTID. However, it is difficult to distinguish the pixels of major crops in the remote sensing images, due to the severe soil salinization, fragmented and scattered crop distribution, as well as the same crop with the different spectrum of various crops. Moreover, there are the close growth periods of major crops in the HTID, which can mix the elements in the images. In this study, Sentinel-2 high-resolution remote sensing images and the GlobeLand30 dataset were used to extract the crop planting structure of the HTID using the Google Earth Engine cloud computing platform. Nearly 1 200 sample points were filtrated using the OTSU algorithm and Google Earth visual interpretation. The features of spectra, frequently-used vegetation, red-edge vegetation, and crop texture were input into four classifiers, including the Random Forest (RF), Support Vector Machine (SVM), Naive Bayes (NB), Classification and Regression Tree (CART). The Overall Accuracy (OA) and Kappa coefficient were used to evaluate the performances of model for the extraction of crop planting structure. Firstly, the impacts of classification features and classifiers combinations on the classification accuracy were explored to identify the classifier with the highest classification accuracy. Then, the feature optimization was performed on the five irrigation sub-districts using out-of-bag error rates for each irrigation sub-district. Finally, the optimal classifier and feature combinations were achieved to derive the cropping structure of four crops in the HTID in 2018. The results show that the RF classifier presented the highest classification accuracy using all feature bands, where the average OA of the HTID (81%) was 6 percentage points and 11 percentage points higher than that of the SVM and NB classifier, respectively. The Kappa coefficient reached 0.68, which was much higher than the rest. Furthermore, the importance of feature bands filtered by the RF was ranked first for the spectral features, the second for the vegetation features, and last for the gray texture features. The indexes were calculated using red-edge bands, indicating the better performance over the other commonly-used remote sensing vegetation indices in crop recognition. In addition, the feature-optimized scheme was the combination with the highest average OA of 86% and Kappa coefficient of 0.78, while the scheme containing 25 bands of spectral, vegetation and texture features presented an OA of 85% and Kappa coefficient of 0.75. Therefore, the new sights can be offered for extracting crop spatial distribution using remote sensing cloud computing platform in complex planting structure area. The finding can provide a strong reference to adjust the agricultural production structure, and further formulate the food macro-control policies in the Hetao Irrigation District. [ABSTRACT FROM AUTHOR]

Understanding the mechanisms underlying drying-wetting cycles and their consequence for vegetation dynamic is important for sustainable eco-environmental development in alpine regions. We investigated these in this paper by taking the Yarlung Zangbo River (YZR) basin in southeast Qinghai-Tibet Plateau as an example. The standardized precipitation evapotranspiration index (SPEI) in the region was calculated using the global land data assimilation system (GLDAS) from 1982 to 2015 to represent the drying-wetting cycles. The spatiotemporal variation of the dry-wet cycles and the responsive vegetation dynamic was investigated using the remotely sensed NDVI (normal difference vegetation index) from GIMMS (global inventory modeling and mapping studies). The results showed that: 1) Spatiotemporal variation of both precipitation and surface air temperature calculated from GLDAS agreed well with the ground-truth data. 2) The spatiotemporal changes in the dry-wet cycles calculated from the SPEI from 1982 to 2015 showed that the YZR basin became increasingly wet from 1982-1999 but changed course in 2000 and has been become increasingly drier since. In particular, the arid areas showed a tendency of wetting whereas the humid areas tended to become drying. 3) The overall vegetation calculated from the NDVI had been in increase from 1982 to 2000 but changed course in 2000 and has been in decline since. In terms of spatial distribution, areas with higher NDVI value represented vegetation degradation, while areas with lower NDVI represented an improvement in vegetation. 4) Approximately 71.83% of the areas saw a positive correlation between the SPEI and NDVI, mainly in the middle and low reaches of the basin which have a high vegetation coverage. Areas showing negative correlation between SPEI and NDVI were small and not statistically significant. The high consistency between spatiotemporal variation of the NDVI and SPEI indicated that the drying-wetting cycles played an important role in vegetation dynamics. 5) The driving forces of the dry-wet cycles were precipitation, surface air temperature, potential evapotranspiration and soil water content, which were consistent with that estimated from SPEI. In addition, the SPEI showed that soil water content was the dominant factor impacting the drying-wetting in 92.17% of the areas in the region. Results of this study have important implications for evaluating water cycles and the associated vegetation dynamics in alpine regions. [ABSTRACT FROM AUTHOR]

The Qinghai-Tibet Plateau has been greatly sensitive to global climate change in recent years, particularly the change in water storage. However, the differences in multi-source data and calculation have caused great uncertainty in the accurate estimation of terrestrial water storage. Taking the Yarlung Zangbo River Basin located in the southeast of the Qinghai-Tibet Plateau as the study area, this study aims to investigate the spatio-temporal variation characteristics of terrestrial water storage change (TWSC). The performance evaluation of TWSC was conducted using the GRACE (Gravity Recovery and Climate Experiment) data from 2003 to 2017, combining with ERA5 (the fifth-generation reanalysis product of the European Centre for Medium-Range Weather Forecasts) data and GLDAS (Global Land Data Assimilation System) data. Four sets of TWSC were obtained using ERA5 data and GLDAS data from 2003 to 2017, where the terrestrial water balance (PER) and the summation (SS) were used to estimate TWSC, compared with TWSC derived from GRACE. It was found that TWSC estimated by the SS was more consistent than the PER in both temporal and spatial patterns with TWSC derived from GRACE, whereas, TWSC estimated by the SS using GLDAS was most consistent with the results from GRACE over the Yarlung Zangbo River Basin. A sensitivity analysis was carried out using GLDAS and the SS, in order to further investigate the influence of different components on the estimation of TWSC. It was found that SS (GLDAS) performed best for TWSC estimation over the Yarlung Zangbo River Basin, when only soil moisture and snow water equivalent were taken into consideration, with the basin average TWSC correlation coefficient of 0.53. Therefore, the SS calculated from the soil moisture and snow water equivalent was utilized to further explore the long-term temporal and spatial evolution of TWSC from 1948 to 2017 over the Yarlung Zangbo River Basin. TWSC showed a significantly increasing trend from 1948 to 2017. The wavelet analysis indicated that there was an abrupt change in TWSC around 2002 during 1948—2017. There was a significant increase before 2002 (slope=0.024 mm/month, P<0.01), and a significant decrease (slope=−0.397 mm/month, P<0.01) after 2002. The change trend of soil water content and snow water equivalent before and after 2002 is consistent with TWSC. Additional attribution analysis was performed on the abrupt change in TWSC before and after 2002. The contributions of changes in soil moisture and snow water equivalent to TWSC were 61% and 39% during 1948—2002, respectively, whereas, a much more significant domination of soil moisture was identified during 2003—2017. The contributions of changes in soil moisture and snow water equivalent to TWSC were 99% and 1%, respectively. Compared with the change in snow water equivalent, the change in soil moisture dominated the long-term variation of TWSC during 1948—2017. In terms of spatial distribution, TWSC showed a large spatial heterogeneity, mainly in the middle reaches with a high intensity of human activities and the Yarlung Zangbo River Basin with intensive glaciers. The finding can provide reliable data support to explore the spatio-temporal evolution of terrestrial water storage in data-scarce alpine regions. [ABSTRACT FROM AUTHOR]