Machine learning (ML) has proven to be a powerful tool for utilizing the rapidly increasing amounts of remote sensing data for environmental monitoring. Yet ML models often require a substantial amount of ground truth labels for training, and models trained using labeled data from one domain often demonstrate poor performance when directly applied to other domains. Transfer learning (TL) has emerged as a promising strategy to address domain shift and alleviate the need for labeled data. Here we provide the first systematic review of TL studies in environmental remote sensing. We start by defining the different forms of domain shift and then describe five commonly used TL techniques. We then present the results of a systematic search for peer-reviewed articles published between 2017 and 2022, which identified 1676 papers. Applications of TL in remote sensing have increased rapidly, with nearly 10 times more publications in 2022 than in 2017. Across seven categories of applications (land cover mapping, vegetation monitoring, soil property estimation, crop yield prediction, biodiversity monitoring, water resources management, and natural disaster management) we identify several recent successes of TL as well as some remaining research gaps. Finally, we highlight the need to organize benchmark datasets explicitly for TL in remote sensing for model evaluation. We also discuss potential research directions for TL studies in environmental remote sensing, such as realizing scale transfer, improving model interpretability, and leveraging foundation models for remote sensing tasks. 

Transfer learning in environmental remote sensing

Rapid determination of chlorophyll content is significant for evaluating cotton's nutritional and physiological status. Hyperspectral technology equipped with multivariate analysis methods has been widely used for chlorophyll content detection. However, the model developed on one batch or variety cannot produce the same effect for another due to variations, such as samples and measurement conditions. Considering that it is costly to establish models for each batch or variety, the feasibility of using spectral preprocessing combined with deep transfer learning for model transfer was explored. Seven different spectral preprocessing methods were discussed, and a self-designed convolutional neural network (CNN) was developed to build models and conduct transfer tasks by fine-tuning. The approach combined first-derivative (FD) and standard normal variate transformation (SNV) was chosen as the best pretreatment. For the dataset of the target domain, fine-tuned CNN based on spectra processed by FD + SNV outperformed conventional partial least squares (PLS) and squares-support vector machine regression (SVR). Although the performance of fine-tuned CNN with a smaller dataset was slightly lower, it was still better than conventional models and achieved satisfactory results. Ensemble preprocessing combined with deep transfer learning could be an effective approach to estimate the chlorophyll content between different cotton varieties, offering a new possibility for evaluating the nutritional status of cotton in the field.

https://downloads.spj.sciencemag.org/plantphenomics/2022/9813841.pdf

Spectral Preprocessing Combined with Deep Transfer Learning to Evaluate Chlorophyll Content in Cotton Leaves

Chlorophyll drives plant photosynthesis. Under stress conditions, leaf chlorophyll content changes dramatically, which could provide insight into plant photosynthesis and drought resistance. Compared to traditional methods of evaluating chlorophyll content, hyperspectral imaging is more efficient and accurate and benefits from being a nondestructive technique. However, the relationships between chlorophyll content and hyperspectral characteristics of wheat leaves with wide genetic diversity and different treatments have rarely been reported. In this study, using 335 wheat varieties, we analyzed the hyperspectral characteristics of flag leaves and the relationships thereof with SPAD values at the grain-filling stage under control and drought stress. The hyperspectral information of wheat flag leaves significantly differed between control and drought stress conditions in the 550–700 nm region. Hyperspectral reflectance at 549 nm (r = −0.64) and the first derivative at 735 nm (r = 0.68) exhibited the strongest correlations with SPAD values. Hyperspectral reflectance at 536, 596, and 674 nm, and the first derivatives bands at 756 and 778 nm, were useful for estimating SPAD values. The combination of spectrum and image characteristics (L*, a*, and b*) can improve the estimation accuracy of SPAD values (optimal performance of RFR, relative error, 7.35%; root mean square error, 4.439; R2, 0.61). The models established in this study are efficient for evaluating chlorophyll content and provide insight into photosynthesis and drought resistance. This study can provide a reference for high-throughput phenotypic analysis and genetic breeding of wheat and other crops.

