Spatiotemporal Patterns of Cultivated Land Quality Integrated with Multi-Source Remote Sensing: A Case Study of Guangzhou, China
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 syste...
| Main Authors: | , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-03-01
|
| Series: | Remote Sensing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-4292/14/5/1250 |
| _version_ | 1797473882500759552 |
|---|---|
| author | Dingding Duan Xiao Sun Shefang Liang Jing Sun Lingling Fan Hao Chen Lang Xia Fen Zhao Wanqing Yang Peng Yang |
| author_facet | Dingding Duan Xiao Sun Shefang Liang Jing Sun Lingling Fan Hao Chen Lang Xia Fen Zhao Wanqing Yang Peng Yang |
| author_sort | Dingding Duan |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-09T20:22:45Z |
| format | Article |
| id | doaj.art-ddf9f15ca8024531b7f4092bb8a5cb3b |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-09T20:22:45Z |
| publishDate | 2022-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-ddf9f15ca8024531b7f4092bb8a5cb3b2023-11-23T23:43:48ZengMDPI AGRemote Sensing2072-42922022-03-01145125010.3390/rs14051250Spatiotemporal Patterns of Cultivated Land Quality Integrated with Multi-Source Remote Sensing: A Case Study of Guangzhou, ChinaDingding Duan0Xiao Sun1Shefang Liang2Jing Sun3Lingling Fan4Hao Chen5Lang Xia6Fen Zhao7Wanqing Yang8Peng Yang9Key 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, ChinaKey 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, ChinaKey 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, ChinaKey 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, ChinaKey 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, ChinaKey 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, ChinaKey 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, ChinaKey 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, ChinaKey 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, ChinaKey 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, ChinaScientifically 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.https://www.mdpi.com/2072-4292/14/5/1250cultivated land qualityspatiotemporal patternsevaluation systemremote sensingobstacle factor |
| spellingShingle | Dingding Duan Xiao Sun Shefang Liang Jing Sun Lingling Fan Hao Chen Lang Xia Fen Zhao Wanqing Yang Peng Yang Spatiotemporal Patterns of Cultivated Land Quality Integrated with Multi-Source Remote Sensing: A Case Study of Guangzhou, China Remote Sensing cultivated land quality spatiotemporal patterns evaluation system remote sensing obstacle factor |
| title | Spatiotemporal Patterns of Cultivated Land Quality Integrated with Multi-Source Remote Sensing: A Case Study of Guangzhou, China |
| title_full | Spatiotemporal Patterns of Cultivated Land Quality Integrated with Multi-Source Remote Sensing: A Case Study of Guangzhou, China |
| title_fullStr | Spatiotemporal Patterns of Cultivated Land Quality Integrated with Multi-Source Remote Sensing: A Case Study of Guangzhou, China |
| title_full_unstemmed | Spatiotemporal Patterns of Cultivated Land Quality Integrated with Multi-Source Remote Sensing: A Case Study of Guangzhou, China |
| title_short | Spatiotemporal Patterns of Cultivated Land Quality Integrated with Multi-Source Remote Sensing: A Case Study of Guangzhou, China |
| title_sort | spatiotemporal patterns of cultivated land quality integrated with multi source remote sensing a case study of guangzhou china |
| topic | cultivated land quality spatiotemporal patterns evaluation system remote sensing obstacle factor |
| url | https://www.mdpi.com/2072-4292/14/5/1250 |
| work_keys_str_mv | AT dingdingduan spatiotemporalpatternsofcultivatedlandqualityintegratedwithmultisourceremotesensingacasestudyofguangzhouchina AT xiaosun spatiotemporalpatternsofcultivatedlandqualityintegratedwithmultisourceremotesensingacasestudyofguangzhouchina AT shefangliang spatiotemporalpatternsofcultivatedlandqualityintegratedwithmultisourceremotesensingacasestudyofguangzhouchina AT jingsun spatiotemporalpatternsofcultivatedlandqualityintegratedwithmultisourceremotesensingacasestudyofguangzhouchina AT linglingfan spatiotemporalpatternsofcultivatedlandqualityintegratedwithmultisourceremotesensingacasestudyofguangzhouchina AT haochen spatiotemporalpatternsofcultivatedlandqualityintegratedwithmultisourceremotesensingacasestudyofguangzhouchina AT langxia spatiotemporalpatternsofcultivatedlandqualityintegratedwithmultisourceremotesensingacasestudyofguangzhouchina AT fenzhao spatiotemporalpatternsofcultivatedlandqualityintegratedwithmultisourceremotesensingacasestudyofguangzhouchina AT wanqingyang spatiotemporalpatternsofcultivatedlandqualityintegratedwithmultisourceremotesensingacasestudyofguangzhouchina AT pengyang spatiotemporalpatternsofcultivatedlandqualityintegratedwithmultisourceremotesensingacasestudyofguangzhouchina |