In recent years, remote sensing (RS) research on crop growth status monitoring has gradually turned from static spectrum information retrieval in large-scale to meso-scale or micro-scale, timely multi-source data cooperative analysis; this change has presented higher requirements for RS data acquisition and analysis efficiency. How to implement rapid and stable massive RS data extraction and analysis becomes a serious problem. This paper reports on a Raster Dataset Clean & Reconstitution Multi-Grid (RDCRMG) architecture for remote sensing monitoring of vegetation dryness in which different types of raster datasets have been partitioned, organized and systematically applied. First, raster images have been subdivided into several independent blocks and distributed for storage in different data nodes by using the multi-grid as a consistent partition unit. Second, the “no metadata model” ideology has been referenced so that targets raster data can be speedily extracted by directly calculating the data storage path without retrieving metadata records; third, grids that cover the query range can be easily assessed. This assessment allows the query task to be easily split into several sub-tasks and executed in parallel by grouping these grids. Our RDCRMG-based change detection of the spectral reflectance information test and the data extraction efficiency comparative test shows that the RDCRMG is reliable for vegetation dryness monitoring with a slight reflectance information distortion and consistent percentage histograms. Furthermore, the RDCGMG-based data extraction in parallel circumstances has the advantages of high efficiency and excellent stability compared to that of the RDCGMG-based data extraction in serial circumstances and traditional data extraction. At last, an RDCRMG-based vegetation dryness monitoring platform (VDMP) has been constructed to apply RS data inversion in vegetation dryness monitoring. Through actual applications, the RDCRMG architecture is proven to be appropriate for timely vegetation dryness RS automatic monitoring with better performance, more reliability and higher extensibility. Our future works will focus on integrating more kinds of continuously updated RS data into the RDCRMG-based VDMP and integrating more multi-source datasets based collaborative analysis models for agricultural monitoring.

Rapid urbanization has caused the reduction of green spaces in most cities, disrupting the structure and process of urban and rural ecosystems. The accurate identification of spatiotemporal changes in green spaces is important to delineate future management and planning. We investigated green space types of the Beijing–Tianjin–Hebei region in 1995, 2000, 2005, 2010, and 2015 based on the elevation data and land use/cover for those years. Spatiotemporal changes in these identified green spaces between 1995 and 2015 were evaluated as well as the spatial hotspots of disappeared and unstable green patches. The results indicate that the cultivated land in plains and forests and cultivated land in medium-high mountainous areas were the main green space types in the Beijing–Tianjin–Hebei region during the period from 1995 to 2015. A large number of green spaces, in particular cultivated lands, in the peripheral areas of big cities were replaced by construction sites over the past 20 years. Hotspots of unstable green spaces were mainly distributed in the western and northern mountainous areas of the Beijing–Tianjin–Hebei region, where green spaces changed from one type to another. These findings provide an important reference for the management and planning of land and green spaces towards an integrative and collaborative development of the Beijing-Tianjin-Hebei region.

As cultivated land quality has been paid more and more scientific attention, its connotation generalization and cognitive bias are widespread, bringing many challenges to the investigation and evaluation of regional cultivated land quality and its data analysis and mining. Establishing a systematic and interdisciplinary cognitive approach to cultivated land quality is urgent and necessary. Therefore, we explored and developed a conceptual framework of the model for the cultivated land quality analysis from the data perspective, including cultivated land quality ontology, mapping, correlation, and decision models. We identified the primary content of cultivated land quality perceptions and four cognitive mechanisms. We built vital technologies, such as the collaborative perception of the quality of cultivated land, intelligent treatment, diagnostic evaluation, and simulation prediction. Applying this analysis framework, we sorted out the frequency of indicators that characterize the function of cultivated land according to the literature in recent years and have built the cognitive system of cultivated land quality in the black soil region of Northeast China. The system’s central component was production capacity and it had three components: a foundation, a guarantee, and an effect. The black soil region cultivated land quality evaluation system has seven purposes involving 20–31 key indicators: production supply, threat control, farmland infrastructure regulation, cultivated land ecological maintenance, economics, social culture, and environmental protection. In various application contexts, the system had many critical supporting technologies. The results demonstrate that the framework has strong adaptability, efficiency, and scalability, which might offer a theoretical direction for further studies on the evaluation of the quality of cultivated land in the area. The analysis framework established in this study is helpful to deepen the understanding of cultivated land quality systems from the perspective of big data. Taking the big data of cultivated land quality as the driving force, combined with the technical methods of cultivated land quality analysis, the evaluation results of cultivated land quality under different scenarios and different objectives are optimized. In addition, the framework can serve the practice of farmland management and engineering improvement, adapt to the management needs of different objects and different scales, and achieve the combination of theory and practice.

Soils in black soil areas are high in organic matter and rich in nutrients. Soil microorganisms are particularly critical to cultivated land. The objective of this study was to explore the influencing factors of soil bacterial diversity under special regional conditions in a black soil region. In this study, the cultivated land in a black soil area was used as the study area and a random forest was used to map the bacterial abundance in the black soil area based on 1810 sample points. DbMEM analysis was used to quantify the spatial effect of the black soil area and to identify the influencing factors of soil bacterial abundance in the black soil area in combination with soil properties, terrain, and climate. Results of a variation division showed that broad (8.336%), AT (accumulated temperature, 5.520%), and pH (4.184%) were the main factors affecting soil bacterial diversity. The broad effect was more significant in the spatial effect, which may be related to the local landscape configuration. Overall, our research showed that the influencing factors of soil bacteria will be affected by regional characteristics.