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The aeromagnetic survey is a common remote sensing tool for detecting iron deposits. The local wavenumber of a magnetic anomaly is used to interpret the edges or positions of the sources, and can involve first- or second-order local wavenumbers. In this paper, we derived a linear equation between the second-order local wavenumber and the source location; therefore, we propose a constraint of first and second-order local wavenumbers. Tests on synthetic data show that the source parameters, computed using a combination of equations that involved different-order local wavenumbers, are closer to the true values and show a smaller spread in estimated values. For gridded data, we proved that the different-order combination allowed us to accurately estimate the source position. When applied to the aeromagnetic data from Hebei province, China, we refined the location of most magnetic features, which we interpreted as possible iron deposits. [ABSTRACT FROM AUTHOR]

ZY-1 02D is China's first civil hyperspectral (HS) operational satellite, developed independently and successfully launched in 2019. It can collect HS data with a spatial resolution of 30 m, 166 spectral bands, a spectral range of 400~2500 nm, and a swath width of 60 km. Its competitive advantages over other on-orbit or planned satellites are its high spectral resolution and large swath width. Unfortunately, the relatively low spatial resolution may limit its applications. As a result, fusing ZY-1 02D HS data with high-spatial-resolution multispectral (MS) data is required to improve spatial resolution while maintaining spectral fidelity. This paper conducted a comprehensive evaluation study on the fusion of ZY-1 02D HS data with ZY-1 02D MS data (10-m spatial resolution), based on visual interpretation and quantitative metrics. Datasets from Hebei, China, were used in this experiment, and the performances of six common data fusion methods, namely Gram-Schmidt (GS), High Pass Filter (HPF), Nearest-Neighbor Diffusion (NND), Modified Intensity-Hue-Saturation (IHS), Wavelet Transform (Wavelet), and Color Normalized Sharping (Brovey), were compared. The experimental results show that: (1) HPF and GS methods are better suited for the fusion of ZY-1 02D HS Data and MS Data, (2) IHS and Brovey methods can well improve the spatial resolution of ZY-1 02D HS data but introduce spectral distortion, and (3) Wavelet and NND results have high spectral fidelity but poor spatial detail representation. The findings of this study could serve as a good reference for the practical application of ZY-1 02D HS data fusion. [ABSTRACT FROM AUTHOR]

Accurate determination of phenological information of crops is essential for field management and decision-making. Remote sensing time-series data are widely used for extracting phenological phases. Existing methods mainly extract phenological phases directly from individual remote sensing time-series, which are easily affected by clouds, noise, and mixed pixels. This paper proposes a novel method of phenological phase extraction based on the time-weighted dynamic time warping (TWDTW) algorithm using MODIS Normalized Difference Vegetation Index (NDVI) 5-day time-series data with a spatial resolution of 500 m. Firstly, based on the phenological differences between winter wheat and other land cover types, winter wheat distribution is extracted using the TWDTW classification method, and the results show that the overall classification accuracy and Kappa coefficient reach 94.74% and 0.90, respectively. Then, we extract the pure winter-wheat pixels using a method based on the coefficient of variation, and use these pixels to generate the average phenological curve. Next, the difference between each winter-wheat phenological curve and the average winter-wheat phenological curve is quantitatively calculated using the TWDTW algorithm. Finally, the key phenological phases of winter wheat in the study area, namely, the green-up date (GUD), heading date (HD), and maturity date (MD), are determined. The results show that the phenological phase extraction using the TWDTW algorithm has high accuracy. By verification using phenological station data from the Meteorological Data Sharing Service System of China, the root mean square errors (RMSEs) of the GUD, HD, and MD are found to be 9.76, 5.72, and 6.98 days, respectively. Additionally, the method proposed in this article is shown to have a better extraction performance compared with several other methods. Furthermore, it is shown that, in Hebei Province, the GUD, HD, and MD are mainly affected by latitude and accumulated temperature. As the latitude increases from south to north, the GUD, HD, and MD are delayed, and for each 1° increment in latitude, the GUD, HD, and MD are delayed by 4.84, 5.79, and 6.61 days, respectively. The higher the accumulated temperature, the earlier the phenological phases occur. However, latitude and accumulated temperature have little effect on the length of the phenological phases. Additionally, the lengths of time between GUD and HD, HD and MD, and GUD and MD are stable at 46, 41, and 87 days, respectively. Overall, the proposed TWDTW method can accurately determine the key phenological phases of winter wheat at a regional scale using remote sensing time-series data. [ABSTRACT FROM AUTHOR]

