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

2021 ◽  
Vol 13 (24) ◽  
pp. 5095
Author(s):  
Yinshuai Li ◽  
Chunyan Chang ◽  
Zhuoran Wang ◽  
Guanghui Qi ◽  
Chao Dong ◽  
...  
Keyword(s):  
Area Ratio ◽  
Cultivated Land ◽  
Land Quality ◽  
Wheat Field ◽  
Evaluation Systems ◽  
Inversion Model ◽  
Plain Area ◽  
Ratio Difference ◽  
Field Quality ◽  
County Scale

It is an objective demand for sustainable agricultural development to realize fast and accurate cultivated land quality assessment. In this paper, Tengzhou city (county-scale hilly area: scale A), Shanghe county (county-scale plain area: scale B), and Huang-Huai-Hai region (including large-scale hilly and plain area: scale C and D) were taken as research areas. Through the conversion of evaluation systems, the inversion models at the county-scale were constructed. Then, the image scale conversion was carried out based on the numerical regression method, and the upscaling inversion was realized. The results showed that: (1) the conversion models of evaluation systems (CMES) are Y = 1.021x − 4.989 (CMESA−B), Y = 0.801x + 16.925 (CMESA−C), and Y = 0.959x + 3.458 (CMESC−D); (2) the booting stage is the best inversion phase; (3) the back propagation neural network model based on the combination index group (CI-BPNN) is the best inversion model, with the R2 are 0.723 (modeling set) and 0.722 (verification set). CI-BPNN and CI-BPNN-CMESA−B models are suitable for the hilly and plain areas at the county-scale, and the level area ratio difference is less than 4.87%. Furthermore, (4) the reflectance conversion model of short-wave infrared 2 is cubic, and the rest are quadratic. CI-BPNN-CMESA−C and CI-BPNN-CMESA−C-CMESC−D models realized upscaling inversion in the hilly and plain areas, with the maximum level area ratio difference being 1.60%. Additionally, (5) the wheat field quality has improved steadily since 2001 in the Huang-Huai-Hai region. This study proposes an upscaling inversion method of wheat field quality, which provides a scientific basis for cultivated land management and agricultural production in large areas.

scholarly journals Discussion on the Unified Survey and Evaluation of Cultivated Land Quality at County Scale for China’s 3rd National Land Survey: A Case Study of Wen County, Henan Province

2021 ◽  
Vol 13 (5) ◽  
pp. 2513
Author(s):  
Rui Zhao ◽  
Kening Wu ◽  
Xiaoliang Li ◽  
Nan Gao ◽  
Mingming Yu
Keyword(s):  
Evaluation System ◽  
Henan Province ◽  
Evaluation Methodology ◽  
Cultivated Land ◽  
Land Quality ◽  
Land Protection ◽  
Land Survey ◽  
Survey And Evaluation ◽  
Cultivated Land Protection ◽  
County Scale

Under the task requirements of China’s 3rd national land survey on the grade determination of cultivated land, research on a reasonable and unified survey and evaluation system of cultivated land quality (CLQ) is of great importance. From the three dimensions of agricultural climate, production potential, and health status components of cultivated land, six factors are selected in this study to construct a set of simple, practicable, and operable county-level CLQ survey and evaluation system. These factors are climate conditions of cultivated land, soil characteristics, tillage conditions, the attributes of cultivated land type, environmental conditions, and biological activity. The developed survey and evaluation system meets the demands of national engineering for the inclusion and coordination of multiple departments based on the current evaluation system and evaluation methodology of all relevant land administrative departments. Wen County, Henan Province is used for field verification and evaluation. Results demonstrate that the average quality index of cultivated land in Wen County is 2196.33, ranging from 660.70 to 2802.96 with the largest area of the first-class and second-class land accounting for 20.98% and 52.61% of the county’s cultivated land, respectively; the third-class and fourth-class land, 12.63% and 13.78%. The obvious regional differentiation characteristics are presented along the boundary of Qingfengling with the quality of northern cultivated land higher than that in the south. The comparison with the results of the national utilization gradation in 2018 infers that they are in accordance with the distribution of CLQ, which bears a significant positive correlation trend with the measured grain output of the field. The constructed evaluation system serves as a rewarding attempt of a unified survey and evaluation of CLQ at the county scale to reflect the production capacity of local crops, realize the sharing of data platforms, save manpower and capital investment, improve the practical connection of supervision and management of cultivated land protection in different departments, and meet the requirements of current cultivated land protection and management.

