Military conflicts are one of the inevitable factors that can cause countries to suffer from food insecurity due to reduced agricultural productivity, increased food prices, and the deterioration of agricultural land and infrastructure. Farmland may become fallowed and abandoned as a result of reduced investment in agricultural management caused by military conflicts. To rapidly assess the impact of conflicts on agricultural land and food security, the utilization of effective and feasible methods for the regular monitoring agricultural management status is necessary. To achieve this goal, we developed a framework for analyzing the spatiotemporal distribution of agricultural land and assessing the impact of the Ukraine–Russia war on agricultural management in Ukraine using remote sensing (RS) and geographic information system (GIS) technology. The random forest (RF) classifier, gap filling and Savitzky–Golay filtering (GF-SG) method, fallow-land algorithm based on neighborhood and temporal anomalies (FANTA) algorithm, and kernel density method were jointly used to classify and reveal the spatiotemporal distribution of fallowed and abandoned croplands from 2018 to 2022 based on Landsat time series data on the Google Earth Engine (GEE) platform. The results demonstrated that fallowed and abandoned croplands could be successfully and effectively identified through these proven methods. Hotspots of fallowed croplands frequently occurred in eastern Ukraine, and long-term consecutive fallow agricultural management caused cropland abandonment. Moreover, hotspots of war-driven fallowed croplands were found in western Kherson and the center of Luhansk, where the war has been escalated for a long time. This reveals that the war has had a significant negative impact on agricultural management and development. These results highlight the potential of developing an accessible methodological framework for conducting regular assessments to monitor the impact of military conflicts on food security and agricultural management.

The suitability evaluation of agricultural land at the regional scale is of great significance for protecting land and water resources and building sustainable agricultural systems. Based on climate, soil, topographical, and surface water resources, land suitability index (LSI) data for maize, rice, and soybeans are established using an analytical hierarchy process and matter element analysis (AHP–MEA) model in Jilin Province, China. The results show that there is a significant positive linear correlation between the LSI and the measured yield, which indicates that the model has an ideal effect and certain reference and extension significance. The main limiting factors for maize and soybean planting are pH, total nitrogen (TN), available phosphorus (AP), and soil texture, while water shortage limits rice planting. Different spatial structure optimization schemes for planting are established using the LSI and measured yield, along with economic indices. This study shows that the scheme that integrates policy and cost can make full use of land and water resources and promote the economic growth of agriculture. After optimization, the planting areas of maize, rice, and soybeans were 7.22, 2.44, and 0.71 million ha, respectively, representing an increase of 15.71 billion yuan over the agricultural GDP for the existing planting structure. It is expected that this study will provide a basis for follow-up studies on crop cultivation suitability.

Drought, which causes the economic, social, and environmental losses, also threatens food security worldwide. In this study, we developed a vegetation-soil water deficit (VSWD) method to better assess agricultural droughts. The VSWD method considers precipitation, potential evapotranspiration (PET) and soil moisture. The soil moisture from different soil layers was compared with the in situ drought indices to select the appropriate depths for calculating soil moisture during growing seasons. The VSWD method and other indices for assessing the agricultural droughts, i.e., Scaled Drought Condition Index (SDCI), Vegetation Health Index (VHI) and Temperature Vegetation Dryness Index (TVDI), were compared with the in situ and multi-scales of Standardized Precipitation Evapotranspiration Index (SPEIs). The results show that the VSWD method has better performance than SDCI, VHI, and TVDI. Based on the drought events collected from field sampling, it is found that the VSWD method can better distinguish the severities of agricultural droughts than other indices mentioned here. Moreover, the performances of VSWD, SPEIs, SDCI and VHI in the major historical drought events recorded in the study area show that VSWD has generated the most sensible results than others. However, the limitation of the VSWD method is also discussed.