The forest canopy height (FCH) plays a critical role in forest quality evaluation and resource management. The accurate and rapid estimation and mapping of the regional forest canopy height is crucial for understanding vegetation growth processes and the internal structure of the ecosystem. A stacking algorithm consisting of multiple linear regression (MLR), support vector machine (SVM), k-nearest neighbor (kNN), and random forest (RF) was used in this paper and demonstrated optimal performance in predicting the forest canopy height by synergizing Sentinel-2 images acquired from the cloud-based computation platform Google Earth Engine (GEE) with data from ICESat-2 (Ice, Cloud, and Land Elevation Satellite-2). This research was conducted to achieve continuous mapping of the canopy height of plantations in Saihanba Mechanical Forest Plantation, which is located in Chengde City, northern Hebei province, China. The results show that stacking achieved the best prediction accuracy for the forest canopy height, with an R2 of 0.77 and a root mean square error (RMSE) of 1.96 m. Compared with MLR, SVM, kNN, and RF, the RMSE obtained by stacking was reduced by 25.2%, 24.9%, 22.8%, and 18.7%, respectively. Since Sentinel-2 images and ICESat-2 data are publicly available, this opens the door for the accurate mapping of the continuous distribution of the forest canopy height globally in the future. [ABSTRACT FROM AUTHOR]

The data assimilation technique is an effective method for reducing initial condition errors in numerical weather prediction (NWP) models. This paper evaluated the potential of the weather research and forecasting (WRF) model and its three-dimensional data assimilation (3DVar) module in improving the accuracy of rainfall-runoff prediction through coupled atmospheric-hydrologic systems. The WRF model with the assimilation of radar reflectivity and conventional surface and upper-air observations provided the improved initial and boundary conditions for the hydrological process; subsequently, three atmospheric-hydrological systems with variable complexity were established by coupling WRF with a lumped, a grid-based Hebei model, and the WRF-Hydro modeling system. Four storm events with different spatial and temporal rainfall distribution from mountainous catchments of northern China were chosen as the study objects. The assimilation results showed a general improvement in the accuracy of rainfall accumulation, with low root mean square error and high correlation coefficients compared to the results without assimilation. The coupled atmospheric-hydrologic systems also provide more accurate flood forecasts, which depend upon the complexity of the coupled hydrological models. The grid-based Hebei system provided the most stable forecasts regardless of whether homogeneous or inhomogeneous rainfall was considered. Flood peaks before assimilation were underestimated more in the lumped Hebei model relative to the other coupling systems considered, and the model seems more applicable for homogeneous temporal and spatial events. WRF-Hydro did not exhibit desirable predictions of rapid flood process recession. This may reflect increasing infiltration due to the interaction of atmospheric and land surface hydrology at each integration, resulting in mismatched solutions for local runoff generation and confluence. [ABSTRACT FROM AUTHOR]

The southern part of the Hebei Province is one of China's major crop-producing regions. Due to the continuous decline in groundwater level, agricultural water use is facing significant challenges. Precision agricultural irrigation management is undoubtedly an effective way to solve this problem. Based on multisource data (time series soil moisture active passive (SMAP) data, Moderate Resolution Imaging Spectroradiometer (MODIS) normalized difference vegetation index (NDVI) and evapotranspiration (ET), and meteorological station precipitation), the irrigation signal (frequency, timing and area) is detected in the southern part of the Hebei Province. The SMAP data was processed by the 5-point moving average method to reduce the error caused by the uncertainty of the microwave data derived SM. Irrigation signals can be detected by removing the precipitation effect and setting the SM change threshold. Based on the validation results, the overall accuracy of the irrigation signal detection is 77.08%. Simultaneously, considering the spatial resolution limitation of SMAP pixels, the SMAP irrigation area was downscaled using the winter wheat area extracted from MODIS NDVI. The analytical results of 55 winter wheat samples (5 samples in a group) showed that winter wheat covered by one SMAP pixel had an 82.72% growth consistency in surface water irrigation period, which can indicate a downscaling effectiveness. According to the above statistical analysis, this paper considers that although the spatial resolution of SMAP data is insufficient, it can reflect the change of SM more sensitively. In areas where the crop pattern is relatively uniform, the introduction of high-resolution crop pattern distribution can be used not only to detect irrigation signals but also to validate the effectiveness of irrigation signal detection by analyzing crop growth consistency. Therefore, the downscaling results can indicate the true winter wheat irrigation timing, area and frequency in the study area. [ABSTRACT FROM AUTHOR]