This study integrates a dynamic plant growth model with a three-dimensional (3D) radiative transfer model (RTM) for maize traits retrieval using high spatial-spectral resolution airborne data. The research combines the Discrete Anisotropic Radiative Transfer (DART) model with the Dynamic L-System-based Architectural maize (DLAmaize) growth model to simulate field reflectance. Comparison with the 1D RTM SAIL revealed limitations in representing row structure effects, field slope, and complex light-canopy interactions. Novel Global Sensitivity Analyses (GSA) were carried out using dependence-based methods to overcome limitations traditional variance-based approaches, enabling better characterization of hyperspectral sensitivity to changes in leaf biochemistry, canopy architecture, and soil moisture. GSA provided complementary results to assess estimation uncertainties of the proposed traits retrieval method across growth stages. A hybrid inversion framework combining DART simulations with an active learning strategy using Kernel Ridge Regression was implemented for traits estimation. The approach was validated using ground data and HyPlant-DUAL airborne hyperspectral images from two field campaigns in 2018 and achieved high retrieval accuracy of key maize traits: leaf area index (LAI, R2=0.91, RMSE=0.42 m2/m2), leaf chlorophyll content (LCC, R2=0.61, RMSE=3.89 mu g/cm2), leaf nitrogen content (LNC, R2=0.86, RMSE=1.13 x 10-2 mg/cm2), leaf dry matter content (LMA, R2=0.84, RMSE=0.15 mg/cm2), and leaf water content (LWC, R2=0.78, RMSE=0.88 mg/cm2). The validated models were used to generate two-date 10 m resolution maps, showing good spatial consistency and traits dynamics. The findings demonstrate that integrating 3D RTMs with dynamic growth models is suited for maize trait mapping from hyperspectral data in varying growing conditions.

The wheat powdery mildew (WPM) is one of the most severe crop diseases worldwide, affecting wheat growth and causing yield losses. The WPM was a bottom-up disease that caused the loss of cell integrity, leaf wilting, and canopy structure damage with these symptoms altering the crop's functional traits (CFT) and canopy spectra. The unmanned aerial vehicle (UAV)-based hyperspectral analysis became a mainstream method for WPM detection. However, the CFT changes experienced by infected wheats, the relationship between CFT and canopy spectra, and their role in WPM detection remained unclear, which might blur the understanding for the WPM infection. Therefore, this study aimed to propose a new method that considered the role of CFT for detecting WPM and estimating disease severity. The UAV hyperspectral data used in this study were collected from the Plant Protection Institute's research demonstration base, Xinxiang city, China, covering a broad range of WPM severity (0-85 %) from 2022 to 2024. The potential of eight CFT [leaf structure parameter (N), leaf area index (LAI), chlorophyll a + b content (Cab), carotenoids (Car), Car/Cab, anthocyanins (Ant), canopy chlorophyll content (CCC) and average leaf angle (Deg)] obtained from a hybrid method combining a radiative transfer model and random forest (RF) and fifty-five narrow-band hyperspectral indices (NHI) was explored in WPM detection. Results indicated that N, Cab, Ant, Car, LAI, and CCC showed a decreasing trend with increasing disease severity, while Deg and Car/Cab exhibited the opposite pattern. There were marked differences between healthy samples and the two higher infection levels (moderate and severe infection) for Cab, Car, LAI, Deg, CCC, and Car/Cab. N and Ant only showed significant differences between the healthy samples and the highest infection level (severe infection). As Cab, Car, and Ant decreased, the spectral reflectance in the visible light region increased. The decrease in N and LAI was accompanied by a reduction in reflectance across the entire spectral range and the near-infrared area, which was exactly the opposite of Deg. The introduction of CFT greatly improved the accuracy of the WPM severity estimation model with R2 of 0.92. Features related to photosynthesis, pigment content, and canopy structure played a decisive role in estimating WPM severity. Also, results found that the feature importance showed a remarkable interchange as increasing disease levels. Using features that described canopy structure changes, such as optimized soil adjusted vegetation index, LAI, visible atmospherically resistant indices, and CCC, the mild infection stage of this disease was most easily distinguished from healthy samples. In contrast, most severe impacts of WPM were best characterized by features related to photosynthesis (e.g., photochemical reflectance index 515) and pigment content (e.g., normalized phaeophytinization index). This study help deepen the understanding of symptoms and spectral responses caused by WPM infection.

