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Volume 14
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10.3390/rs14051250
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Article

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

by
Dingding Duan
1,†,
Xiao Sun
1,†,
Shefang Liang
1,
Jing Sun
1,
Lingling Fan
1,
Hao Chen
1,2,
Lang Xia
1,3,
Fen Zhao
1,
Wanqing Yang
1 and
Peng Yang
1,*
1
Key Laboratory of Agricultural Remote Sensing (AGRIRS), Ministry of Agriculture and Rural Affairs, Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, China
2
Land Use Planning Group, Wageningen University and Research, 6700 HB Wageningen, The Netherlands
3
National Research Center of Intelligent Equipment for Agriculture, Beijing Academy of Agricultural and Forestry Sciences, Beijing 100097, China
*
Author to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Remote Sens. 2022, 14(5), 1250; https://doi.org/10.3390/rs14051250
Submission received: 17 January 2022 / Revised: 25 February 2022 / Accepted: 1 March 2022 / Published: 3 March 2022
(This article belongs to the Special Issue Near Real-Time (NRT) Agriculture Monitoring)
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Abstract

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.
Keywords: cultivated land quality; spatiotemporal patterns; evaluation system; remote sensing; obstacle factor cultivated land quality; spatiotemporal patterns; evaluation system; remote sensing; obstacle factor
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MDPI and ACS Style

Duan, D.; Sun, X.; Liang, S.; Sun, J.; Fan, L.; Chen, H.; Xia, L.; Zhao, F.; Yang, W.; Yang, P. Spatiotemporal Patterns of Cultivated Land Quality Integrated with Multi-Source Remote Sensing: A Case Study of Guangzhou, China. Remote Sens. 2022, 14, 1250. https://doi.org/10.3390/rs14051250

AMA Style

Duan D, Sun X, Liang S, Sun J, Fan L, Chen H, Xia L, Zhao F, Yang W, Yang P. Spatiotemporal Patterns of Cultivated Land Quality Integrated with Multi-Source Remote Sensing: A Case Study of Guangzhou, China. Remote Sensing. 2022; 14(5):1250. https://doi.org/10.3390/rs14051250

Chicago/Turabian Style

Duan, Dingding, Xiao Sun, Shefang Liang, Jing Sun, Lingling Fan, Hao Chen, Lang Xia, Fen Zhao, Wanqing Yang, and Peng Yang. 2022. "Spatiotemporal Patterns of Cultivated Land Quality Integrated with Multi-Source Remote Sensing: A Case Study of Guangzhou, China" Remote Sensing 14, no. 5: 1250. https://doi.org/10.3390/rs14051250

APA Style

Duan, D., Sun, X., Liang, S., Sun, J., Fan, L., Chen, H., Xia, L., Zhao, F., Yang, W., & Yang, P. (2022). Spatiotemporal Patterns of Cultivated Land Quality Integrated with Multi-Source Remote Sensing: A Case Study of Guangzhou, China. Remote Sensing, 14(5), 1250. https://doi.org/10.3390/rs14051250

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