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ICESat ‐2’s laser data has the highest elevation accuracy up to date, and its observation range covers the global land, which can be used as basic data for the high ‐ precision global ground elevation reference. Based on ICESat‐2 /ATLAS global laser data product ATL08, this paper obtained ICESat‐2 la‐ ser points on the global land, and studied the method of extracting global elevation control points based on elevation reference and attribute parameters, and used reference elevation data to verify their accuracy. The obtained laser points were verified by airborne laser data in Shandong experimental field and Henan experi‐ mental field in China. And the root mean square error (RMSE) were 1.11 m and 1.39 m respectively before filtering. After filtering with elevation reference and slope constraints, the mean RMSE were 0.69 m and 0.57 m respectively, and the corresponding data retention rates were 61.38% and 60.00%, which proved that the proposed method in this paper could effectively improve the elevation accuracy while ensuring the data retention rate. The airborne laser data from the western, central and eastern United States experimen‐ tal fields were used to verify the elevation control points. The RMSE of each experimental field was less than 0.9 m, which proved that the extraction method proposed in this paper could be used to extract eleva‐ tion control points worldwide. This method can automatically extract global elevation control points with high density and high precision, providing support for the stereo mapping of domestic high‐resolution satel‐ lites without or with few ground control points, and assistance in evaluating the quality of DEM/DSM products. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to solve the prediction problem of nonlinear sequences with multiperiodic features, and a multiperiod grey prediction model based on grey theory and Fourier series is established. For nonlinear sequences with both trend and periodic features, the empirical mode decomposition method is used to decompose the sequences into several periodic terms and a trend term; then, a grey model is used to fit the trend term, and the Fourier series method is used to fit the periodic terms. Finally, the optimization parameters of the model are solved with the objective of obtaining a minimum mean square error. The novel model is applied to research on the loss rate of agricultural droughts in Henan Province. The average absolute error and root mean square error of the empirical analysis are 0.3960 and 0.5086, respectively. The predicted results show that the novel model can effectively fit the loss rate sequence. Compared with other models, the novel model has higher prediction accuracy and is suitable for the prediction of multiperiod sequences. [ABSTRACT FROM AUTHOR]

Abstract: The local singularity model involved in the context of multifractal theory calculates an index which indicates the local elemental enrichment or depletion and can be used to delineate the weak anomalies. The index, called singularity exponent can be calculated using the total amount model and density model, respectively. The former is based on the power–law relationship between the cumulative concentrations of the neighboring samples in variable cell sizes and cell size, while the latter is between the average concentration and cell size. The influence of the two models on the calculation of the local singularity was estimated in this paper. The total amount model was thought to be preferable in calculating the singularity exponent, which was applied to stream sediment data from the Xiong''ershan gold and molybdenum ore district, Western Henan province, China, and an algorithm based on the window-based method was advanced to map the local singularity distributions of Au and Mo. The thresholds of Au and Mo calculated on the singularity map were used to delineate anomalies which were associated not only with known gold and molybdenum ore deposits in the northern part of EW-trending Machaoying fault, but also with the areas south of Machaoying fault where no gold deposits have yet been discovered. Neither were they clearly identified by the Concentration–Area model and mean+standard deviation of Au concentrations. The results show that weak anomalies hidden within the strong variance of background can be well identified by the local singularity model, and the delineated anomalies should be considered as favorable target areas. [Copyright &y& Elsevier]

Cropping intensity is a key indicator for evaluating grain production and intensive use of cropland. Timely and accurately monitoring of cropping intensity is of great significance for ensuring national food security and improving the level of national land management. In this study, we used all Sentinel-2 images on the Google Earth Engine cloud platform, and constructed an improved peak point detection method to extract the cropping intensity of a heterogeneous planting area combined with crop phenology. The crop growth cycle profiles were extracted from the multi-temporal normalized difference vegetation index (NDVI) and land surface water index (LSWI) datasets. Results show that by 2020, the area of single cropping, double cropping, and triple cropping in the Henan Province are 52,236.9 km2, 74,334.1 km2, and 1927.1 km2, respectively; the corresponding producer accuracies are 86.12%, 93.72%, and 91.41%, respectively; the corresponding user accuracies are 88.99%, 92.29%, and 71.26%, respectively. The overall accuracy is 90.95%, and the Kappa coefficient is 0.81. Using the sown area in the statistical yearbook data of cities in the Henan Province to verify the extraction results of this paper, the R2 is 0.9717, and the root mean square error is 1715.9 km2. This study shows that using all the Sentinel-2 data, the phenology algorithm, and cloud computing technology has great potential in producing a high spatio-temporal resolution dataset for crop remote sensing monitoring and agricultural policymaking in complex planting areas. [ABSTRACT FROM AUTHOR]

• Drought loss models of wheat are built using two different statistical models. • The important SPEI variables recognized by two methods are different. • The random forest-derived model outperforms the stepwise regression model. Agricultural drought loss models provide services for the rapid risk assessment of agricultural disasters, and regional disaster prevention and mitigation efforts. This paper takes wheat as an example, and chooses counties dominated by rain-fed farmland in Henan Province as the study area. Counties dominated by rain-fed farmland are determined by setting a rain-fed threshold that is related to the proportion of the effective irrigation area to the cultivated land area. Modeling samples are screened by considering both drought occurrence time and wheat yield reductions. Under different thresholds (30%, 40%, 50% and 60%), we use the yield loss ratio as the dependent variable and 24 standardized precipitation evapotranspiration index parameters as independent variables to build drought loss models using both a multivariate stepwise regression model and a random forest model. Yield loss ratio from 1990 to 2015 is calculated by decomposing historical wheat yield time series. 24 standardized precipitation evapotranspiration index variables are 1–3 months' time scale standardized precipitation evapotranspiration index during the growth period (from October to May of the following year) of winter wheat in Henan Province. The results show that the random forest-derived model outperforms the stepwise regression model in all tests. The accuracy of all the models increases with an increase of the proportion of the rain-fed threshold. When the rain-fed threshold is 60%, the R2 values of the random forest model and the multivariate stepwise regression equation are 0.720 and 0.523, respectively. The validation results show that the mean absolute error and the root mean square error of the multivariate stepwise regression are 1.38 times and 1.31 times larger than the mean absolute error and the root mean square error from the random forests model. Moreover, both models identify that standardized precipitation evapotranspiration indices in October (sowing/planting stage) and February (overwintering stage) are important variables. However, the multivariate stepwise regression model fails to recognize the importance of standardized precipitation evapotranspiration indices during April–May (filling stage). [ABSTRACT FROM AUTHOR]