Fast and accurate SOM estimation and spatial mapping are significant for cultivated land planning and management, crop growth monitoring, and soil carbon pool estimation. It is a key problem to construct a fast and efficient estimation model based on hyperspectral remote sensing image data to realize the inversion mapping of SOM in large areas. In order to solve the problem that the estimation accuracy is not high due to the influence of hyperspectral image quality and soil sample quantity during the estimation model construction, this study explored a method for constructing an estimation model of SOM contents based on a new stacking ensemble learning algorithm and hyperspectral images. Surface soil samples in Huangzhong County of Qinghai Province were collected, and their ZY1-02D hyperspectral remote sensing images were investigated. As input data, a feature band dataset was constructed using the Pearson correlation coefficient and successive projections algorithm. Based on the dataset, a new SOM estimation model under the stacking ensemble learning framework combined with heterogeneous models was developed by optimizing the combination of base and meta-learners. Finally, the spatial distribution map of SOM was plotted based on the result of the model over the study area. The result suggested that the input data quality of the estimation model is improved by constructing a feature band dataset. The multi-class ensemble learning estimation model with the combination strategy of the base and meta-learners has better predictive effects and stability than the single-algorithm and single-level ensemble models with homogeneous learners. The coefficient of determination is 0.829, the residual prediction deviation is 2.85, and the predictive set root mean square error is 1.953. The results can provide new ideas for estimating SOM content using hyperspectral images and ensemble learning algorithms, and serve as a reference for mapping large-scale SOM spatial distribution using space-borne hyperspectral images.

Chlorophyll absorbs light energy and converts it into chemical energy, making it a crucial biochemical parameter for monitoring vegetation health, detecting environmental stress, and predicting physiological states. Accurate and rapid estimation of canopy chlorophyll content is crucial for assessing vegetation dynamics, ecological changes, and growth patterns. Remote sensing technology has become an indispensable tool for monitoring vegetation chlorophyll content since 2015, with more than 50 research papers published annually, contributing to a substantial body of case studies. This review discusses remote sensing technologies currently used for estimating vegetation chlorophyll content, focusing on four key aspects: the acquisition of reference datasets, the identification of optimal spectral variables, the selection of estimation models, and the analysis of application scenarios. The results indicate that spectral bands in the visible and red-edge regions (e.g., 530 nm, 670 nm, and 705 nm) provide high prediction accuracy. Machine learning methods, such as random forest and support vector regression, exhibit excellent performance, with determination coefficients (R²) typically exceeding 0.9, although overfitting remains an issue. Although radiative transfer models are slightly less accurate (R² = 0.6–0.8), they provide greater interpretability. Hybrid models integrating machine learning and radiative transfer show strong potential to balance accuracy and generalizability. Future research should improve model generalizability for different vegetation types and environmental conditions and integrate multi-source remote sensing data to improve spatial and temporal resolution. Combining physical models with data processing methods, such as artificial intelligence, can improve scalability, cost-effectiveness, and real-time monitoring capabilities.

The use of satellite remote sensing could effectively predict maize yield. However, many statistical prediction models using remote sensing data cannot extend to the regional scale without considering the regional climate. This paper first introduced the hierarchical linear modeling (HLM) method to solve maize-yield prediction problems over years and regions. The normalized difference vegetation index (NDVI), calculated by the spectrum of the Landsat 8 operational land imager (OLI), and meteorological data were introduced as input parameters in the maize-yield prediction model proposed in this paper. We built models using 100 samples from 10 areas, and used 101 other samples from 34 areas to evaluate the model’s performance in Jilin province. HLM provided higher accuracy with an adjusted determination coefficient equal to 0.75, root mean square error (RMSE_V) equal to 0.94 t/ha, and normalized RMSE_V equal to 9.79%. Results showed that the HLM approach outperformed linear regression (LR) and multiple LR (MLR) methods. The HLM method based on the Landsat 8 OLI NDVI and meteorological data could flexibly adjust in different regional climatic conditions. They had higher spatiotemporal expansibility than that of widely used yield estimation models (e.g., LR and MLR). This is helpful for the accurate management of maize fields.