Accurately quantifying water inundation dynamics in terms of both spatial distributions and temporal variability is essential for water resources management. Currently, the water map is usually derived from synthetic aperture radar (SAR) data with the support of auxiliary datasets, using thresholding methods and followed by morphological operations to further refine the results. However, auxiliary datasets may lose efficacy on large plain areas, whilst the parameters of morphological operations are hard to be decided in different situations. Here, a heuristic and automatic water extraction (HAWE) method is proposed to extract the water map from Sentinel-1 SAR data. In the HAWE, we integrate tile-based thresholding and the active contour model, in which the former provides a convincing initial water map used as a heuristic input, and the latter refines the initial map by using image gradient information. The proposed approach was tested on the Dongting Lake plain (China) by comparing the extracted water map with the reference data derived from the Sentinel-2 dataset. For the two selected test sites, the overall accuracy of water classification is between 94.90% and 97.21% whilst the Kappa coefficient is within the range of 0.89 and 0.94. For the entire study area, the overall accuracy is between 94.32% and 96.7% and the Kappa coefficient ranges from 0.80 to 0.90. The results show that the proposed method is capable of extracting water inundations with satisfying accuracy.

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As a kind of soil-borne epidemic disease, bacterial wilt (BW) is one of the most serious diseases in tomatoes in southern China, which may significantly reduce food quality and the total amount of yield. Hyperspectral remote sensing can detect crop diseases in the early stages and offers potential for BW detection in tomatoes. Tomatoes in southern China are commonly cultivated in greenhouses or bird nets, limiting the application of remote sensing based on natural sunlight. To resolve these issues, we collected the spectrum of tomatoes firstly using the HS-VN1000B Portable Intelligent Spectrometer, which is equipped with a simulated solar light source. We then proposed a tomato BW detection model based on some optimal spectral features. Specifically, these optimal features, including vegetation indexes and principal components (PCs), were extracted by the sequential forward selection (SFS), the simulated annealing (SA), and the genetic algorithm (GA) and were finally fed into the support vector machine (SVM) classifier to detect diseased tomatoes. The results showed that the infected and healthy tomatoes exhibit different spectral characteristics for both leave and stem spectra, especially for near-infrared bands. In addition, the BW detecting model built by the combination of GA and SVM (GA-SVM) achieved the best performance with overall accuracies (OA) of 90.7% for leaves and 92.6% for stems. Compared with the results based on leaves, spectral features of stems provided better accuracy, indicating that the symptom of early infection of BW is more significant in tomato stems than in leaves. Further, the reliability of the GA-SVM tomato stem model was verified in our 2022 experiment with an OA of 88.6% and an F1 score of 0.80. Our study provides an effective means to detect BW disease of tomatoes in the early stages, which could help farmers manage their tomato production and effectively prevent pesticide abuse.

Hyperspectral image classification is an important topic for hyperspectral remote sensing with various applications. Hyperspectral image classification accuracy has been greatly improved with the introduction of deep neural networks, while the idea of transfer learning provides an opportunity to solve the problem even with the lack of training samples. In this paper, we propose an effective transfer learning approach for hyperspectral images, projecting hyperspectral images with different sensors and different number of bands into a general spectral space, preserving the relative positions of each band for spectral alignment, and designing a hierarchical depth neural network for shallow feature transfer and deep feature classification. The experiments show that the proposed method can effectively preserve the source domain features, especially for the scenarios with very few samples in the target domain, which can significantly improve the classification accuracy and reduce the risk of model overfitting. Meanwhile, this strategy greatly reduces the requirement of source domain data, using multi-sensor data to jointly train a more robust general feature model. The proposed method can achieve high accuracies even with few training samples compared to currently many state-of-the-art classification methods.

Extraterrestrial solar irradiance spectra detail the solar energy distribution over wavelengths, and numerous solar irradiance models are available within the remote sensing community. However, reference spectra may differ widely owing to differences in solar activity, measurement instruments and calibration. Six widely referenced solar spectra were selected in this work to examine their differences and the impacts of these differences on calculations of narrow band top-of-atmosphere reflectance using MERIS and Hyperion hyperspectral sensor spectral configurations. Mean solar exoatmospheric irradiance (MSEI) was computed using the different solar irradiance models and spectral response functions of the MERIS and Hyperion hyperspectral sensors. Then, the effects of MSEI on top-of-atmosphere (TOA) reflectance and the normalized difference vegetation index (NDVI) and atmospherically resistant vegetation index (ARVI) were investigated. The results show that the six selected solar irradiance models have significant differences from 350 to 2500 nm, which in turn result in differences in the MSEI derived from MERIS and Hyperion observations. These differences have a less significant effect on the TOA reflectance in the visible and near-infrared bands and on NDVI. However, the differences result in large differences in TOA reflectance in the infrared bands and in ARVI.