This paper aims to examine the impact of SMAP derived soil moisture data on the crop yield prediction of the deep learning neural network models. In this study, the crop yield model based on Bayesian neural network (BNN) is used. Corn crop yield prediction experimental results with different type of SMAP Level 4 soil moisture data as inputs are compared to illustrate the impact of the soil moisture data on deep learning crop yield prediction models. The preliminary results show that either rootzone or surface, or rootzone plus surface soil moisture data can improve the BNN model crop yield prediction or produce comparable results though which type of soil moisture data (surface or root zoon) will help improve the prediction accuracy is uncertain. Soil moisture data and precipitation plus NWDI data can produce comparable yield prediction results due to high correlation between soil moisture data and precipitation along with NWDI.

Landslide is one of the most serious geological disasters around the world, and acquiring landslide inventory mapping (LIM) with remote sensed images (RSIs) plays an important role in disaster relief. However, various external imaging conditions of bitemporal RSIs usually cause pseudo-changes and challenges for achieving satisfied LIMs. In this article, a pioneering change magnitude measured distance named Spectral-Spatial Similarity based on Adaptive Region (S3AR) is proposed for achieving LIMs with bitemporal RSIs. First, an adaptive region is proposed to utilize the spatial-contextual information around each pixel, because the shapes and size of a landslide site are usually irregular and unpredictable. Then, a shape description algorithm is proposed for constructing a shape description vector, which aims at measuring the spatial difference of adaptive regions. Finally, to improve the separability between the landslide area and the background, brightness is suggested to couple with the shape description vector of an adaptive region to generate spatial-spectral similarity to measure the change magnitude between pairwise adaptive regions from the bitemporal RSIs. When the entire bitemporal RSIs are scanned and calculated via these steps, a change magnitude image between bitemporal RSIs can be generated, and then binary LIMs are obtained by a binary threshold. Experiments based on comparing eight state-of-the-art approaches demonstrated the feasibility and superiorities of the proposed S3AR for achieving LIMs with bitemporal RSIs. For example, the improvements on the four datasets are 5.81%, 14.06%, 6.03%, and 20.51% in terms of total error.

Crop mapping is a fundamental step for various higher level agricultural applications, such as crop yield prediction, farm management analysis, and agricultural market regulation. Recent advancements in deep learning models have greatly promoted crop mapping using satellite imagery time series (SITS), enabling frequent and extensive monitoring of croplands. However, a classifier trained on a specific year(s) with crop type labels (i.e., source domain) can exhibit reduced effectiveness when directly applied to a different year(s) without reference data (i.e., target domain) due to the interannual variation in image signals and crop growth dynamics. To address this issue, we propose an unsupervised domain adaptation (UDA) method named Bayesian joint adaptation network (BJAN), which aims to align the joint distributions of input SITS and output crop types across different years, thereby facilitating crop mapping in years without ground-truth samples. In the proposed BJAN method, Bayesian uncertainty is used to detect target data that are outside the support of the source domain. By minimizing the uncertainty on target samples, the model is trained to align the task-specific conditional distributions of source and target domains. Simultaneously, by constraining the feature distributions of source and target domains, the discrepancy of data-related marginal distributions is alleviated. Our experiments on two landcover classification datasets from the U.S. showed that BJAN has effectively aligned source and target domains and outperforms several state-of-the-art domain adaptation methods.

The field level soil moisture information is critical for crop irrigation, water resource and crop management, crop modeling, and crop yield estimation. USDA National Agricultural Statistics Services (NASS) uses SMAP derived soil moisture data for cropland soil moisture condition monitoring and assessment. However, spatial resolutions of Crop-CASMA soil moisture data are too coarse for field level assessment. Therefore, this paper proposes to use machine learning based high-resolution soil moisture (ML-HRSM) data for field level drought and irrigation monitoring. The preliminary study results show that the ML-HRSM data can accurately delineate field-level soil moisture variations at high spatial (30-m) and temporal (daily) resolutions. It can accurately capture field level soil moisture changes caused by the irrigation activities and vegetation ET processes. The irrigation activities are indirectly confirmed by weekly MODIS NDVI maps. The preliminary results indicate that the surface ML-HRSM is capable for irrigation activity and drought monitoring at field level.