Weed control in agricultural systems is of the utmost importance. Weeds reduce crop yields by up to 30% to 40%. Different methods are used to control weeds, such as manual, chemical, mechanical, and precision weed management. Weeds are managed more effectively by using the hand weeding method, which nevertheless falls short due to the unavailability of labor during peak periods and increasing labor wages. Generally, manual weeding tools have higher weeding efficiency (72% to 99%) but lower field capacity (0.001 to 0.033 hm(2)/h). Use of chemicals to control weeds is the most efficient and cost-effective strategy. Chemical weedicides have been used excessively and inappropriately, which has over time resulted in many issues with food and environmental damage. Mechanical weed control improves soil aeration, increases water retention capacity, slows weed growth, and has no negative effects on plants. Mechanical weed management techniques have been gaining importance recently. Automation in agriculture has significantly enhanced mechanization inputs for weed management. The development of precision weed management techniques offers an efficient way to control weeds, contributing to greater sustainability and improved agricultural productivity. Devices for agricultural automated navigation have been built on the rapid deployment of sensors, microcontrollers, and computing technologies into the field. The automated system saves time and reduces labor requirements and health risks associated with drudgery, all of which contribute to more effective farm operations. The new era of agriculture demands highly efficient and effective autonomous weed control techniques. Methods such as remote sensing, multispectral and hyperspectral imaging, and the use of robots or UAVs (drones) can significantly reduce labor requirements, enhance food production speed, maintain crop quality, address ecological imbalances, and ensure the precise application of agrochemicals. Weed monitoring is made more effective and safer for the environment through integrated weed management and UAVs. In the future, weed control by UAV or robot will be two of the key solutions because they do not pollute the environment or cause plant damage, nor do they compact the soil, because UAV sprays above the ground and robotic machines are lighter than tractor operated machines. This paper aims to review conventional, chemical, mechanical, and precision weed management methods.

Background: Rodents severely damage the ecological environment of grasslands, and rodent mounds of different ages require distinct management strategies. Understanding the age of these mounds aids in formulating targeted restoration measures, which can enhance grassland productivity and biodiversity. Current surveys of rodent mounds rely on ground exposure and mound height to determine their age, which is time-consuming and labor-intensive. Remote sensing methods can quickly and easily identify the distribution of rodent mounds. Existing remote sensing images use ground exposure and mound height for identification but do not distinguish between mounds of different ages, such as one-year-old and two-year-old mounds. According to the existing literature, rodent mounds of different ages exhibit significant differences in vegetation structure, soil background, and plant diversity. Utilizing a combination of vegetation indices and hyperspectral data to determine the age of rodent mounds aims to provide a better method for extracting rodent hazard information. This experiment investigates and analyzes the age, distribution, and vegetation characteristics of rodent mounds, including total coverage, height, biomass, and diversity indices such as Patrick, Shannon-Wiener, and Pielou. Spectral data of rodent mounds of different ages were collected using an Analytical Spectral Devices field spectrometer. Correlation analysis was conducted between vegetation characteristics and spectral vegetation indices to select key indices, including NDVI670, NDVI705, EVI, TCARI, Ant, and SR. Multiple stepwise regression and Random Forest (RF) inversion models were established using vegetation indices, and the most suitable model was selected through comparison. Random Forest modeling was conducted to classify plateau zokor rat mounds of different ages, using both vegetation characteristic indicators and vegetation indices for comparison. The rodent mound classification models established using vegetation characteristic indicators and vegetation indices through Random Forest could distinguish rodent mounds of different ages, with out-of-bag error rates of 36.96% and 21.74%, respectively. The model using vegetation indices performed better. Conclusions: (1) Rodent mounds play a crucial ecological role in alpine meadow ecosystems by enhancing plant diversity, biomass, and the stability and vitality of the ecosystem. (2) The vegetation indices SR and TCARI are the most influential in classifying rodent mounds. (3) Incorporating vegetation indices into Random Forest modeling facilitates a precise and robust remote sensing interpretation of rodent mound ages, which is instrumental for devising targeted restoration strategies.

