## 1 Introduction
[18] proposed a predictive modeling framework that integrates genetic, environmental, management, and phenotype data to predict crop performance across diverse scenarios. [PERSON] and [PERSON] [19] reviewed the advanced mathematical and statistical methods for predicting plant development performance using multiple traits, as well as the integration of experimental metadata within data schemas. Such traditional statistical models effectively estimate plant phenotype factors and water quality. Nevertheless, theoretical derivations among multi-source data still require in-depth studies. Theoretical inversion models can be significantly developed using multi-source data, in terms of the analyzed information, to reduce the uncertainty and error of the inversion results. [PERSON] et al. [20] used multi-source data fusion of near-surface spectral reflectance, vegetation index, and soil moisture to estimate the growth parameters of summer maize, such as leaf area index and chlorophyll content. [PERSON] et al. [21] proposed a data integration method that combines the time series monitoring of satellite-based synthetic aperture radar interferometry and leveling data to extract fine subsidence information. [PERSON] et al. [22] developed a multi-source, multi-scale, source-independent full waveform inversion method that uses both surface and borehole seismic data to invert the velocity distribution of the subsurface. Deep learning techniques are capable of integrating large multi-source data in crop growth and hydrodynamic models to develop in situ monitoring equipment to detect fast-changing phenomena, as they can extract complex features and patterns from remote sensing data, such as spectral, spatial, temporal, and contextual information. [PERSON] et al. [23] used a deep neural network (DNN), recursive neural network (RNN), and convolutional neural network (CNN) to classify crops based on remote sensing data, and achieved a higher accuracy than traditional methods. [PERSON] et al. [24] reviewed data fusion techniques that employ multi-source satellite data sets to monitor the hydrological, vegetation, and topographic characteristics of wetlands, which are important indicators of wetland health and function. [PERSON] et al. [25] presented object-based and pixel-based deep learning techniques to classify agricultural crops via unmanned aerial vehicle (UAV) imagery, showing that these can improve agricultural field management and productivity. In summary, the comprehensive monitoring of croplands and wetlands is has potential but is also a huge challenge. This Special Issue is a collection of reviews and original research articles related to space-, aerial-, and ground-based monitoring techniques, which are used to orient crops, wetlands, freshwater areas and their complex interactions, including both algorithms, theoretical models, applications, and hardware development. [PERSON] and [PERSON] contributed equally to the article. All authors have read and agreed to the published version of the manuscript. The authors acknowledge financial support from the 9 th Group Project of Key Disciplines in Henan Province--Mechanical design, Manufacturing and Mechatronics. The authors declare no conflict of interest.