Recently, with the advent of satellite missions and artificial intelligence techniques, supervised machine learning (ML) methods have been more and more used for analyzing remote sensing (RS) observation data for crop yield prediction. However, due to the domain shift between heterogeneous regions, supervised ML models tend to have poor spatial transferability. As a result, models trained with labeled data from one spatial region (i.e., source domain) often lose their validity when directly applied to another region (i.e., target domain). To address this issue, we proposed a multisource maximum predictor discrepancy (MMPD) neural network that is an unsupervised domain adaptation (UDA) approach for corn yield prediction at the county level. The novelties of this study include that: 1) we proposed to maximize the discrepancy between two source-specific yield predictors and align source and target domains by considering crop yield response in the target domain and 2) we adopted the strategy of multisource UDA to avoid negative interference between labeled samples from different sources. Case studies in the U.S. corn belt and Argentina demonstrated that the proposed MMPD model had effectively reduced domain shifts and outperformed several other state-of-the-art deep learning (DL) and UDA methods.

Alfalfa is a valuable and widely adapted forage crop, and its nutritive value directly affects animal performance and ultimately affects the profitability of livestock production. Traditional nutritive value measurement method is labor-intensive and time-consuming and thus hinders the determination of alfalfa nutritive values over large fields. The adoption of unmanned aerial vehicles (UAVs) facilitates the generation of images with high spatial and temporal resolutions for field-level agricultural research. Additionally, compared with other imaging modalities, hyperspectral data usually consist of hundreds of narrow spectral bands and allow the accurate detection, identification, and quantification of crop quality. Although various machine-learning methods have been developed for alfalfa quality prediction, they were all single-task models that learned independently for each quality trait and failed to utilize the underlying relatedness between each task. Inspired by the idea of multitask learning (MTL), this study aims to develop an approach that simultaneously predicts multiple quality traits. The algorithm first extracts shared information through a long short-term memory (LSTM)-based common hidden layer. To enhance the model flexibility, it is then divided into multiple branches, each containing the same or different number of task-specific fully connected hidden layers. Through comparison with multiple mainstream single-task machine-learning models, the effectiveness of the model is illustrated based on the measured alfalfa quality data and multitemporal UAV-based hyperspectral imagery.

Crop stand count plays an important role in modern agriculture as a reference for precision management and plant breeding. In this study, a new network—IntegrateNet—was proposed to supervise the learning of density map and local count simultaneously and thus boost the model performance by balancing the tradeoff between their errors. The IntegrateNet was trained and validated with an image set containing 124 maize images by an unmanned aerial vehicle. The model achieved an excellent result for 24 test images with the root-mean-square error of 2.28 and the coefficient of determination ($R^{2}$) of 0.9578 between the predicted and ground-truth maize stand counts. In conclusion, the proposed model provides an efficient solution for counting maize stands at early stages and could be used as a reference for similar studies.

Corn is the most widely grown crop in the U.S. and makes up a significant part of the American diet. Under the pressure of feeding a growing population, accurate and timely estimation of corn yield before the harvest is of great importance to supply chain management and regional food security. Recently, machine learning in conjunction with satellite remote sensing has been used for developing corn yield prediction models. Despite the success, a major bottleneck of training a reliable supervised machine learning model is the need for representative ground truth labels (e.g., yield records) which may be limited or even not available due to financial and manpower reasons. Also, due to domain shift, a machine learning model trained with labeled data from a label-rich region (i.e., source domain) could experience a significant performance decrease when directly applied to the region of interest (i.e., target domain). To address this issue, we proposed a Bayesian Domain Adversarial Neural Network (BDANN) for unsupervised domain adaptation on county-level corn yield prediction. By applying adversarial learning and Bayesian inference, BDANN was trained to reduce domain shift and accurately predict corn yield by extracting domain-invariant and task-informative features from both source and target domains. Moreover, the results also demonstrated that the BDANN model generalized well on small training sets. Experiments in two ecoregions in the U.S. corn belt have shown the effectiveness of the proposed BDANN and its superiority over other state-of-the-art methods.

Combining spectral and spatial features in hyperspectral image classification is a common practice due to the improvements in classification accuracy that can be obtained by extracting information from neighboring pixels. However, the resulting high dimensionality of the input data and the typically limited number of labeled samples are two key challenges that affect the overall performance of supervised classification methods. To alleviate these two issues, we propose an adaptive multiview (MV)-based active learning (AL) approach that is different from the existing MV AL methods in two main ways: 1) to improve the view sufficiency, a spectral–spatial view generation approach is proposed by incorporating spatial features derived from the segmentation maps into each view and 2) to increase the diversity across views, a dynamic view is generated at each AL iteration by selecting important features from the predefined views. The performance of each view is further improved by applying the proposed AL algorithm in conjunction with an ensemble approach as back-end classifier, a scenario less explored in the remote sensing community than single classifier-based AL methodologies. The proposed approach is applied to three widely analyzed hyperspectral data sets [i.e., Kennedy Space Center (KSC), Indian Pine, and University of Houston (UH)], and the results demonstrate the efficacy of the proposed method compared with other state-of-the-art AL classification methods.