In China, ion-adsorbing rare earth minerals are mainly located in the southern hilly areas and are important strategic resources. Extensive long-term mining has severely damaged the land cover in mining areas, caused soil pollution and terrain fragmentation, disrupted the balance between mining and agriculture, severely restricted agricultural development, and affected ecological development. Precise and detailed classification of land use within mining areas is crucial for monitoring the sustainable development of agricultural ecology in these areas. In this study, we leverage the high spatial and high spectral resolution characteristics of the Zhuhai-1 (OHS) hyperspectral image datasets. We create four types of datasets based on spectral, vegetation, red edge, and texture characteristics. These datasets are optimized for multifaceted features, considering the complex land use scenario in rare earth mining areas. Additionally, we design seven optimal combination schemes for features. This is performed to examine the impact of different schemes on land use classification in rare earth mining areas and the accuracy of identifying agricultural land classes from broken blocks. The results show that (1) the inclusion of texture features has the most obvious effect on the overall classification accuracy; (2) the red edge feature has the worst effect on improving the overall accuracy of the surface classification; however, it has a prominent effect on the identification of agricultural lands such as farmland, orchards, and reclaimed vegetation; and (3), following the combination of various optimization features, the land use classification yielded the highest overall accuracy, at 88.16%. Furthermore, the comprehensive identification of various agricultural land classes, including farmland, orchards, and greenhouse vegetables, yielded the most desirable outcomes. The research results not only highlight the advantages of hyperspectral images for complex terrain classification and recognition but also address the previous limitations in the application of hyperspectral datasets over wide mining areas. Additionally, the results underscore the reliability of feature selection methods in reducing information redundancy and improving classification accuracy. The proposed feature selection combination, based on OHS hyperspectral datasets, offers technical support and guidance for the detailed classification of complex land use in mining areas and the accurate monitoring of agroecological environments.

The optical properties of snow can be strongly modified by the presence of a variety of impurities including mineral dust and snow algae. We made use of measured concentration of snow algae and mineral dust to parameterize the BioSNICAR radiative transfer model. Surficial snow samples were gathered during a field campaign on 7th July 2020 at the Presena glacier (Rhaetian Alps). We collected 18 samples of surface snow containing different amount of snow algae and mineral dust. Through radiative transfer simulations we estimated an average broadband albedo reduction of 7.4 +/- 6.1 % and 35.3 +/- 7.4 % compared to clean snow, caused by snow algae and mineral dust presence, respectively. When we considered the combined effect of snow algae and dust, we estimated a broadband albedo reduction equal to 40.8 +/- 8.4 %. We estimated an average instantaneous radiative forcing induced by snow algae, mineral dust and both impurities equals to 42.3 (+/- 36.1) W/m(2), 203.7 (+/- 45.5) W/m(2), and 211.8 (+/- 45.9) W/m(2), respectively. Using BioSNICAR simulations, we also tested a series of narrowband spectral indices to determine the concentration of mineral dust and snow algae from multi- and hyper -spectral data. Results showed that most spectral indices used for snow algae mapping are correlated also with mineral dust concentration. We found that only an index correlates uniquely with snow algae: the scaled band integral at 680 nm. A new spectral index, namely the Green Blue Normalized Index, is therefore proposed to discriminate mineral dust from snow algae when both impurities are present. The high spectral resolution of current (e.g. PRISMA, EnMAP) and future (e.g. CHIME, SBG) hyperspectral satellite missions will be fundamental to decouple the effect of mineral dust and snow algae on the optical properties of snow. In fact, from those data it is possible to calculate all narrowband indices presented in this study.