Evaluation Of Cultivated Land Quality Research Articles

Research on the Inversion Model of Cultivated Land Quality Using High-Resolution Remote Sensing Data

Cultivated land quality is an essential measure of cultivated land production capability. Establishing a cultivated land quality inversion model based on high-resolution remote sensing data provides a scientific basis for regional cultivated land resource management and sustainable utilization. Utilizing field survey data, cultivated land quality evaluation data, and high-resolution remote sensing data, a spectral index-cultivated land quality model was constructed and optimized with the machine learning method, and cultivated land quality inversion and verification in Chuzhou City in 2021 were carried out. The results showed that the distribution of cultivated land quality in the study area depicted with the remote sensing inversion model based on random forest was consistent with the actual cultivated land quality. Although the accuracy of the SVT-CLQ inversion model established using four spectral indices is slightly lower than that of the MSVT-CLQ group established using 15 indices, it can still accurately reflect the distribution of cultivated land quality in the study area. Compared with the two models of the MSVT-CLQ and SVT-CLQ groups, the field survey data of sampling points is reduced, the time and energy of field sampling and analysis are correspondingly saved, the efficiency of cultivated land quality evaluation is improved, and the dynamic monitoring and rapid evaluation of cultivated land quality are realized.

Research Progress on Cultivated Land Quality Grade Evaluation and Obstacle Factor Identification

The evaluation of cultivated land quality can reflect the different characteristics of cultivated land, which is of great significance to the sustainable development of agriculture. On the basis of literature research, this paper summarizes the development and characteristics of land quality evaluation at home and abroad, and the research status of cultivated land obstacle factor identification, and looks forward to the development direction of cultivated land quality evaluation and obstacle factor identification research.

System Cognition and Analytic Technology of Cultivated Land Quality from a Data Perspective

As cultivated land quality has been paid more and more scientific attention, its connotation generalization and cognitive bias are widespread, bringing many challenges to the investigation and evaluation of regional cultivated land quality and its data analysis and mining. Establishing a systematic and interdisciplinary cognitive approach to cultivated land quality is urgent and necessary. Therefore, we explored and developed a conceptual framework of the model for the cultivated land quality analysis from the data perspective, including cultivated land quality ontology, mapping, correlation, and decision models. We identified the primary content of cultivated land quality perceptions and four cognitive mechanisms. We built vital technologies, such as the collaborative perception of the quality of cultivated land, intelligent treatment, diagnostic evaluation, and simulation prediction. Applying this analysis framework, we sorted out the frequency of indicators that characterize the function of cultivated land according to the literature in recent years and have built the cognitive system of cultivated land quality in the black soil region of Northeast China. The system’s central component was production capacity and it had three components: a foundation, a guarantee, and an effect. The black soil region cultivated land quality evaluation system has seven purposes involving 20–31 key indicators: production supply, threat control, farmland infrastructure regulation, cultivated land ecological maintenance, economics, social culture, and environmental protection. In various application contexts, the system had many critical supporting technologies. The results demonstrate that the framework has strong adaptability, efficiency, and scalability, which might offer a theoretical direction for further studies on the evaluation of the quality of cultivated land in the area. The analysis framework established in this study is helpful to deepen the understanding of cultivated land quality systems from the perspective of big data. Taking the big data of cultivated land quality as the driving force, combined with the technical methods of cultivated land quality analysis, the evaluation results of cultivated land quality under different scenarios and different objectives are optimized. In addition, the framework can serve the practice of farmland management and engineering improvement, adapt to the management needs of different objects and different scales, and achieve the combination of theory and practice.

