Multi-Species Inference of Exotic Annual and Native Perennial Grasses in Rangelands of the Western United States Using Harmonized Landsat and Sentinel-2 Data

Soil and crop interaction analysis for yield prediction with satellite imagery and deep learning techniques for the coastal regions

Crop yield is a significant factor in world income and poverty alleviation as well as food production through agriculture. Conventional crop yield forecasting approaches that employ subjective estimates including farmers' perceptions are imprecise and contain high variability over large farming areas, particularly in areas where data is limited. The improvement of data capture techniques in the last few years especially from high-resolution sensors and Deep Learning (DL) have enhanced the quality and scope of agricultural data to assist policymakers and administrators. Mostly researchers used various techniques for independently forecasting soil fertility and crop yield. In image processing, Sentinel-2 is one technique that enhances agriculture, especially in analyzing crop health and type of soil prediction. Using the Normalized Difference Vegetation Index (NDVI) for processing the red and near-infrared bands allows computation ranges between -1 and 1. The values are higher than 0.7, the crops are in good health, or the values are less than 0.3 means crops are under stress. Therefore, information about soil types and NDVI data provide the most elaborate recommendations regarding agriculture. This is done through executing superior picture analysis and verification for precise errors below 5 %. It also develops a rainfall-runoff forecast through a Convolutional Neural Network approach. Our proposed methodology attains an average accuracy of about 98.7 % compared with traditional approaches average is about 85 %-90 %. A high-accuracy model of this type facilitates a spatial and temporal resolution of five days and improves farmers' irrigation process since it offers more accurate agronomic decisions. This research may lead in the agriculture and deep learning applications for economic and societal improvement. Application of artificial intelligence in agriculture synchronizes relevancy from satellite imagery making precision smart and boosting food productivity by 20 % with better utilization of resources.

Capacity and capability of remote sensing to inform invasive plant species management in the Pacific Islands region

The Pacific Islands region is home to several of the world's biodiversity hotspots, yet its unique flora and fauna are under threat because of biological invasions. These invasions are likely to proliferate as human activity increases and large-scale natural disturbances unfold, exacerbated by climate change. Remote sensing data and techniques provide a feasible method to map and monitor invasive plant species and inform invasive plant species management across the Pacific Islands region. We used case studies taken from literature retrieved from Google Scholar, 3 regional agencies' digital libraries, and 2 online catalogs on invasive plant species management to examine the uptake and challenges faced in the implementation of remote sensing technology in the Pacific region. We synthesized remote sensing techniques and outlined their potential to detect and map invasive plant species based on species phenology, structural characteristics, and image texture algorithms. The application of remote sensing methods to detect invasive plant species was heavily reliant on species ecology, extent of invasion, and available geospatial and remotely sensed image data. However, current mechanisms that support invasive plant species management, including policy frameworks and geospatial data infrastructure, operated in isolation, leading to duplication of efforts and creating unsustainable solutions for the region. For remote sensing to support invasive plant species management in the region, key stakeholders including conservation managers, researchers, and practitioners; funding agencies; and regional organizations must invest, where possible, in the broader geospatial and environmental sector, integrate, and streamline policies and improve capacity and technology access.

Hierarchical Classification of Soybean in the Brazilian Savanna Based on Harmonized Landsat Sentinel Data

The Brazilian Savanna presents a complex agricultural dynamic and cloud cover issues; therefore, there is a need for new strategies for more detailed agricultural monitoring. Using a hierarchical classification system, we explored the Harmonized Landsat Sentinel-2 (HLS) dataset to detect soybean in western Bahia, Brazil. Multispectral bands (MS) and vegetation indices (VIs) from October 2021 to March 2022 were used as variables to feed Random Forest models, and the performances of the complete HLS time-series, HLSS30 (harmonized Sentinel), HLSL30 (harmonized Landsat), and Landsat 8 OLI (L8) were compared. At Level 1 (agricultural areas × native vegetation), HLS, HLSS30, and L8 produced identical models using MS + VIs, with 0.959 overall accuracies (OA) and Kappa of 0.917. At Level 2 (annual crops × perennial crops × pasturelands), HLS and L8 achieved an OA of 0.935 and Kappa > 0.89 using only VIs. At Level 3 (soybean × other annual crops), the HLS MS + VIs model achieved the best performance, with OA of 0.913 and Kappa of 0.808. Our results demonstrated the potential of the new HLS dataset for medium-resolution mapping initiatives at the crop level, which can impact decision-making processes involving large-scale soybean production and agricultural sustainability.