Soil Salinity Inversion of Winter Wheat Areas Based on Satellite-Unmanned Aerial Vehicle-Ground Collaborative System in Coastal of the Yellow River Delta.

Soil salinization is an important factor affecting winter wheat growth in coastal areas. The rapid, accurate and efficient estimation of soil salt content is of great significance for agricultural production. The Kenli area in the Yellow River Delta was taken as the research area. Three machine learning inversion models, namely, BP neural network (BPNN), support vector machine (SVM) and random forest (RF) were constructed using ground-measured data and UAV images, and the optimal model is applied to UAV images to obtain the salinity inversion result, which is used as the true salt value of the Sentinel-2A image to establish BPNN, SVM and RF collaborative inversion models, and apply the optimal model to the study area. The results showed that the RF collaborative inversion model is optimal, R2 = 0.885. The inversion results are verified by using the measured soil salt data in the study area, which is significantly better than the directly satellite remote sensing inversion method. This study integrates the advantages of multi-scale data and proposes an effective "Satellite-UAV-Ground" collaborative inversion method for soil salinity, so as to obtain more accurate soil information, and provide more effective technical support for agricultural production.

[Extraction of land-cover and wetland area in Bohai Rim region based on Google Earth Engine.] / 基于Google Earth Engine的环渤海地区土地覆盖分类.

The land cover of Bohai Rim region has changed greatly due to urbanization and economic development. Monitoring the land cover with high accuracy and real time is the most important basis for relevant researches. Traditional single-machine processing mode is difficult to realize rapid monitoring for large-scale and long-time series. The emergence of remote sensing big data makes it possible to combine computing platform and massive data. The land cover maps of study area were interpreted based on Google Earth Engine (GEE) platform with decision tree (CART) method from 2000 to 2019. The land cover change was analyzed, and the interpretation results using different data sources were compared. The results showed that the GEE platform could realize the rapid land cover interpretation in a large area, which interpreted coastal wetlands and other cover types with high accuracy over 80% comparing the surveyed points. Compared with Landsat images, the Sentinel-2A images interpretation results had a great improvement in accuracy, which increased from 85% to 95%, and thus more detailed surface information could be reflected. In 2000, the area of wetland, build-up area, farmland, forest, and water in the study area were 1612.5, 5734.9, 32074.8, 11853 and 3504.3 km2, accounting for 2.9%, 10.5%, 58.6%, 21.6% and 6.4% respectively. By 2019, wetlands had been reduced by 775.1 km2, with a decline of 40.1%; built-up area increased by 5310.5 km2 with an increasing rate of 92.6%. The area of farmland, forestland and water area decreased 1841.6, 1823.5 and 870.3 km2, with a decreasing rate of 5.7%, 24.8% and 48.1%, respectively. The coastal urbanization process caused the occupation of built-up area to other land use types, which was the main driving force of land cover change in the study area.

Developing a New Spectral Index for Detecting Cadmium-Induced Stress in Rice on a Regional Scale.

In natural farmland ecosystems, cadmium (Cd) pollution in rice has attracted increasing attention because of its high toxicity, relative mobility, and high water solubility. This study aims to develop a spectral index for detecting Cd stress in rice on a regional scale. Three experimental sites are selected in Zhuzhou City, Hunan Province. The hyperspectral data, chlorophyll (Chl) content, leaf area index, average leaf angle, Cd concentration in soil, and Sentinel-2A images from 2017 and 2018 are collected. A new spectral index sensitive to Cd stress in rice is established based on the global sensitivity analysis of the radiative transfer model PROSPECT + SAIL (commonly called PROSAIL) model with the auxiliary of the field-measured data. The heavy metal Cd stress-sensitive spectral index (HCSI) is devised as an indicator of the degree of Cd stress in rice. Results indicate that (1) the HCSI developed based on Chl is a good indicator of rice damage caused by Cd stress, that is, low values of HCSI occur in rice subject to relatively high pollution; (2) compared with common spectral indices, such as red-edge position and red-edge Chl index, HCSI is more sensitive to Chl content with higher Pearson correlation coefficients with respect to Chl content, ranging from 0.85 to 0.95; (3) HCSI is successfully applied in Sentinel-2A images from the two different years of monitoring rice Cd stress on a regional scale. Cd stress levels in rice stabilized, and the largest area percentage of each pollution levels of Cd decreased in the following order: No pollution (i.e., 40%), low pollution (i.e., 35%), and high pollution (i.e., 25%). This study indicates that a combination of simulation data from the PROSAIL model and measured data appears to be a promising method for establishing a sensitivity spectral index to heavy metal stress, which can accurately detect regional Cd stress in crops.