Agricultural Land Classification Research Articles

Prediction of Canopy Cover for Agricultural Land Classification in Land Parcel Identification System (LPIS) Data Using Planet-Scope Multispectral Images: A Case of Gelendost District

Prediction of Canopy Cover for Agricultural Land Classification in Land Parcel Identification System (LPIS) Data Using Planet-Scope Multispectral Images: A Case of Gelendost District

Spectrally Segmented-Enhanced Neural Network for Precise Land Cover Object Classification in Hyperspectral Imagery

The paradigm shift brought by deep learning in land cover object classification in hyperspectral images (HSIs) is undeniable, particularly in addressing the intricate 3D cube structure inherent in HSI data. Leveraging convolutional neural networks (CNNs), despite their architectural constraints, offers a promising solution for precise spectral data classification. However, challenges persist in object classification in hyperspectral imagery or hyperspectral image classification, including the curse of dimensionality, data redundancy, overfitting, and computational costs. To tackle these hurdles, we introduce the spectrally segmented-enhanced neural network (SENN), a novel model integrating segmentation-based, multi-layer CNNs, SVM classification, and spectrally segmented dimensionality reduction. SENN adeptly integrates spectral–spatial data and is particularly crucial for agricultural land classification. By strategically fusing CNNs and support vector machines (SVMs), SENN enhances class differentiation while mitigating overfitting through dropout and early stopping techniques. Our contributions extend to effective dimensionality reduction, precise CNN-based classification, and enhanced performance via CNN-SVM fusion. SENN harnesses spectral information to surmount challenges in “hyperspectral image classification in hyperspectral imagery”, marking a significant advancement in accuracy and efficiency within this domain.

A Joint Network of Super-resolution and Active Learning for Agricultural Land Classification

A Joint Network of Super-resolution and Active Learning for Agricultural Land Classification

AGRO-ECOLOGICAL CLASSIFICATION OF AGRICULTURAL LAND USING MACHINE LEARNING, GIS AND REMOTE SENSING DATA

The paper is devoted to agro-ecological classification of agricultural land using modern methods of geo-information data analysis and machine learning.
 Background. There are few works in the literature, which cover the issues of accuracy of machine learning (MLL) models for agro-ecological grouping of agricultural land. A large number of raster information layers are used to improve the accuracy of land classification from satellite images. This considerably increases the time of training and testing MMOs, producing thematic maps of agricultural land classification. This approach requires considerably high computing resources and a considerable amount of computer RAM. Raster GIS data models occupy a much larger volume than vector models. In this regard, research on automated agro-ecological (grouping, classification) of agricultural land using vector GIS models is of practical importance.
 Purpose. The aim of the study is to apply GIS methods, remote sensing (ERS) data and machine learning methods for agricultural land grouping.
 Materials and methods. The materials used were synthesized multispectral high spatial resolution Sentinel-2A images, maps of vegetation indices NDVI (Normalized Difference Vegetation Index), OSAVI (Optimized Soil Adjusted Vegetation Index), EVI2 (Enhanced Vegetation Index2), NDWI (Normalized Difference Water Index), SAVI, PVI, GDVI, MCARI, NDRE, TSAVI; topographic map, ALOS DSM (30 m/pixel) and ALOS PALSAR (12.5 m/pixel) satellite images, soil map and field survey results. Field measurements were carried out using the Triump-2 satellite geodetic receiver and included determination of coordinates of characteristic points of land plot boundaries, relief elements, and soil survey.
 Digital spatial model of land use was created using GIS ArcGIS and QGIS, Python engineering libraries were used in the machine learning process.
 Results. Agro-ecological grouping of lands was realized by the example of the farm "Zerno Sibiri" of Novosibirsk region using the following machine learning methods: Random Forest (RF) method, Decision Tree (DT), k-nearest neighbours method (KNN). The best accuracy is the RF machine learning model. The accuracy of the model averaged 97.9% (with training 99.9%, testing 98.8%, and cross validation 95.0%). The Root Mean Square Error (RMSE) is 0.006: for training sample 0.001; test sample 0.076; validation sample 0.123 respectively). The mean kappa coefficient was 0.97 (1.00 for the training sample; 0.982 for the test sample, and 0.927 for the validation sample). 
 Conclusion. The offered method of agro-ecological grouping of agricultural lands by means of GIS, RS data and machine learning methods enabled to distinguish informative quantitative indicators of the relief. The main essence of the proposed method is to create a machine learning model (MLM) based on a spatial dataset. The spatial dataset is formed using geoinformation analysis methods and includes: geomorphometric maps, maps of agrometeorological parameters, soil map, on-farm land management map and operational-territorial units of land classification. The application of vector data model allowed for agro-ecological grouping of agricultural lands in automated mode, to accelerate labor-intensive process of raster data recognition, to increase objectivity of the work. The suggested method of agro-ecological agro-ecological grouping of lands allows taking into account the totality of relief and soil-ecological conditions indicators with the help of geoinformation analysis methods, remote sensing and machine learning.