Evaluation of Cultivated Land Quality in Semiarid Sandy Areas: A Case Study of the Horqin Zuoyihou Banner

Objective: The evaluation of cultivated land quality from the regional perspective and the clear level and change in cultivated land quality in the whole region can better guide the construction of cultivated land quality, carry out scientific optimization allocation, improve grain production capacity, and promote the sustainable use of cultivated land. Research methods: The utilization of local resources and natural conditions were comprehensively considered, and the evaluation was carried out from the four dimensions of ecology, quantity, spatial structure and scale to comprehensively reflect the level of regional cultivated land utilization. Results: (1) The cultivated land quality level of the Horqin Zuoyihou Banner is low, and the pressure of regional water resources is great. Some cultivated land is located in the area with poor natural conditions, and there are many thin and narrow cultivated areas of land. The area of cultivated land that can be carried by regional water resources under current irrigation is 184,492.17 hm2, and that under water-saving irrigation is 259,703.72 hm2, which are lower than the current cultivated land areas. (2) During the study period, the total amount and spatial distribution of cultivated land changed greatly, and there were good natural conditions and utilization conditions of newly added cultivated land, but the quality of cultivated land from the regional perspective showed a downward trend. The dynamic attitude of cultivated land change was 0.99%. (3) According to the evaluation results, the cultivated land was divided into the following four categories: priority protection type, optimization and coordination type, gradual conversion type and priority conversion type, which accounted for 34.18%, 30.59%, 28.83% and 6.40% of the total cultivated land area, respectively. Conclusions: There is a sharp contradiction between the supply and demand of water resources, and the quality of cultivated land in the region is low and shows a downward trend, which is not conducive to the sustainable use of regional land resources. In this regard, the Horqin Zuoyihou Banner should actively curb the growth of cultivated land, develop water-saving irrigation processes, strengthen ecological environment protection, and systematically optimize the layout of cultivated land on the premise of ensuring ecological security according to the actual conditions of different regions.

Study on cultivated land quality evaluation from the perspective of farmland ecosystems

Historically, the cognition and management of cultivated land quality (CLQ) have been fragmented, leading to an inability to meet contemporary scenarios and requirements of China’s agricultural development and cultivated land protection. This study aimed to systematically recognize the concepts, elements, connotations, and dimensions of CLQ and build a new cultivated land quality evaluation (CQE) system. Initially, we deduced the concept of CLQ through the clear concept of “quality” in line with international standards. Next, we connected CLQ with the framework of farmland ecosystem service cascades and clarified the element structure of CLQ based on the structure of farmland ecosystems. Then, based on the multi-level human well-being that farmland ecosystems can provide, the connotations and dimensions of CLQ was determined. Using Wen County within Henan Province as a case study site, we constructed a CQE system for empirical analysis and comparative verification, showing that the new system was better than the existing one. This study helped to clarify the CLQ and provided a theoretical basis and technical reference for sustainable intensification (SI) and multi-directional protection and supervision of cultivated land.

Evaluation of cultivated land quality using attention mechanism-back propagation neural network.

Cultivated land quality is related to the quality and safety of agricultural products and to ecological safety. Therefore, reasonably evaluating the quality of land, which is helpful in identifying its benefits, is crucial. However, most studies have used traditional methods to estimate cultivated land quality, and there is little research on using deep learning for this purpose. Using Ya’an cultivated land as the research object, this study constructs an evaluation system for cultivated land quality based on seven aspects, including soil organic matter and soil texture. An attention mechanism (AM) is introduced into a back propagation (BP) neural network model. Therefore, an AM-BP neural network that is suitable for Ya’an cultivated land is designed. The sample is divided into training and test sets by a ratio of 7:3. We can output the evaluation results of cultivated land quality through experiments. Furthermore, they can be visualized through a pie chart. The experimental results indicate that the model effect of the AM-BP neural network is better than that of the BP neural network. That is, the mean square error is reduced by approximately 0.0019 and the determination coefficient is increased by approximately 0.005. In addition, this study obtains better results via the ensemble model. The quality of cultivated land in Yucheng District is generally good, i.e.,mostly third and fourth grades. It conforms to the normal distribution. Lastly, the method has certain to evaluate cultivated land quality, providing a reference for future cultivated land quality evaluation.

