Estimation of Cultivated Land Quality Based on Soil Hyperspectral Data

Efficient monitoring of cultivated land quality (CLQ) plays a significant role in cultivated land protection. Soil spectral data can reflect the state of cultivated land. However, most studies have used crop spectral information to estimate CLQ, and there is little research on using soil spectral data for this purpose. In this study, soil hyperspectral data were utilized for the first time to evaluate CLQ. We obtained the optimal spectral variables from dry soil spectral data using a gradient boosting decision tree (GBDT) algorithm combined with the variance inflation factor (VIF). Two estimation algorithms (partial least-squares regression (PLSR) and back-propagation neural network (BPNN)) with 10-fold cross-validation were employed to develop the relationship model between the optimal spectral variables and CLQ. The optimal algorithms were determined by the degree of fit (determination coefficient, R2). In order to estimate CLQ at the regional scale, HuanJing-1A Hyperspectral Imager (HJ-1A HSI) data were transformed into dry soil spectral data using the linkage model of original soil spectral reflectance to dry soil spectral reflectance. This study was conducted in the Guangdong Province, China and the Conghua district within the same province. The results showed the following: (1) the optimal spectral variables selected from the dry soil spectral variables were 478 nm, 502 nm, 614 nm, 872 nm, 966 nm, 1007 nm, and 1796 nm. (2) The BPNN was the optimal model, with an R2(C) of 0.71 and a normalized root mean square error (NRMSE) of 12.20%. (3) The results showed the R2 of the regional-scale CLQ estimation based on the proposed method was 0.05 higher, and the NRMSE was 0.92% lower than that of the CLQ map obtained using the traditional method. Additionally, the NRMSE of the regional-scale CLQ estimation base on dry soil spectral variables from HJ-1A HSI data was 2.00% lower than that of the model base on the original HJ-1A HSI data.

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

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.

An Automatic Extraction Method for Hatched Residential Areas in Raster Maps Based on Multi-Scale Feature Fusion

Extracting the residential areas from digital raster maps is beneficial for research on land use change analysis and land quality assessment. In traditional methods for extracting residential areas in raster maps, parameters must be set manually; these methods also suffer from low extraction accuracy and inefficiency. Therefore, we have proposed an automatic method for extracting the hatched residential areas from raster maps based on a multi-scale U-Net and fully connected conditional random fields. The experimental results showed that the model that was based on a multi-scale U-Net with fully connected conditional random fields achieved scores of 97.05% in Dice, 94.26% in Intersection over Union, 94.92% in recall, 93.52% in precision and 99.52% in accuracy. Compared to the FCN-8s, the five metrics increased by 1.47%, 2.72%, 1.07%, 4.56% and 0.26%, respectively and compared to the U-Net, they increased by 0.84%, 1.56%, 3.00%, 0.65% and 0.13%, respectively. Our method also outperformed the Gabor filter-based algorithm in the number of identified objects and the accuracy of object contour locations. Furthermore, we were able to extract all of the hatched residential areas from a sheet of raster map. These results demonstrate that our method has high accuracy in object recognition and contour position, thereby providing a new method with strong potential for the extraction of hatched residential areas.

Visualizing clustering characteristics of multidimensional arable land quality indexes at the county level in mainland China

The evaluation of the arable land ecosystem services capacity and arable land use intensity is important for recognizing regional key factors that impact the change of arable land attributes. A chronic lack of cooperation persists between these two fields of study, which makes providing sufficient information to support developing arable land use management and control policies difficult. In this study, the clustering characteristics of four arable land quality indexes have been assessed using the K-means algorithm to indicate the regional coordination between arable land resource protection and arable land use. The clustering results have been visualized using circular cartogram. This study can contribute to the identification of key regional challenges in China's arable land use and help to build the framework of other countries’ arable land protection policies.

Impacts of Farming Layer Constructions on Cultivated Land Quality under the Cultivated Land Balance Policy

Cultivated Land Balance Policy (CLBP) has led to the “better land occupied and worse land supplemented” program. At the same time, the current field-scale cultivated land quality (CLQ) evaluation cannot meet the work requirements of the CLBP. To this end, this study selected 24 newly added farmland in Fuping County and performed eight different high quality farming layer construction experiments to improve the CLQ. A new comprehensive model was constructed on a field scale to evaluate the CLQ using different tests from multi-dimensional perspectives of soil fertility, engineering, environment, and ecology, and to determine the best test mode. The results showed that after the test, around 62% of the cultivated land improved by one level, and the average cultivated land quality level and quality index of the test area increased by 0.63 and 30.63, respectively. The treatment of “woody peat + rotten crop straw + biostimulation regulator II + conventional fertilization” had the best effect on the improvement of organic matter, soil aggregates, and soil microbial activity, and was the best treatment method. In general, application of soil amendments, such as woody peat when constructing high quality farmland, could quickly improve CLQ, and field-scale CLQ evaluation model constructed from a multi-dimensional perspective could accurately assess the true quality of farmland and allow managers to improve and manage arable land resources under CLBP.

Pengaruh Kemiringan Lereng terhadap Kerusakan Tanah di Giritontro, Wonogiri

The Effect of Slope Gradient of land on the Soil Damage in Giritontro, Wonogiri. Soil degradation is one of the problems in agriculture that affects the level of quality and carrying capacity of the soil for certain land uses. This study aims to analyse the status of soil degradation, the effect of slope, the determinant factor, and recommendation for land management.It was conducted in Giritontro District, Wonogiri Regencywith survey methods by field verification, taking soil samples and laboratoryanalysis based on Regulation of The Government of Indonesia Number 150 of 2000. Analysis unit is land map unit (LMU) which obtained from an overlay of mapssoil types, slope, rainfall, and land use. There were 12 LMU, and repeated 3 site samplings in each it. The result shows that the status of soil degradation was classified as slightly degraded (R.I) in all area research of 5.016.37 ha. The slope has a very significant effect on soil degradation. Slope 26-40% has significant highest score of soil degradration. Determinant factors were soil bulk density and porosity, therefore giving organic matter and optimizing tillage were recommended to improve land quality.