Представяне на „Софтуер за потенциална относителна оценка и класификация по пригодност за земеползване на земеделските земи в България“

In 2023, I developed “Software for potential land use suitability classification of agricultural land in Bulgaria” (Radovanova, 2023). It works in MS Office terms, programmed entirely through Microsoft Excel. The software with the corresponding methodology is essentially an update of the legally adopted “Methodology for work on the cadastre of agricultural land in Bulgaria” (Petrov E. et al., 1988), and this was achieved by a newly developed “Methodology for potential assessment of agricultural land in Bulgaria” (Radovanova, 2020). Actual and potential assessment are fully comparable in terms of approach, scheme, parameters, etc. The difference between actual and potential assessment, as in the case of software, is that in the case of potential assessment of agricultural land, potential characteristics (subject to improvement by external intervention) are used. These characteristics are: depth of the humus horizon, soil reaction, humus content, groundwater level, stoniness of the plough soil and degree of salinity and/or alkalinity. For these characteristics, 86 newly developed Bonite scales and algorithms have been applied (Radovanova, 2020). The software uses the built-in functions of Microsoft Excel. The mechanism of the existing final land use suitability categorisation of the assessed land is fully maintained, but is now considered in a “potential” aspect and uses the new potential algorithms. The software allows the printing of an official document of the potential assessment of the agricultural land concerned.

A Spatio-Temporal Encoding Neural Network for Semantic Segmentation of Satellite Image Time Series

Remote sensing image semantic segmentation plays a crucial role in various fields, such as environmental monitoring, urban planning, and agricultural land classification. However, most current research primarily focuses on utilizing the spatial and spectral information of single-temporal remote sensing images, neglecting the valuable temporal information present in historical image sequences. In fact, historical images often contain valuable phenological variations in land features, which exhibit diverse patterns and can significantly benefit from semantic segmentation tasks. This paper introduces a semantic segmentation framework for satellite image time series (SITS) based on dilated convolution and a Transformer encoder. The framework includes spatial encoding and temporal encoding. Spatial encoding, utilizing dilated convolutions exclusively, mitigates the loss of spatial accuracy and the need for up-sampling, while allowing for the extraction of rich multi-scale features through a combination of different dilation rates and dense connections. Temporal encoding leverages a Transformer encoder to extract temporal features for each pixel in the image. To better capture the annual periodic patterns of phenological phenomena in land features, position encoding is calculated based on the image’s acquisition date within the year. To assess the performance of this framework, comparative and ablation experiments were conducted using the PASTIS dataset. The experiments indicate that this framework achieves highly competitive performance with relatively low optimization parameters, resulting in an improvement of 8 percentage points in the mean Intersection over Union (mIoU).

Environmental land use conflicts and ecosystem services: a paper review

The topic of potential conflicts related to land use involving human activities in a watershed is an important matter to be discussed. The background of this research is that conditions in mountainous watersheds, and agricultural land use cause changes in ecosystem services, with trade-offs between crop production and erosion regulation. Watershed management with an environmental concept often faces problems with different interests among stakeholders. Although several studies have initiated the mapping of land-use conflicts between human activities and conservation, the spatial assessment of land-use conflicts on environmental issues and trade-offs of ecosystem services in agricultural areas has not been fully considered. The purpose of this study is to map land use, with indicators of measuring conflicts in the value of conservation and agricultural development, through scenarios of the level of erosion hazard on agricultural land. This study provides input to decision-makers regarding watershed conservation efforts that still consider aspects of the economic needs of agricultural land. The objective of the reviewers is to understand how the concept of mapping the potential conflict of land use in the Haean watershed in South Korea can be applied in Indonesia. The methodology used is to apply the agricultural land suitability index based on various analytical criteria to estimate the spatial preferences of agricultural activities. To predict erosion, using the Revised Universal Soil Loss Equation (RUSLE) method and the classification of agricultural land in the watershed is divided into four levels of land use conflict (lowest, low, high, and highest).