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 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.

How Does Land Consolidation Affect Soil Fungal Community Structure? Take Heavy Metal Contaminated Areas in Eastern China for Example

Farmland land consolidation can effectively improve the quality of farmland soil and the agricultural production level, and can effectively guarantee farmland ecology and food security, which has been widely used in the world. A large number of studies have shown that farmland consolidation has certain adjustments to the basic physical and chemical properties of soil and the content of heavy metals. As a key indicator of soil quality and ecological conditions, soil microorganisms play an important role in soil pollution restoration and the promotion of crop growth. However, there are few domestic and foreign studies on how farmland consolidation affects soil microbial properties, and there are no related reports on the mechanism of action between them, which is a blank in the field of agricultural land consolidation and soil microecology, especially in heavy metal contaminated areas. Therefore, we used the DNA sequence technology to compare fungal community structure in farmlands with and without consolidation in heavy metal contaminated areas. Our results showed that (1) farmland consolidation had a significant impact on soil microbial characteristics, which were mainly manifested as changes in microbial biomass, microbial diversity and community structure. (2) Farmland consolidation had an indirect impact on soil fungal community structure by adjusting the soil physical and chemical properties. (3) The impact of heavy metals on the fungal community structure varied significantly under different levels of heavy metal pollution in farmland consolidation areas. When the pollution was at the highest level, there existed 7 fungus genera showing a strong tolerance to heavy metals and consuming a lot of soil nutrients, of which were Melanospora, Pseudeurotium, Guehomyces, Schizothecium, Gibberella, Myrothecium, and Neurospora. In this study, an analytical method was proposed to analyze the effects of farmland consolidation on soil fungi, and the mechanism was discussed from two aspects—soil physical and chemical properties, and heavy metal content. The results shed some light on farmland consolidation, cultivated land quality evaluation and territorial space ecological restoration.

Spatial variation evaluation of cultivated land quality from the perspective of crop rotation for a typical lateritic red soil farming area in south China

Spatial variation evaluation of cultivated land quality from the perspective of crop rotation for a typical lateritic red soil farming area in south China

Research on filling methods of missing data in cultivated land quality evaluation

Research on filling methods of missing data in cultivated land quality evaluation

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

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.

Spatial characteristics of cultivated land quality accounting for ecological environmental condition: A case study in hilly area of northern Hubei province, China

Scientific evaluation of cultivated land quality is the premise for ensuring food security and improving land quality. In this research, new evaluation method was developed referring to the Soil Quality Index method, using Xiangzhou District, Hubei province, China, as the studying area. Five first-level indicators including physical and chemical properties, nutrient condition, management condition, health condition and ecological environmental condition of cultivated land were selected for construction of the index system. 28 second-level indicators including topographic position, organic matter, active potassium, effective phosphorus, effective sulfur, effective boron, cadmium concentration, soil runoff nitrogen, soil runoff phosphorus, etc., were selected. Interpolation methods were compared between Inverse Distance Weighted (IDW) method, Spline method and Ordinary Kriging (OK) method at different evaluation scales to reduce the effect of modifiable areal unit problem (MAUP). The IDW method at the small scale was finally performed in the evaluation process. The results showed that the average cultivated land quality index was 6.09 in Xiangzhou District. The main limiting factors of cultivated land quality included soil bulk density, organic matter, effective phosphorus, pH, effective boron, effective sulfur, soil runoff nitrogen and phosphorus. Therefore, land quality improving suggestions were put forward by adopting ecological agricultural engineering and applying suitable organic fertilizer.

