Upscaling Remote Sensing Inversion Model of Wheat Field Cultivated Land Quality in the Huang-Huai-Hai Agricultural Region, China

Li, Yinshuai; Chang, Chunyan; Wang, Zhuoran; Qi, Guanghui; Dong, Chao; Zhao, Gengxing

doi:10.3390/rs13245095

Cited by 8 publications

(9 citation statements)

References 53 publications

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“…Numerous studies highlighted that the CLQ is closely related to image‐derived vegetation indices (Li et al, 2021; Xia et al, 2019a; Xia et al, 2022). Therefore, seven vegetation indices at the rice tassel stage, including the normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), soil adjusted vegetation index (SAVI), surface water capacity index (SWCI), modified SAVI (MSAVI), difference vegetation index (DVI), and ratio vegetation index (RVI), were selected as the CLQ spectral indicators in this study.…”

Section: Methodsmentioning

confidence: 99%

Optimal deployment of cultivated land quality monitoring points based on satellite image‐driven assessment and improved spatial simulated annealing

et al. 2023

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The deployment of scientific and reasonable cultivated land quality (CLQ) monitoring points can provide timely and accurate information on the current situation and changes in CLQ. The conventional method of selecting CLQ monitoring points are based on the CLQ of land use patches including different grades of large patches, which reduces the reliability of monitoring CLQ. Moreover, the conventional method mainly considers only CLQ, resulting in the inaccessibility of some monitoring points. There exist knowledge gaps in deploying reliable CLQ monitoring sites in the present. Thus, to improve the reliability of CLQ monitoring, this study presented a novel approach for deploying CLQ monitoring points. The pixel‐scale CLQ was firstly estimated using the genetic algorithm‐back propagation neural network (GA‐BPNN) model and LANDSAT image‐derived predictors. Then, the stratified sampling model and the improved spatial simulated annealing algorithm (ISSA), considering both slope and road accessibility were applied to deploy monitoring points. The results highlighted that (1) the pixel‐scale CLQ data were more reasonable than the patch‐scale CLQ data with different grades. (2) A total of 132 monitoring points using the stratified sampling model and ISSA were finally identified in the study area, which can effectively avoid the inaccessible places. Thus, the results based on the novel approach proposed in this study provide a scientific basis and technical support for obtaining the optimal CLQ monitoring points.

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Section: Methodsmentioning

confidence: 99%

Optimal deployment of cultivated land quality monitoring points based on satellite image‐driven assessment and improved spatial simulated annealing

et al. 2023

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“…The rapid and efficient evaluation of CLQ is currently considered a requirement for cultivated land resource management [45]. Several studies on CLQ evaluations based on NDVI have produced good results.…”

Section: Selection Of Clq Evaluation Methodsmentioning

confidence: 99%

Optimization of Sample Construction Based on NDVI for Cultivated Land Quality Prediction

Wang

et al. 2022

IJERPH

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The integrated use of remote sensing technology and machine learning models to evaluate cultivated land quality (CLQ) quickly and efficiently is vital for protecting these lands. The effectiveness of machine-learning methods can be profoundly influenced by training samples. However, in the existing research, samples have mainly been constructed by random point (RPO). Little attention has been devoted to the optimization of sample construction, which may affect the accuracy of evaluation results. In this study, we present two optimization methods for sample construction of random patch (RPA) and area sequence patch (ASP). Differing from RPO samples, it aims to include cultivated land area and its size into sample construction. Based on landsat-8 Operational Land Manager images and agricultural land grading data, the proposed sample construction methods were applied to the machine learning model to predict the CLQ in Dongtai City, Jiangsu Province, China. Four machine learning models (the backpropagation neural network, decision tree, random forest (RF), and support vector machine) were compared based on RPO samples to determine the accurate evaluation model. The best machine learning model was selected to compare RPA and ASP samples with RPO samples. Results determined that the RF model generated the highest accuracy. Meanwhile, a high correlation was noted between the cultivated land area and CLQ. Thus, incorporating cultivated land area in the sample construction attributes can improve the prediction accuracy of the model. Among the three sample construction methods, the ASP yielded the highest prediction accuracy, indicating that the use of a large, cultivated land patch as the sample unit can further elevate the model performance. This study provides a new sample construction method for predicting CLQ using a machine learning model, as well as providing a reference for related research.

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“…Cultivated land is the most important natural resource that plays a vital role in ensuring food security and the sustainable development of human society [1]. However, cultivated land is currently suffering from frequent natural disasters [2,3], soil nutrient loss [4,5], soil acidification, and heavy metal pollution [6,7] due to climate change and high-intensity utilization [8].…”

Section: Introductionmentioning

confidence: 99%

“…This study aimed to (1) construct a new CLQ evaluation system by integrating multisource remote sensing data to improve the efficiency of the spatiotemporal evaluation of the CLQ; (2) reveal the spatiotemporal patterns of CLQ in Guangzhou from 2010 to 2018;…”

Section: Introductionmentioning

confidence: 99%

“…(3) identify the main obstacle factors affecting the improvement of the CLQ in Guangzhou. The potential scientific contributions of this study include (1) exploring the potential of using multi-source remote sensing data in the comprehensive evaluation of the CLQ; (2) providing scientific guidance for improving CLQ in Guangzhou, increasing food production, and ensuring regional food security.…”

Section: Introductionmentioning

confidence: 99%

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Spatiotemporal Patterns of Cultivated Land Quality Integrated with Multi-Source Remote Sensing: A Case Study of Guangzhou, China

et al. 2022

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Scientifically revealing the spatiotemporal patterns of cultivated land quality (CLQ) is crucial for increasing food production and achieving United Nations Sustainable Development Goal (SDG) 2: Zero Hunger. Although studies on the evaluation of CLQ have been conducted, an effective evaluation system that is suitable for the macro-regional scale has not yet been developed. In this study, we first defined the CLQ from four aspects: soil fertility, natural conditions, construction level, and cultivated land productivity. Then, eight indicators were selected by integrating multi-source remote sensing data to create a new CLQ evaluation system. We assessed the spatiotemporal patterns of CLQ in Guangzhou, China, from 2010 to 2018. In addition, we identified the main factors affecting the improvement of CLQ. The results showed that the CLQ continuously improved in Guangzhou from 2010 to 2018. The area of high-quality cultivated land increased by 13.7%, which was mainly distributed in the traditional agricultural areas in the northern and eastern regions of Guangzhou. The areas of medium- and low-quality cultivated land decreased by 8.1% and 5.6%, respectively, which were scattered throughout the whole study area. The soil fertility and high productivity capacity were the main obstacle factors that affected the improvement of CLQ. Simultaneously, the obstacle degree of stable productivity capacity gradually increased during the study period. Therefore, the targeted improvement measures could be put forward by applying biofertilizers, strengthening crop management and constructing well-facilitated farmland. The new CLQ evaluation system we proposed is particularly practical at the macro-regional scale, and the results provided targeted guidance for decision makers to improve CLQ and promote food security.

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Upscaling Remote Sensing Inversion Model of Wheat Field Cultivated Land Quality in the Huang-Huai-Hai Agricultural Region, China

Cited by 8 publications

References 53 publications

Optimal deployment of cultivated land quality monitoring points based on satellite image‐driven assessment and improved spatial simulated annealing

Optimal deployment of cultivated land quality monitoring points based on satellite image‐driven assessment and improved spatial simulated annealing

Optimization of Sample Construction Based on NDVI for Cultivated Land Quality Prediction

Spatiotemporal Patterns of Cultivated Land Quality Integrated with Multi-Source Remote Sensing: A Case Study of Guangzhou, China

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