Search

To evaluate the state of an agricultural development more comprehensively, a vulnerability assessment is introduced into agricultural water and land resources system, and it is expected that the vulnerability assessment can provide a basis for improving system structure and function and realizing sustainable development. In the study, 27 evaluation indicators are selected from the agricultural water and land resources system (AWLRS), socio-economic system and ecological structure system to construct the evaluation index system for agricultural water and land resource system vulnerability (AWLRSV). Seagull optimization algorithm (SOA) is used to calibrate the parameters of the random forest (RF) model. SOA-RF model is applied to measure the AWLRSV of Heilongjiang Province in China. The results show that the SOA-RF model has higher accuracy and stronger stability than the traditional RF model and DA-RF model. The value of AWLRSV in Heilongjiang Province presents a downward–upward–downward trend from 2008 to 2018. The vulnerability levels are mainly level II and III, and level III is mainly distributed northwest and southeast of Heilongjiang Province. The novelty of this paper is to regard the agricultural water and land resources system as a compound system, put forward the vulnerability assessment framework. The findings may provide reference for regional sustainable development from a new research perspective. [ABSTRACT FROM AUTHOR]

Early or in-season crop type mapping using remote sensing data is important for crop managements to maximize crop yield. Existing remote sensing-based approaches in the literature rely mainly on the timing and temporal frequency of satellite data acquisition during a growing season to obtain optimal identification feature (OIF) to complete crop mapping. At present, the optimal features for mapping crop types are commonly generated by using different feature selection methods, and thereby are not generic in nature. In this paper, a novel approach based on the OIF knowledge graph is proposed for identification of crop types within the crop growing season. The concept of OIF was proposed based on the one-class classification and optimization theory, which relates a specific crop type to its OIF. The OIF knowledge graph can be created to describe the seasonal trend of OIF in a season. Eventually, the OIF knowledge graphs can be incorporated into the moment-preserving segmentation method to automatically identify crop types within the growing season. To evaluate the performance of the new method, crop mapping was conducted using 2019 Sentinel-2 data in Heilongjiang province, China, and Illinois, USA, respectively. Results demonstrate that integrating the OIF knowledge graph and moment-preserving thresholding method is effective in identifying crop types without ground-truth data. The overall accuracy higher than 90 % (Kappa coefficient greater than 0.90), and the producer's accuracies for corn, soybean, and rice are generally greater than 93 % in Heilongjiang province. The proposed approach has the advantage of simplifying crop identification process while maintaining a satisfactory identification accuracy in shorter time than conventional methods. [ABSTRACT FROM AUTHOR]