Mapping Agricultural Land in Afghanistan’s Opium Provinces Using a Generalised Deep Learning Model and Medium Resolution Satellite Imagery

Understanding the relationship between land use and opium production is critical for monitoring the dynamics of poppy cultivation and developing an effective counter narcotics policy in Afghanistan. However, mapping agricultural land accurately and rapidly is challenging, as current methods require resource-intensive and time consuming manual image-interpretation. Deep convolutional neural nets have been shown to greatly reduce the manual effort in mapping agriculture from satellite imagery but require large amounts of densely labelled training data for model training. Here we develop a generalised model using past images and labels from different medium resolution satellite sensors for fully automatic agricultural land classification using the latest medium resolution satellite imagery. The model (FCN-8) is first trained on Disaster Monitoring Constellation (DMC) satellite images from 2007 to 2009. The effect of shape, texture and spectral features on model performance are investigated along with normalisation in order to standardise input medium resolution imagery from DMC, Landsat-5, Landsat-8, and Sentinel-2 for transfer learning between sensors and across years. Textural features make the highest contribution to overall accuracy (∼73%) while the effect of shape is minimal. The model accuracy on new images, with no additional training, is comparable to visual image interpretation (overall > 95%, user accuracy > 91%, producer accuracy > 85%, and frequency weighted intersection over union > 67%). The model is robust and was used to map agriculture from archive images (1990) and can be used in other areas with similar landscapes. The model can be updated by fine tuning using smaller, sparsely labelled datasets in the future. The generalised model was used to map the change in agricultural area in Helmand Province, showing the expansion of agricultural land into former desert areas. Training generalised deep learning models using data from both new and long-term EO programmes, with little or no requirement for fine tuning, is an exciting opportunity for automating image classification across datasets and through time that can improve our understanding of the environment.

Vejetasyon İndeksleri, Ana Bileşenler Analizi ve Google Earth Engine Kullanılarak Tarımsal Alan Sınıflandırması: Söke/Aydın Örneği

Land use and land cover (LULC) classification is known to be one of the most widely used indicators of environmental change and degradation all over the world. There are various algorithms and methods for LULC classification, whereby reliability of the classification maps presents the principal concern. The study focused on evaluation of accuracies of LULC maps produced from original bands of Sentinel-2 imageries together with Normalized Difference Vegetation Index (NDVI), Green NDVI (GNDVI), and Principal Component Analysis (PCA) using Google Earth Engine (GEE) platform to identify best enhancing method for agricultural land classification. Moreover, short-term LULC changes aimed to be identified in the specified area. To achieve the aims, all available imageries acquired in the same month of different years with less than 10% cloud contamination were used to compose averaged images for May 2018 and May 2022 for generating LULC2018 and LULC2022 maps. The area has separated into seven main classes, namely, olive (O), perennial cultivation (P), non-perennial cultivation (NP), forest (F), natural vegetation (N), settled area-bare land (S), and water surface (W) via random forest algorithym. Reliabilities of LULC maps were evaluated through accuracy assessment procedures considering stratified randomized control points. Transitions between each LULC classes were identified.

HyperSFormer: A Transformer-Based End-to-End Hyperspectral Image Classification Method for Crop Classification

Crop classification of large-scale agricultural land is crucial for crop monitoring and yield estimation. Hyperspectral image classification has proven to be an effective method for this task. Most current popular hyperspectral image classification methods are based on image classification, specifically on convolutional neural networks (CNNs) and recurrent neural networks (RNNs). In contrast, this paper focuses on methods based on semantic segmentation and proposes a new transformer-based approach called HyperSFormer for crop hyperspectral image classification. The key enhancement of the proposed method is the replacement of the encoder in SegFormer with an improved Swin Transformer while keeping the SegFormer decoder. The entire model adopts a simple and uniform transformer architecture. Additionally, the paper introduces the hyper patch embedding (HPE) module to extract spectral and local spatial information from the hyperspectral images, which enhances the effectiveness of the features used as input for the model. To ensure detailed model processing and achieve end-to-end hyperspectral image classification, the transpose padding upsample (TPU) module is proposed for the model’s output. In order to address the problem of insufficient and imbalanced samples in hyperspectral image classification, the paper designs an adaptive min log sampling (AMLS) strategy and a loss function that incorporates dice loss and focal loss to assist model training. Experimental results using three public hyperspectral image datasets demonstrate the strong performance of HyperSFormer, particularly in the presence of imbalanced sample data, complex negative samples, and mixed sample classes. HyperSFormer outperforms state-of-the-art methods, including fast patch-free global learning (FPGA), a spectral–spatial-dependent global learning framework (SSDGL), and SegFormer, by at least 2.7% in the mean intersection over union (mIoU). It also improves the overall accuracy and average accuracy values by at least 0.9% and 0.3%, respectively, and the kappa coefficient by at least 0.011. Furthermore, ablation experiments were conducted to determine the optimal hyperparameter and loss function settings for the proposed method, validating the rationality of these settings and the fusion loss function.

