Global food security and nutrition is suffering from unprecedented challenges. To reach a world without hunger and malnutrition by implementing precision agriculture, satellite remote sensing plays an increasingly important role in field crop monitoring and management. Alfalfa, a global widely distributed forage crop, requires more attention to predict its yield and quality traits from satellite data since it supports the livestock industry. Meanwhile, there are some key issues that remain unknown regarding alfalfa remote sensing from optical and synthetic aperture radar (SAR) data. Using Sentinel-1 and Sentinel-2 satellite data, this study developed, compared, and further integrated new optical- and SAR-based satellite models for improving alfalfa yield and quality traits prediction, i.e., crude protein (CP), acid detergent fiber (ADF), neutral detergent fiber (NDF), and neutral detergent fiber digestibility (NDFD). Meanwhile, to better understand the physical mechanism of alfalfa optical remote sensing, a unified hybrid leaf area index (LAI) retrieval scheme was developed by coupling the PROSAIL radiative transfer model, spectral response function of the desired optical satellite, and a random forest (RF) model, denoted as a scalable optical satellite-based LAI retrieval framework. Compared to optical vegetation indices (VIs) that only capture canopy information, the results indicate that LAI had the highest correlation (r = 0.701) with alfalfa yield due to its capacity in delivering the vegetation structure characteristics. For alfalfa quality traits, optical chlorophyll VIs presented higher correlations than LAI. On the other hand, LAI did not provide a significant additional contribution for predicting alfalfa parameters in the RF developed optical prediction model using VIs as inputs. In addition, the optical-based model outperformed the SAR-based model for predicting alfalfa yield, CP, and NDFD, while the SAR-based model showed better performance for predicting ADF and NDF. The integration of optical and SAR data contributed to higher accuracy than either optical or SAR data separately. Compared to a traditional embedded integration approach, the combination of multisource heterogeneous optical and SAR satellites was optimized by multiple linear regression (yield: R² = 0.846 and RMSE = 0.0354 kg/m²; CP: R² = 0.636 and RMSE = 1.57%; ADF: R² = 0.559 and RMSE = 1.926%; NDF: R² = 0.58 and RMSE = 2.097%; NDFD: R² = 0.679 and RMSE = 2.426%). Overall, this study provides new insights into forage crop yield prediction for large-scale fields using multisource heterogeneous satellites.

Potato holds significant importance as a staple food crop worldwide, particularly in addressing the needs of a growing population. Accurate estimation of the potato Leaf Area Index (LAI) plays a crucial role in predicting crop yield and facilitating precise management practices. Leveraging the capabilities of UAV platforms, we harnessed their efficiency in capturing multi-source, high-resolution remote sensing data. Our study focused on estimating potato LAI utilizing UAV-based digital red–green–blue (RGB) images, Light Detection and Ranging (LiDAR) points, and hyperspectral images (HSI). From these data sources, we computed four sets of indices and employed them as inputs for four different machine-learning regression models: Support Vector Regression (SVR), Random Forest Regression (RFR), Histogram-based Gradient Boosting Regression Tree (HGBR), and Partial Least-Squares Regression (PLSR). We assessed the accuracy of individual features as well as various combinations of feature levels. Among the three sensors, HSI exhibited the most promising results due to its rich spectral information, surpassing the performance of LiDAR and RGB. Notably, the fusion of multiple features outperformed any single component, with the combination of all features of all sensors achieving the highest R² value of 0.782. HSI, especially when utilized in calculating vegetation indices, emerged as the most critical feature in the combination experiments. LiDAR played a relatively smaller role in potato LAI estimation compared to HSI and RGB. Additionally, we discovered that the RFR excelled at effectively integrating features.

Building footprint is a key indicator of urban structures and economic development. Automatic extraction of building footprint from very-high-resolution (VHR) remote sensing imagery, which is of great practical interest for various geospatial-related applications, is still a challenging task for complex textures, varying scales and shapes, and other confusing artificial objects. To alleviate these problems and improve the extraction accuracy, this study proposed a novel pure convolutional neural network called ConvBNet, which integrates ConvNeXt-XL with a fusing decoder and adopts deep supervision in the training stage for the middle stage. Two-stage training strategy, using weighted cross-entropy (CE) loss and mask CE loss, respectively, ensures the stable convergence and makes the network focus on the boundary region. The proposed model was tested on the Wuhan University (WHU) building dataset (publicly available) and one private Zhejiang building dataset. Compared with other state-of-the-art (SOTA) methods, ConvBNet achieved the best intersection over Union (IoU), 91.22% and 77.90% for the two datasets, which proves its good performance in the task of extracting buildings from VHR images.

Tropical forests are of vital importance for maintaining biodiversity, regulating climate and material cycles while facing deforestation, agricultural reclamation, and managing various pressures. Remote sensing (RS) can support effective monitoring and mapping approaches for tropical forests, and to facilitate this we propose a deep neural network with an encoder–decoder architecture here to classify tropical forests and their environment. To deal with the complexity of tropical landscapes, this method utilizes a multi-scale convolution neural network (CNN) to expand the receptive field and extract multi-scale features. The model refines the features with several attention modules and fuses them through an upsampling module. A two-stage training strategy is proposed to alleviate misclassifications caused by sample imbalances. A joint loss function based on cross-entropy loss and the generalized Dice loss is applied in the first stage, and the second stage used the focal loss to fine-tune the weights. As a case study, we use Hainan tropical reserves to test the performance of this model. Compared with four state-of-the-art (SOTA) semantic segmentation networks, our network achieves the best performance with two Hainan datasets (mean intersection over union (MIoU) percentages of 85.78% and 82.85%). We also apply the new model to classify a public true color dataset which has 17 semantic classes and obtain results with an 83.75% MIoU. This further demonstrates the applicability and potential of this model in complex classification tasks.

Alfalfa is one of the most widely cultivated perennial legume crops used as feedstock for animals. Efficiently estimating alfalfa yield and quality traits before harvesting is critical for the decision-making process regarding precision management activities and harvesting time to ensure high profitability. Satellite-based radar is a powerful tool in remote sensing for crop monitoring because it provides high-quality data regardless of weather conditions. Therefore, this study aims to investigate the potential use of satellite radar features and environmental factors in estimating alfalfa yield and quality. Alfalfa yield and quality traits, including dry matter yield (DMY), crude protein (CP), neutral detergent fiber (NDF), NDF digestibility (NDFD), and acid detergent fiber (ADF), were collected over 16 alfalfa fields from 2016 to 2021, leading to 126 samples in total. Sentinel-1 radar backscattering coefficients and environmental factors were collected for all the fields across all growing seasons. Five commonly used machine learning models were established to estimate each alfalfa trait separately. The results show that the Extreme Gradient Boosting model consistently performed the best for all alfalfa traits. The accuracy of the DMY estimates is acceptable, with an average R² of 0.67 and an RMSE of 0.68 tons/ha. The best result for estimating CP was an average R² of 0.70 and an RMSE of 1.63% DM. In estimating alfalfa fiber indicators (i.e., ADF, NDF, and NDFD), we achieved the highest average R² values of 0.54, 0.62, and 0.56, respectively. Overall, this study demonstrated the potential use of environmental factors for alfalfa yield and quality estimation in-field before harvesting. However, the Sentinel-1 radar backscattering coefficients did not make significant contributions to improving the estimation performance, compared to the environmental factors.