/pdf/rapid-and-nondestructive-evaluation-of-wheat-chlorophyll-2bgy55uc.pdf

Rapid and Nondestructive Evaluation of Wheat Chlorophyll under Drought Stress Using Hyperspectral Imaging

Coffee is the world’s most traded tropical crop, accounting for most export profits, and is a significant source of income for the countries in which it is produced. To meet the needs of the coffee market worldwide, farmers need to increase and monitor coffee production and quality. Coffee leaf disease is a significant factor that decreases coffee quality and production. In this research study, we aim to accurately classify and detect the diseases in four major types of coffee leaf disease (phoma, miner, rust, and Cercospora) in images using deep learning (DL)-based architectures, which are the most powerful artificial intelligence (AI) techniques. Specifically, we present an ensemble approach for DL models using our proposed layer. In our proposed approach, we employ transfer learning and numerous pre-trained CNN networks to extract deep characteristics from images of the coffee plant leaf. Several DL architectures then accumulate the extracted deep features. The best three models that perform well in classification are chosen and concatenated to build an ensemble architecture that is then given into classifiers to determine the outcome. Additionally, a data pre-processing and augmentation method is applied to enhance the quality and increase the data sample’s quantity to improve the training of the proposed method. According to the evaluation in this study, among all DL models, the proposed ensemble architecture outperformed other state-of-the-art neural networks by achieving 97.31% validation. An ablation study is also conducted to perform a comparative analysis of DL models in different scenarios.

/pdf/deep-learning-ensemble-based-automated-and-high-performing-hnzwkuc9.pdf

Deep Learning Ensemble-Based Automated and High-Performing Recognition of Coffee Leaf Disease

The use of a fast and accurate unmanned aerial vehicle (UAV) digital camera platform to estimate leaf area index (LAI) of kiwifruit orchard is of great significance for growth, yield estimation, and field management. LAI, as an ideal parameter for estimating vegetation growth, plays a significant role in reflecting crop physiological process and ecosystem function. At present, LAI estimation mainly focuses on winter wheat, corn, soybean, and other food crops; in addition, LAI on forest research is also predominant, but there are few studies on the application of orchards such as kiwifruit. Concerning this study, high-resolution UAV images of three growth stages of kiwifruit orchard were acquired from May to July 2021. The extracted significantly correlated spectral and textural parameters were used to construct univariate and multivariate regression models with LAI measured for corresponding growth stages. The optimal model was selected for LAI estimation and mapping by comparing the stepwise regression (SWR) and random forest regression (RFR). Results showed the model combining texture features was superior to that only based on spectral indices for the prediction accuracy of the modeling set, with the R2 of 0.947 and 0.765, RMSE of 0.048 and 0.102, and nRMSE of 7.99% and 16.81%, respectively. Moreover, the RFR model (R2 = 0.972, RMSE = 0.035, nRMSE = 5.80%) exhibited the best accuracy in estimating LAI, followed by the SWR model (R2 = 0.765, RMSE = 0.102, nRMSE = 16.81%) and univariate linear regression model (R2 = 0.736, RMSE = 0.108, nRMSE = 17.84%). It was concluded that the estimation method based on UAV spectral parameters combined with texture features can provide an effective method for kiwifruit growth process monitoring. It is expected to provide scientific guidance and practical methods for the kiwifruit management in the field for low-cost UAV remote sensing technology to realize large area and high-quality monitoring of kiwifruit growth, thus providing a theoretical basis for kiwifruit growth investigation.

/pdf/combining-spectral-and-textural-information-from-uav-rgb-3ef4n5lq.pdf

Combining Spectral and Textural Information from UAV RGB Images for Leaf Area Index Monitoring in Kiwifruit Orchard

Transfer-learning-based approach for leaf chlorophyll content estimation of winter wheat from hyperspectral data

The invention relates to a method and device for monitoring the drought process in farmland. A method for staged evaluation on the farmland drought process by utilizing information including the soilmoisture, crop moisture content, and crop growth status and the like comprehensively is put forward by starting with the mechanism of drought occurrence and a monitoring device being capable of measuring the soil surface temperature difference, the crop canopy surface temperature difference, and the crop canopy NDVI simultaneously in field working is also provided. Information including the soil moisture, crop moisture content, and crop growth status and the like is obtained by corresponding models and thus storage and decision analysis are performed. According to the invention, with comprehensive consideration of two farmland drought important components of the soil and crop, the moisture content of the soil and plants and the growth status the plants are balanced respectively; and thus the agricultural drought process is evaluated comprehensively, so that early warning is performed based on the actual development stage of agricultural drought. Therefore, practical, effective, energy-saving and environment-friendly farmland drought-resistance operation is made.

Method and device for monitoring drought process in farmland

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Transfer-learning-based approach for leaf chlorophyll content estimation of winter wheat from hyperspectral data

Method and device for monitoring drought process in farmland