The problem of the modernisation of land and building register in Poland as exemplified by the village of Rejowiec

The right of ownership is the broadest and one of the most basic property rights, which allows the owner to use and dispose of possessions without the participation of third parties. Therefore, the process of modernising the land and building register (MLBR) is extremely important considering, for example, that the data contained in this register is the basis for the subsequent calculation of public-legal obligations associated with the owned property, such as taxes. According to the current legislation, the data collected in the land and building register (cadastre) on the type of land use in combination with the soil classification of agricultural and forest land, the use of the property, its purpose and other information, are the basis for determining the agricultural, forest and real estate taxes by the competent authority. In Poland, in most cases, we have to deal with a situation in which the data appearing in the register differs from the actual state. Municipal budgets suffer losses because of this, and owners of properties of similar area and value sometimes pay completely different taxes. In this paper, the authors show a practical example of MLBR and the problems existed with it.

UAV, a Farm Map, and Machine Learning Technology Convergence Classification Method of a Corn Cultivation Area

South Korea’s agriculture is characterized by a mixture of various cultivated crops. In such an agricultural environment, convergence technology for ICT (information, communications, and technology) and AI (artificial intelligence) as well as agriculture is required to classify objects and predict yields. In general, the classification of paddy fields and field boundaries takes a lot of time and effort. The Farm Map was developed to clearly demarcate and classify the boundaries of paddy fields and fields in Korea. Therefore, this study tried to minimize the time and effort required to divide paddy fields and fields through the application of the Farm Map. To improve the fact that UAV image processing for a wide area requires a lot of time and effort to classify objects, we suggest a method for optimizing cultivated crop recognition. This study aimed to evaluate the applicability and effectiveness of machine learning classification techniques using a Farm Map in object-based mapping of agricultural land using unmanned aerial vehicles (UAVs). In this study, the advanced function selection method for object classification is to improve classification accuracy by using two types of classifiers, support vector machine (SVM) and random forest (RF). As a result of classification by applying a Farm Map-based SVM algorithm to wide-area UAV images, producer’s accuracy (PA) was 81.68%, user’s accuracy (UA) was 75.09%, the Kappa coefficient was 0.77, and the F-measure was 0.78. The results of classification by the Farm Map-based RF algorithm were as follows: PA of 96.58%, UA of 92.27%, a Kappa coefficient of 0.94, and the F-measure of 0.94. In the cultivation environment in which various crops were mixed, the corn cultivation area was estimated to be 96.54 ha by SVM, showing an accuracy of 90.27%. RF provided an estimate of 98.77 ha and showed an accuracy of 92.36%, which was higher than that of SVM. As a result of using the Farm Map for the object-based classification method, the agricultural land classification showed a higher efficiency in terms of time than the existing object classification method. Most importantly, it was confirmed that the efficiency of data processing can be increased by minimizing the possibility of misclassification in the obtained results. The obtained results confirmed that rapid and reliable analysis is possible when the cultivated area of crops is identified using UAV images, a Farm Map, and machine learning.

Mapping Climate Classification of Oldeman in Agricultural Resources Management in South Tapanuli District

The Oldeman classification system is very useful in the classification of agricultural land for food crops in Indonesia using the element of rainfall. The criteria are based on the calculation of wet months (BB) and dry months (BK), respectively, with the limits taking into account the chance of rain, effective rain and water requirements for plants. With the Oldeman climate classification, a cropping pattern system can be determined in an area. Based on the research results obtained 4 types of climate in the South Tapanuli region, namely: D1, D2, E1, and E2. The wettest climate type in the Sipirok region is climate type D1 with 4-5 wet months and ,2 dry months. And those that are suitable for planting short-lived rice are 3 (three) harvests or short-lived rice is 2 (two) harvests and secondary crops 1 (sastu) harvests. The driest climate types in the East Angkola region are climate types E1 and E2 with the number of wet months ,3 and the number of dry months 6-7. What is suitable for planting short-lived lowland rice is 1 (one) harvest and secondary crops 2 (two) times, the second special harvest falls in the dry season. The topographical condition of an area greatly influences the climatic conditions of the area. With a good climate classification and suitable for rice plants without irrigation assistance, the climate types are D1 and D2, namely in Sipirok District, West Angkola District and a Small Part of Saipar Dolok Hole and Batang Angkola Districts. Meanwhile, the East Angkola sub-district can be planted with rice with the help of irrigation, especially in the dry season.

