The Assessment of Soil Quality on the Arable Land in Yellow River Delta Combined with Remote Sensing Technology

Soil quality assessment is essential to improve the understanding of soil quality and make proper agricultural practices. However, soil quality assessments are extremely difficult to implement in a large-scale area, since they are time and labor consuming. Remote sensing technique gained more attention in plant and soil information monitoring recently for its high effi-ciency and convenience. But seldom studies tested the applicability of remote sensing techniques before implementing. This study conducted the soil quality assessment in a typical agricultural county in the Yellow River delta (Kenli). We found the soil quality in Kenli was dominantly in the low grade (71.85%), with deficient nutrient (SOM and TN), poor structure (high BD) and high EC. Salinity is the primary limiting factor for soil quality in Kenli, and adjustment of soil salinization through suitable farming practices such as organic fertilizers application, irrigation for leaching, and salt-tolerant crop planting is the key point for soil quality improvement. We obtained the normalized difference vegetation index (NDVI) of the study area by remote sensing technique, and found the high correlation between NDVI and soil quality indicator (SOM, TN and EC) and yield. The NDVI can help to study the soil conditions as a soil quality assessment indicator. More studies about the ap-plication of remote sensing technique on soil quality detecting are expected.

The analysis of gales over the “Maritime Silk Road” with remote sensing data

The 21st century ＂Maritime Silk Road＂ strategy is a significant part of the belt and road initiatives of China. The cognition and investigation of ocean environment is essential and necessary in these regions which will provide scientific reference for many fields such as navigation, ocean engineering, and disaster prevent and reduction. A high-resolution cross-calibrated multi-platform wind product is used to analyze gales over the Maritime Silk Road. The yearly mean speed and space distribution of gale, and the frequencies and trends of gale and extreme wind speed are analyzed. The results show that relatively high pools of gale are mainly located in the waters of the Arabian Sea, the Somali Sea, Indo-China Peninsula sea area, and Bay of Bengal in the summer. The gale frequency of the Somali Sea is more than 90%. Overall, the gale days increase year by year in the majority of the South China Sea and the northern Indian Ocean, especially in the autumn and the winter.

Wind speed inversion and in-orbit assessment of the imaging altimeter on Tiangong-2 space station

Imaging altimeter(IALT)is a new type of radar altimeter system.In contrast to the conventional nadir-looking altimeters,such as HY-2 A altimeter,Jason-1/2,and TOPEX/Poseidon,IALT observes the earth surface at low incident angles(2.5°–8°),so its swath is much wider and its spatial resolution is much higher than the previous altimeters.This paper presents a wind speed inversion method for the recently launched IALT onboard Tiangong-2 space station.Since the current calibration results of IALT do not agree well with the well-known wind geophysical model function at low incidence angles,a neural network is used to retrieve the ocean surface wind speed in this study.The wind speed inversion accuracy is evaluated by comparing with the ECMWF reanalysis wind speed,buoy wind speed,and in-situ ship measurements.The results show that the retrieved wind speed bias is about–0.21 m/s,and the root-mean-square(RMS)error is about 1.85 m/s.The wind speed accuracy of IALT meets the performance requirement.

Fengyun-3D MERSI True Color Imagery Developed for Environmental Applications

Many techniques were developed for creating true color images from satellite solar reflective bands, and the so-derived images have been widely used for environmental monitoring. For the newly launched Fengyun-3 D(FY-3 D)satellite, the same capability is required for its Medium Resolution Spectrum Imager-II(MERSI-II). In processing the MERSI-II true color image, a more comprehensive processing technique is developed, including the atmospheric correction, nonlinear enhancement, and image splicing. The effect of atmospheric molecular scattering on the total reflectance is corrected by using a parameterized radiative transfer model. A nonlinear stretching of the solar band reflectance is applied for increasing the image contrast. The discontinuity in composing images from multiple orbits and different granules is eliminated through the distance weighted pixel blending(DWPB) method. Through these processing steps, the MERSI-II true color imagery can vividly detect many natural events such as sand and dust storms, snow, algal bloom, fire, and typhoon. Through a comprehensive analysis of the true color imagery, the specific natural disaster events and their magnitudes can be quantified much easily, compared to using the individual channel data.

Winter wheat mapping using a random forest classifier combined with multi-temporal and multi-sensor data

Wheat is a major staple food crop in China.Accurate and cost-effective wheat mapping is exceedingly critical for food production management,food security warnings,and food trade policy-making in China.To reduce confusion between wheat and non-wheat crops for accurate growth stage wheat mapping,we present a novel approach that combines a random forest(RF)classifier with multi-sensor and multi-temporal image data.This study aims to(1)determine whether an RF combined with multi-sensor and multi-temporal imagery can achieve accurate winter wheat mapping,(2)to find out whether the proposed approach can provide improved performance over the traditional classifiers,and(3)examine the feasibility of deriving reliable estimates of winter wheat-growing areas from medium-resolution remotely sensed data.Winter wheat mapping experiments were conducted in Boxing County.The experimental results suggest that the proposed method can achieve good performance,with an overall accuracy of 92.9%and a kappa coefficient(κ)of 0.858.The winter wheat acreage was estimated at 33,895.71 ha with a relative error of only 9.3%.The effectiveness and feasibility of the proposed approach has been evaluated through comparison with other image classification methods.We conclude that the proposed approach can provide accurate delineation of winter wheat areas.

Estimation of wind speeds inside Super Typhoon Nepartak from AMSR2 low-frequency brightness temperatures

Accurate estimations of typhoon-level winds are highly desired over the westem Pacific Ocean.A wind speed retrieval algorithm is used to retrieve the wind speeds within Super Typhoon Nepartak (2016)using 6.9- and 10.7-GHz brightness temperatures from the Japanese Advanced Microwave Scanning Radiometer 2 (AMSR2) sensor on board the Global Change Observation Mission-Water 1 (GCOM-Wl)satellite.The results show that the retrieved wind speeds clearly represent the intensification process of Super Typhoon Nepartak.A good agreement is found between the retrieved wind speeds and the Soil Moisture Active Passive wind speed product.The mean bias is 0.51 m/s,and the root-mean-square difference is 1.93 m/s between them.The retrieved maximum wind speeds are 59.6 m/s at 04:45 UTC on July 6 and 71.3 m/s at 16:58 UTC on July 6.The two results demonstrate good agreement with the results reported by the China Meteorological Administration and the Joint Typhoon Warning Center.In addition,Feng-Yun 2G (FY-2G) satellite infrared images,Feng-Yun 3C (FY-3C)microwave atmospheric sounder data,and AMSR2 brightness temperature images are also used to describe the development and structure of Super Typhoon Nepartak.

Building segmentation and outline extraction from UAV image-derived point clouds by a line growing algorithm

This paper presents an approach to process raw unmanned aircraft vehicle(UAV)image-derived point clouds for automatically detecting,segmenting and regularizing buildings of complex urban landscapes.For regularizing,we mean the extraction of the building footprints with precise position and details.In the first step,vegetation points were extracted using a support vector machine(SVM)classifier based on vegetation indexes calculated from color information,then the traditional hierarchical stripping classification method was applied to classify and segment individual buildings.In the second step,we first determined the building boundary points with a modified convex hull algorithm.Then,we further segmented these points such that each point was assigned to a fitting line using a line growing algorithm.Then,two mutually perpendicular directions of each individual building were determined through a W-k-means clustering algorithm which used the slop information and principal direction constraints.Eventually,the building edges were regularized to form the final building footprints.Qualitative and quantitative measures were used to evaluate the performance of the proposed approach by comparing the digitized results from ortho images.