Crop Growth Stage GPP-Driven Spectral Model for Evaluation of Cultivated Land Quality Using GA-BPNN

Rapid and accurate evaluation of cultivated land quality (CLQ) using remotely sensed images plays an important role for national food security and social stability. Current approaches for evaluating CLQ do not consider spectral response relationships between CLQ and spectral indicators based on crop growth stages. This study aimed to propose an accurate spectral model to evaluate CLQ based on late rice phenology. In order to increase the accuracy of evaluation, the Empirical Bayes Kriging (EBK) interpolation was first performed to scale down gross primary production (GPP) products from a 500 m spatial resolution to 30 m. As an indicator, the ability of MODIS-GPPs from critical growth stages (tillering, jointing, heading, and maturity stages) was then investigated by combining Pearson correlation analysis and variance inflation factor (VIF) to select the phases of CLQ evaluation. Finally, a linear Partial Least Squares Regression (PLSR) and two nonlinear models, including Support Vector Regression (SVR) and Genetic Algorithm-Based Back Propagation Neural Network (GA-BPNN), were driven to develop an accurate spectral model of evaluating CLQ based on MODIS-GPPs. The models were tested and compared in the Conghua and Zengcheng districts of Guangzhou City, Guangdong, China. The results showed that based on field measured GPP data, the validation accuracy of 30 m spatial resolution MODIS GPP products with a root mean square error (RMSE) of 7.43 and normalized RMSE (NRMSE) of 1.59% was higher than that of the 500 m MODIS GPP products, indicating that the downscaled 30 m MODIS GPP products by EBK were more appropriate than the 500 m products. Compared with PLSR (R2 = 0.38 and RMSE = 87.97) and SVR (R2 = 0.64 and RMSE = 64.38), the GA-BPNN model (R2 = 0.69 and RMSE = 60.12) was more accurate to evaluate CLQ, implying a non-linear relationship of CLQ with the GPP spectral indicator. This is the first study to improve the accuracy of estimating CLQ using the rice growth stage GPP-driven spectral model by GA-BPNN and can thus advance the literature in this field.

Comprehensive Evaluation of Cultivated Land Quality at County Scale: A Case Study of Shengzhou, Zhejiang Province, China.

The existing evaluation system of cultivated land quality mainly considers the natural quality and utilisation conditions, but without sufficient emphasis on ecological environment, which can’t meet the requirements of the trinity pattern protection policy. This study, using GIS spatial analysis and multifactor comprehensive evaluation method, constructed a comprehensive evaluation index system, and applied it in Shengzhou. The results show that: (1) under the comprehensive evaluation system, the quality of cultivated land was classified into five levels and revealed normal distribution with the third level cultivated land area as the peak, successive reduction to the two poles, and the overall quality was good relatively; (2) A close relationship was observed between the quality grade of cultivated land and the landform, the valley plain with highest cultivated land quality was the main grain-producing areas. However, the cultivated land in mountainous areas was poor relatively, and vulnerable to geological disasters; (3) The quality grade of cultivated land was related to economical activities directly. The high-quality cultivated land resources made these towns the main grain-producing areas with many agricultural industries distribution. This study provided a new assessment approach that can support cultivated land grading, quality improvement, and sustainable usage, as well as providing a reference for related research and application.

The GA-BPNN-Based Evaluation of Cultivated Land Quality in the PSR Framework Using Gaofen-1 Satellite Data.