Automation in Agriculture by Machine and Deep Learning Techniques: A Review of Recent Developments

Recently, agriculture has gained much attention regarding automation by artificial intelligence techniques and robotic systems. Particularly, with the advancements in machine learning (ML) concepts, significant improvements have been observed in agricultural tasks. The ability of automatic feature extraction creates an adaptive nature in deep learning (DL), specifically convolutional neural networks to achieve human-level accuracy in various agricultural applications, prominent among which are plant disease detection and classification, weed/crop discrimination, fruit counting, land cover classification, and crop/plant recognition. This review presents the performance of recent uses in agricultural robots by the implementation of ML and DL algorithms/architectures during the last decade. Performance plots are drawn to study the effectiveness of deep learning over traditional machine learning models for certain agricultural operations. The analysis of prominent studies highlighted that the DL-based models, like RCNN (Region-based Convolutional Neural Network), achieve a higher plant disease/pest detection rate (82.51%) than the well-known ML algorithms, including Multi-Layer Perceptron (64.9%) and K-nearest Neighbour (63.76%). The famous DL architecture named ResNet-18 attained more accurate Area Under the Curve (94.84%), and outperformed ML-based techniques, including Random Forest (RF) (70.16%) and Support Vector Machine (SVM) (60.6%), for crop/weed discrimination. Another DL model called FCN (Fully Convolutional Networks) recorded higher accuracy (83.9%) than SVM (67.6%) and RF (65.6%) algorithms for the classification of agricultural land covers. Finally, some important research gaps from the previous studies and innovative future directions are also noted to help propel automation in agriculture up to the next level.

Improvement of scientific and methodological approaches to the classification of agricultural lands

The existing scientific and methodological approaches to the classification of agricultural lands both in Ukraine and abroad are analysed. It is established that modern classifications of lands should be formed based on such principles as complexity, exhaustiveness, hierarchy and legality; meet the requirements of automated technologies for maintaining the state land cadastre and land monitoring; take into account the recommendations of the European Economic Commission at the UN. Comprehensiveness of identifying of all potentially possible land uses, which will facilitate effective state control over land use in accordance with their intended purpose will be ensured through a comprehensive approach to the development of land classification; its consistency with other current state classification systems in the field of nature management and the use of clearly defined target criteria, parameters and features, the system of which is determined by the specific scope and requirements of the relevant user groups – through a systematic approach. A fragment of 3-digit classification of agricultural lands is proposed, in which hierarchically placed interrelated indicators characterizing the legal regime of land use (legal block of land classifier) and their physical condition (physical block of land classifier) are placed hierarchically. In order to legally consolidate the categories “type, subtype, variety of lands” that form the basis of the physical block of the land classifier, it is necessary to make appropriate changes to the national land legislation.

Creating large-scale maps for agriculture using remote sensing

Reliable information on the real state of agricultural lands will be required to the development of appropriate measures for the rational use of agricultural lands. To obtain such information, it is necessary to keep permanent and systematic records and inventories of land resources. Large-scale special plans and maps will be required for accounting, inventory and classification of agricultural land. Currently in Uzbekistan such cartographic materials are being created on the scale 1: 10 000 and 1: 25 000 by administrative and territorial units, farms or individual land plots. The article considers the issues of creation of special maps of agricultural land in scale 1:10000 on the example of Sharof Rashidov district of Jizzakh region using remote sensing data with very high spatial resolution KOMPSAT-3.

КАЧЕСТВЕННОЕ СОСТОЯНИЕ СЕЛЬСКОХОЗЯЙСТВЕННЫХ ЗЕМЕЛЬ РЕСПУБЛИКИ КАЗАХСТАН

As the purpose of the study, the author reveals the problems of improving the quality accounting of agricultural land. The article examines the state of farmland, problems of land management, in particular in the field of agriculture. The omissions in the land legislation in relation to the classification of agricultural lands and their characteristics are revealed. The advantages of space monitoring of land areas are shown. The lack of economic criteria leads to ineffective planning, and in the future - irrational use of land areas. It has been determined that agricultural land that has been withdrawn from circulation is not effective in market economy, therefore, measures are required to involve them in circulation. The dependence of most industries on land quality determines the importance of choosing a methodology for assessing the quality of land. For the effective use of farmland, conscientious agricultural producers should be encouraged, which, in turn, will contribute to the increase in arable land, hayfields, pastures, and perennial plantings. The methods of correlating agricultural land to a specific type of agricultural land in foreign countries are generalized and examples of their improvement are given taking into account regional characteristics. Recommendations on improving the quality accounting of agricultural land using the example of GIS technologies were developed.