Research on the Strategy for Improving Cultivated Land Quality in China

2018 ◽  
Vol 20 (5) ◽  
pp. 16 ◽  
Author(s):  
Hongqi Zhang ◽  
Minghong Tan ◽  
Xiangbin Kong ◽  
Yongmei Xu ◽  
Erqi Xu ◽  
...  
Keyword(s):  
Cultivated Land ◽  
Land Quality

Study on evaluation of regional cultivated land quality based on resource-asset-capital attributes and its spatial mechanism

2020 ◽  
Vol 125 ◽  
pp. 102284 ◽  
Author(s):  
Yunyang Shi ◽  
Wenkai Duan ◽  
Luuk Fleskens ◽  
Mu Li ◽  
Jinmin Hao
Keyword(s):  
Cultivated Land ◽  
Land Quality ◽  
Spatial Mechanism

scholarly journals The Optimal Image Date Selection for Evaluating Cultivated Land Quality Based on Gaofen-1 Images

Sensors ◽  
2019 ◽  
Vol 19 (22) ◽  
pp. 4937 ◽  
Author(s):  
Ziqing Xia ◽  
Yiping Peng ◽  
Shanshan Liu ◽  
Zhenhua Liu ◽  
Guangxing Wang ◽  
...  
Keyword(s):  
Regression Models ◽  
Vegetation Index ◽  
Southern China ◽  
Regional Scale ◽  
Growth Stages ◽  
Maturity Stage ◽  
Cultivated Land ◽  
Flowering Stage ◽  
Area Index ◽  
Land Quality

This study proposes a method for determining the optimal image date to improve the evaluation of cultivated land quality (CLQ). Five vegetation indices: leaf area index (LAI), difference vegetation index (DVI), enhanced vegetation index (EVI), normalized difference vegetation index (NDVI), and ratio vegetation index (RVI) are first retrieved using the PROSAIL model and Gaofen-1 (GF-1) images. The indices are then introduced into four regression models at different growth stages for assessing CLQ. The optimal image date of CLQ evaluation is finally determined according to the root mean square error (RMSE). This method is tested and validated in a rice growth area of Southern China based on 115 sample plots and five GF-1 images acquired at the tillering, jointing, booting, heading to flowering, and milk ripe and maturity stage of rice in 2015, respectively. The results show that the RMSEs between the measured and estimated CLQ from four vegetation index-based regression models at the heading to flowering stage are smaller than those at the other growth stages, indicating that the image date corresponding with the heading to flowering stage is optimal for CLQ evaluation. Compared with other vegetation index-based models, the LAI-based logarithm model provides the most accurate estimates of CLQ. The optimal model is also driven using the GF-1 image at the heading to flowering stage to map CLQ of the study area, leading to a relative RMSE of 14.09% at the regional scale. This further implies that the heading to flowering stage is the optimal image time for evaluating CLQ. This study is the first effort to provide an applicable method of selecting the optimal image date to improve the estimation of CLQ and thus advanced the literature in this field.

A new grading system for evaluating China's cultivated land quality

2020 ◽  
Vol 31 (12) ◽  
pp. 1482-1501
Author(s):  
Liming Liu ◽  
De Zhou ◽  
Xiao Chang ◽  
Zhulu Lin
Keyword(s):  
Grading System ◽  
Cultivated Land ◽  
Land Quality
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