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Accurate and effective drought monitoring using remote sensing technology is essential for regional and global drought warning and forecasting. Especially, soil moisture (SM) is one of the key potential factors affecting agricultural drought. At present, most studies ignore the fact that soil moisture is a complex nonlinear coupling system，the research only using visible light, near infrared, short-wave infrared, thermal infrared and other remote sensing drought index has certain limitations in SM inversion. A new method of retrieving farmland SM of winter wheat at returning green stage based on MODIS and radial basis function neural network (RBFNN) is presented in this paper. Firstly, the adaptability of various MODIS-derived drought indices is analyzed in the period of seedling establishment of winter wheat, including soil water content, crop morphological change in water requirement, crop canopy water content and temperature and other parameters. Secondly, the correlation between the original remote sensing drought indices and the soil relative humidity 10 cm soil layer was analyzed. A comprehensive evaluation set of apparent thermal inertia (ATI), vegetation supply water index (VSWI), enhanced vegetation index (EVI), normalized difference infrared index band7 (NDIIB7), normalized multi-band drought index (NMDI) and temperature condition index (TCI) are selected to invert soil moisture of farmland. Finally, combining MODIS remote sensing drought index with RBFNN, the SM of farmland was retrieved synergistically, and the retrieved results were compared with those of BP-NN and linear regression (LR) models. The experimental study is conducted in Henan province of China, MODIS reflectance products (MOD09A1) and temperature products (MOD11A2) at returning green stage of winter wheat from 2001 to 2012 were used to extract remote sensing drought indices, and the global land cover product of MODIS (MCD12Q1) is selected to obtain cropland distributions. Soil moisture data is derived from the “China crop growth and development and farmland soil moisture ten-day dataset”, which contains the soil relative humidity of 10 and 20 cm soil layer observed every 10 days by 17 soil moisture stations. The results show that the average accuracy of SM inversion model using RBFNN and multiple drought indices is 93.27%, which is increased by 2.92 and 9.97 percentage points compared with BP-NN and LR model, respectively. Most of the data points of the three models are concentrated around the 1:1 line, which indicate that there is a good correlation between the predicted value and the measured value. Compared with BP-NN model and LR model, the deviation between predicted value of RBFNN model and the 1:1 line is the smallest, the higher the regression correlation coefficient is, the higher the determination coefficient is. SM is significantly correlated with ATI, NDIIB7 and VSWI in the early growth stage of winter wheat. Multi-band remote sensing drought monitoring indices can comprehensively reflect the changes of crop physiology and morphology under soil water stress, and also determine the retrieval accuracy of the humidity model simultaneously. In this paper, only winter wheat in returning green stage is selected as the research object, and the comprehensive evaluation index set selected is not suitable for SM inversion of other growth stages of winter wheat, the adaptability of different remote sensing drought indices in different growth stages needs further experimental study. The study provides a new case for regional SM inversion from remote sensing-based drought monitoring indices and neural network. [ABSTRACT FROM AUTHOR]

Understanding the historical change trajectory of Land Use/Land Cover Change (LUCC) is helpful to analyze the land use trends under the interference of human activities and changes in the natural environment, thereby helping decision makers to eliminate the negative impact of unreasonable land use patterns to the greatest extent. Yellow River Basin is the fifth longest river in the world, its ecological environment continues to deteriorate due to the rapid population growth and urban expansion, which has become one of the regions with the most serious soil erosion in China. Aiming to promote the ecological quality and high-quality development of social economy under the human interference and natural environment change, we analyzed the spatiotemporal dynamic of the LUCC in the Yellow River Basin (Henan section) from the watershed scale and proposed policy recommendations. We introduced the chord diagram visualization model to intuitively show the flow, direction and diversity of land cover changes, which would enrich the visual research method system of the land cover quantity transfer trajectory. The land use change index, chord diagram model and gravity center transfer model were used in this paper to analyze the spatial-temporal evolution trajectory and policy implications of the land use pattern in the Yellow River Basin (Henan section) in the past 30 years based on the land use remote sensing data in 1990, 2000, 2010 and 2018. The results showed that: 1) Ecological conservation land (35%) was mainly concentrated in the mountainous areas with higher elevation in the middle reaches; food security land (55%) and production and living land (10%) were mainly distributed in the central and eastern plains. 2) The outflows and inflows of dry land from 1990 to 2018 showed significant dominance; paddy field, water and grassland were dominated by outflow; urban land, rural settlements and other construction land were dominated by inflow. 3) The land use change in central cities had significant regional driving effects throughout the period. 4) The gravity center of food security land and ecological conservation land moved to the West and the area showed a decreasing trend, which proved that the high-quality cultivated land in the plain area in the lower reaches decreased and the ecosystem service function gradually weakened; The shift of gravity center of urban land to the East indicated that the social and economic development in the lower reaches were gradually active. The shift of gravity center of rural residential and other construction land to the West indicated that the population scale in the middle reaches was gradually increasing. [ABSTRACT FROM AUTHOR]

• A deep learning algorithm is used to build a comprehensive drought monitoring model. • Multi-source remote sensing data is processed as input data. • The comprehensive drought model has good applicability in the monitoring of meteorological drought and agricultural drought. • The model successfully monitored a drought process in Henan Province, China. • This study provides a new method for the comprehensive assessment of regional drought. Drought is a popular scientific issue in global climate change research. Accurate monitoring of drought has important implications for the sustainable development of regional agriculture in the context of increasingly complex global climate change. Deep learning is a widely used technique in the field of artificial intelligence. However, ongoing on drought monitoring using deep learning is relatively scarce. In this paper, the various hazard factors in drought development were comprehensively considered based on satellite data including Moderate Resolution Imaging Spectroradiometer (MODIS) and tropical rainfall measuring mission (TRMM) as multi-source remote sensing data. By using the deep learning technique, a comprehensive drought monitoring model was constructed and tested in Henan Province of China as an example. The results showed that the comprehensive drought model has good applicability in the monitoring of meteorological drought and agricultural drought. There was a significant positive correlation between the drought indicators of the model output and the comprehensive meteorological drought index (CI) measured at the site scale. The consistency rate of the drought grade of the two models was 85.6% and 79.8% for the training set and the test set, respectively. The correlation coefficient between the drought index of the model and the standard precipitation evapotranspiration index (SPEI) was between 0.772 and 0.910 (P < 0.01), which indicated a strong level of significance. The correlation coefficient between the drought index of the model and the soil relative moisture at a 10 cm depth was greater than 0.550 (P < 0.01), and there was a good correlation between them. This study provides a new method for the comprehensive assessment of regional drought. [ABSTRACT FROM AUTHOR]