Physical Potentials and Constraints of Tidal Peat Swamps for Agriculture (Case Study of Rasau Jaya District, West Kalimantan Province, Indonesia)

Tidal swamps which are widespread in lowland areas have the potential to be used for agricultural activities. The amount of rain potential available in this type of land can be used to supply water for irrigation purposes so that plants grow optimally. However, the tidal potential especially on the peat swamps area may have a negative impact, namely the occurrence of nutrient leaching from the soil, which is harmful to plants. Rasau Jaya, a tidal lowland area with peat swamp soil characteristic, is an area allocated for rice and corn cultivation. The study was conducted with the aim to determine the physical potential and constraints of land and water management in Rasau Jaya for the cultivation of both types of plants. This research was conducted through field observations and measurements as well as laboratory tests and model scale to evaluate the characteristics of existing land quality based on Land Suitability Classification by the Food and Agriculture Organization (FAO). The result of this study shows that characteristics of water availability in Rasau Jaya is classified as Class S1 Highly Suitable for rice and corn crops, while existing conditions of land in Rasau Jaya III are generally classified in the S2 class Moderately Suitable for rice and corn crops. Appropriate land management is needed to increase land productivity for the cultivation of the Rasau Jaya’s assigned priority crops.

Soils in the Bulia micro watershed of Gorontalo province, Indonesia, and their quality assessment

Ten representative pedons from the Bulia micro watershed of Gorontalo Province, Indonesia, were characterized and classified to determine its land quality (LQ) class. Angular blocky, sticky, plastic consistencies and a hard consistency prevailed in the soil structure. In the alluvial plains the soil texture is dominated by the clay fraction, while in the hills and volcanic mountains the sand fraction is dominated. The soils in the Bulia micro watershed also have acid to neutral reaction, with the range of very low to high OC (organic carbon) levels, the reserve of exchangeable bases was dominated by Ca2+ in two series patterns, namely: Ca2+ > Mg+ > Na+ > K+ and Ca2+ > Na+ > Mg+ > K+, cation exchange capacity (CEC) ranged from low to very high, and the base saturation varied from moderate to very high. The alluvial plain is represented by Inceptisol in P1 and Typic Humustepts (P7), also by Oxic Humustepts (P3), then Mollisol on P4 (Typic Argiudolls) and Typic Haplustolls (P6), Alfisol on P5 (Typic Paleustalfs). Entisol on P2 (Typic Ustipsamments) was found in volcanic mountains and P9 (Typic Paleustolls) P8 (Ultic Paleustalfs), P10 (Inceptic Haplustalfs) are typical of volcanic hills. On the alluvial plains the land was categorized as the LQ class II, III and IV, the volcanic mountains were the LQ class IV, while the land on the volcanic hills was categorized as the LQ class VI. River bank erosion on the land river terraces can be held by the manufacture of gabions, talud, cliff reinforcement plants and terraces. The soil temperatures and high clay content can be regulated by mulching and organic materials.

Change of Land Quality with the Introduction of the Biochar Combination and Cocoa Waste Compost

This study examines the introduction of a combination of cocoa husk biochar which has been a cocoa plantation waste combined with fermented cocoa leaf waste. This study was conducted in West Sulawesi. This study used a randomized block design using a factorial model where the first factor was the administration of cocoa husk biochar with 3 levels, namely without administration (B0), 6 kg plant biochar-1 (B1), 12 kg plant biochar-1(B2) while the second factor was application of fermented cocoa leaf waste (K) which consists of 3 levels, namely without giving cocoa leaf waste (K0), giving 6 kg plant-1 (K1), biochar 12 kg plant-1 (K2). The nine treatment combinations were repeated three times to obtain 27 plant samples. The analysed soil characteristics are calcium (Ca), magnesium (Mg) and potassium (K) showed that the combination of cocoa rind Biochar and fermented cocoa leaf waste had an effect on increasing calcium (Ca) by 9.23. Magnesium 1.66 and for the element Potassium had no significant effect on the interaction of the combination of cocoa husk biochar and fermented cocoa leaf waste, but gave a very significant effect on the administration of biochar 12 kg plants-1.

Evaluation System Transformation of Multi-Scale Cultivated Land Quality and Analysis of Its Spatio-Temporal Variability

To master the transformation method and spatio-temporal variation characteristics of cultivated land quality at multiple scales, this paper constructed three spatial scales (Laixi city, Qingdao city, and Shandong province) and two temporal scales (the second survey (2007) and the third survey (2020)), and used a linear model to transform the evaluation system. Descriptive statistics, area statistics, spatial distribution, and aggregation analysis were used to explore the spatial scale variability, and the dynamic variation characteristics were analyzed. The results showed that (1) the R2 of scale transformation models are more than 0.826, which has a simple structure and strong universality; (2) with the administrative scale increases, the evaluation units’ number decreases, the spatial distribution is generally similar but progressively approximate, the high and low land levels gradually change to medium-level land, and the spatial aggregation degree is county-scale > provincial-scale > city-scale, with significant scale effect; and (3) in the past ten years, the average grade has increased from 6.26 to 6.13 in Laixi city, but still has much room for development. This study puts forward a method of spatio-temporal scale transformation and scale effect analysis for cultivated land quality, which has positive significance for improving the evaluation system, promoting land protection, and regional sustainable development.