Study on safe usage of agricultural land in karst and non-karst areas based on soil Cd and prediction of Cd in rice: A case study of Heng County, Guangxi

It is well-known that Cd concentration in the soil, Cd bioavailability, and Cd bioaccumulation in rice (Oryza sativa L.) grains vary greatly in different soil parent materials. Therefore, the classification of agricultural land environmental quality and agricultural land safe usage based on the Cd content in the soil has a wide deviation. By the application of systematic sampling and analysis of soil and rice samples, and in comparison with the different soil parent materials in Heng County, it was found that soil derived from carbonate rocks has higher total Cd and lower mobile Cd proportion than soil from the non-karst areas. This result indicated that soil carbonates raised soil pH and the adsorption of Fe/Mn oxide/hydroxide on Cd significantly reduced the bioavailability of Cd in karst areas. In contrast, acidic soils with relatively lower CaO, TFe2O3, Mn and total Cd contents, the grown rice plants accumulated higher Cd in their grains. Further research confirmed significant differences in Cd bioaccumulation abilities in the soil between karst and non-karst areas. On this basis, the bioaccumulation factor prediction models of Cd in rice grains were developed in karst and non-karst areas. According to the total concentration of Cd in topsoil samples obtained from the area survey and the predicted Cd content in rice grains, an agricultural land safe usage scheme was put forward. The results showed that the agricultural land classification method based on Cd concentration in the soil and rice grains was more accurate and scientific than that based on the Cd contents in the soil alone.

Родючість землі як критерій ціннісної класифікації земель сільськогосподарського призначення

Introduction. Soil fertility is the most important parameter that reflects the ecological condition of agricultural land. The complex of interconnected and interdependent properties that determines the natural fertility has certain differences on different types of soils. The constitutional properties of soils are practically not subject to significant changes in relatively short periods of time, while the dynamic-functional ones are quite variable under the influence of anthropogenic factors. In this regard, they have a different impact on the formation of the value of agricultural land. Price is the quantitative equivalent of assessing the right to own or use land resources, and value is an integrated indicator that determines both the set of properties of land resources and their qualitative and quantitative characteristics. Therefore, taking into account the fertility of land as a criterion for the value classification of agricultural land is beyond doubt. Purpose. The purpose of the article is to prove the importance of determining the value of land classification taking into account the fertility of the soil. In this article the presentation of proposals for predictive modeling of land relations is showed. Results. Humus is an essential organic element in which the main nutrients for crops are formed and stored, and therefore humus most accurately characterizes one or another type of productive agricultural soil. It is caused due to the deficit of humus balance in the soil that the value equivalent of agricultural lands significantly decreases. The disproportionate actions of man year by year lead to the transformation of the qualitative properties of productive soils with the loss of significant particles of humus. Direct impoverishment of lands occurs due to insufficient application of organic fertilizers, excessive plowing of rural landscapes, neglect of field crop rotations, and excessive removal of nutrients by commercial crops, reduction of organic residues entering the soil, etc. Conclusions. Genetic classification of land by origin, properties and characteristics is a platform for combining their value equivalent with certain rules and regulations of sustainable productive land use. Based on this, it is appropriate to define the “genetic approach” as a way to study certain phenomena, based not only on the analysis of their genesis, but also on the features of dynamic development associated with the manifestation of ecological and economic transformations in agricultural land use.

Applied methods for remote sensing of the Earth in the interests of agriculture of Russian Far East

The paper presents the methods (procedures) of processing data from satellites of the Landsat series, for classification and evaluation of agricultural land used for rice cultivation in the area of Lake Khanka, Primorsky Krai. Opportunities for using various vegetation indices for analysis are described, as well as their complex combinations that ensure most accurate identification of the considered culture during the entire period of active vegetation. The developed procedures provided foundation for analysing the growth dynamics of crops innate to the Far East, where the potential for using satellite data for agricultural needs remains practically untapped.