Rapid and efficient assessment of cultivated land quality (CLQ) using remote sensing technology is of great significance for protecting cultivated land. However, it is difficult to obtain accurate CLQ estimates using the current satellite-driven approaches in the pressure-state-response (PSR) framework, owing to the limitations of linear models and CLQ spectral indices. In order to improve the estimation accuracy of CLQ, this study used four evaluation models (the traditional linear model; partial least squares regression, PLSR; back propagation neural network, BPNN; and BPNN with genetic algorithm optimization, GA-BPNN) to evaluate CLQ for determining the accurate evaluation model. In addition, the optimal satellite-derived indicator in the land state index was selected among five vegetation indices (the normalized vegetation index, NDVI; enhanced vegetation index, EVI; modified soil-adjusted vegetation index, MSAVI; perpendicular vegetation index, PVI; and soil-adjusted vegetation index, SAVI) to improve the prediction accuracy of CLQ. This study was conducted in Conghua District of Guangzhou, Guangdong Province, China, based on Gaofen-1 (GF-1) data. The prediction accuracies from the traditional linear model, PLSR, BPNN, and GA-BPNN were compared using observations. The results demonstrated that (1) compared with other models (the traditional linear model: R2 = 0.14 and RMSE = 91.53; PLSR: R2 = 0.33 and RMSE = 74.58; BPNN: R2 = 0.50 and RMSE = 61.75), the GA-BPNN model based on EVI in the land state index provided the most accurate estimates of CLQ, with the R2 of 0.59 and root mean square error (RMSE) of 56.87, indicating a nonlinear relationship between CLQ and the prediction indicator; and (2) the GA-BPNN-based evaluation approach of CLQ in the PSR framework was driven to map CLQ of the study area using the GF-1 data, leading to an RMSE of 61.44 at the regional scale, implying that the GA-BPNN-based evaluation approach has the potential to map CLQ over large areas. This study provides an important reference for the high-accuracy prediction of CLQ based on remote sensing technology.

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

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.

The Research for the Evaluation of Cultivated Land Quality Based on Deep Belief Networks

Traditional evaluation methods of cultivated land quality are mainly on the basis of empirical judgments in the process of weight calculation and membership determination. In this paper, taking Enshi city as an example, we attempt to employ Deep Belief Networks (DBN) to estimate the cultivated land quality. We selected 8 evaluation indexes as the input data and the grade of cultivated land as the output data to construct an evaluation model and finally realized the classification of cultivated land quality grade. Compared with the results of the Supplement and Improvement of Farmland's Classification in Enshi city 2013 in quantity and spatial distribution, our experimental results show that accuracy of grade 11, grade 12 and grade 13 in quantity reach as high as 99.06%, 90.04% and 96.07%, respectively. Besides, the spatial distribution is substantially in agreement with real distribution.

The Research for the Evaluation of Cultivated Land Quality Based on Deep Belief Networks

The Research for the Evaluation of Cultivated Land Quality Based on Deep Belief Networks

Variation characteristics of farmland soil pH in the past 30 years of Enshi Autonomous Prefecture, Hubei, China

In order to explore temporal-spatial variability of farmland soil pH at Enshi Antonomous Prefecture, Hubei, China, soil pH during the past three decades was analyzed, using the datasets of the Second National Soil Survey (1980-1983) and the Cultivated Land Quality Evaluation (2010-2013). The natural and human factors inducing the change of soil pH were evaluated to provide theoretical guidance for further soil acidification management. Results showed that acidic soil (i.e., pH＜6.5) and neutral and alkaline soil (i.e., pH 6.5-8.5) were accounted for 98.4% and 1.6% in the farmland during the period of 2010-2013, respectively. The ratio increased 61.4% for the acidic soil but decreased 61.2% for the neutral and alkaline soil as compared with the period of 1980-1983. In addition, there was no alkaline soil (pH>8.5) in the region in 2010-2013. According to the dataset of the Second National Soil Survey (1980-1983), acidic soil was mainly distributed at Laifeng, Lichuan, Xuanen and Xianfeng counties, with the area ratio of 74.4%, 63.5%, 61.3% and 60.7%, respectively. For the period of 2010-2013, the ratio of acidic soil enhanced widely which was above 96% for each county. At Enshi Autonomous Prefecture, farmland soil showed an obvious acidification trend during the past three decades, with spatial variation of higher in the eastern part and lower in the western part of the region. Furthermore, soil pH decline occurred among different land use types in different areas. Overall, farmland soil pH declined 0.90 on average, with 1.14 decrease for upland and 0.87 for paddy soil, respectively. Clearly, upland soil acidification was severe than paddy soil. Factors related to soil acidification in the Enshi Autonomous Prefecture were mainly human factors such as unreasonable fertilizer combination, fertilizer ratio change, and more base cations taking away by high crop yield.