Hyperspectral image classification via supervised approaches is often affected by the high dimensionality of the spectral signatures and the relative scarcity of training samples. Dimensionality reduction (DR) and active learning (AL) are two techniques that have been investigated independently to address these two problems. Considering the nonlinear property of the hyperspectral data and the necessity of applying AL adaptively, in this paper, we propose to integrate manifold and active learning into a unique framework to alleviate the aforementioned two issues simultaneously. In particular, supervised Isomap is adopted for DR for the training set, followed by an out-of-sample extension approach to project the large amount of unlabeled samples into previously learned embedding space. Finally, AL is performed in conjunction with k-nearest neighbor (kNN) classification in the embedded feature space. Experiments on a benchmark hyperspectral dataset illustrate the effectiveness of the proposed framework in terms of DR and the feature space refinement.

High throughput plant phenotyping has gained significant interest in the plant science community due to its potential impact in advancing the use of advanced plant genetics for problems ranging from global food security to biomass-based energy crops. While traditional collection of field-based phenotypes is manual, automated remote sensing-based methods can reduce the manual requirements, expand the number of sampled points, and accelerate associations with genotypes. In this preliminary work, we use multiple types of features derived from multi-temporal UAV-based hyperspectral and RGB image data for prediction of sorghum biomass. Considering the nonlinear properties of the spectral input features, multiple layer perception (MLP) neural networks and support vector regression (SVR) are explored for predicting dry biomass. The analysis is conducted on datasets acquired during June-August 2016 over an agricultural test field at the Agronomy Center for Research and Education (ACRE) at Purdue University.

Techniques that combine multiple types of features, such as spectral and spatial features, for hyperspectral image classification can often significantly improve the classification accuracy and produce a more reliable thematic map. However, the high dimensionality of the input data and the typically limited quantity of labeled samples are two key challenges that affect classification performance of supervised methods. In order to simultaneously deal with these issues, a regularized multimetric active learning (AL) framework is proposed which consists of three main parts. First, a regularized multimetric learning approach is proposed to jointly learn distinct metrics for different types of features. The regularizer incorporates the unlabeled data based on the neighborhood relationship, which helps avoid overfitting at early stages of AL, when the quantity of training data is particularly small. Then, as AL proceeds, the regularizer is also updated through similarity propagation, thus taking advantage of informative labeled samples. Finally, multiple features are projected into a common feature space, in which a new batch-mode AL strategy combining uncertainty and diversity is utilized in conjunction with k-nearest neighbor classification to enrich the set of labeled samples. In order to evaluate the effectiveness of the proposed framework, the experiments were conducted on two benchmark hyperspectral data sets, and the results were compared to those achieved by several other state-of-the-art AL methods.

Augmenting spectral features with spatial features for hyperspectral image classification can often improve the classification accuracy. However, the resulting high dimensionality and the typically scarce quantities of labeled samples impose significant challenges for supervised techniques. To alleviate these two issues simultaneously, in this paper, a semi-supervised multi-metric learning method is proposed for feature extraction and combined with active learning (AL) into a unique framework. In particular, the proposed metric learning approach learns distinct projection matrices jointly, and each metric is assigned to one type of feature. Moreover, the proposed regularizer helps avoid overfitting by taking advantage of the unlabeled data information. Finally, different types of features are projected into a common feature space in which AL is performed in conjunction with k-nearest neighbor (kNN) classification. Experiments on two benchmark hyperspectral datasets illustrate the effectiveness of the proposed framework compared to other state-of-the-art AL classification methods.

The classification of hyperspectral and multimodal remote sensing data is affected by two key problems: the high dimensionality of the input data and the limited number of the labeled samples. In this letter, a multimetric learning approach that combines feature extraction and active learning (AL) is introduced to deal with these two issues simultaneously. In particular, distinct metrics are assigned to different types of features and then learned jointly. In this way, multiple features are projected into a common feature space, in which AL is then performed in conjunction with k- nearest neighbor classification to enrich the set of labeled samples. Experiments on two sets of remote sensing data illustrate the effectiveness of the proposed framework in terms of both classification accuracy and computational requirements.

Augmenting spectral features with spatial features for hyperspectral image classification has recently gained significant attention, as classification accuracy can often be improved by extracting spatial features from neighboring pixels. However, the resulting high dimensional input data, which are often difficult and expensive to obtain, require large quantities of labeled data to train a robust supervised classifier. To alleviate the “curse of dimensionality”, we propose an ensemble based active learning approach that incorporates spatial features for each feature subset (view) independently. Specifically, in each view, the spatial features are extracted from an optimum segmentation selected from the hierarchical segmentation (HSeg). The proposed approach is applied to a benchmark hyperspectral data set, and the experimental results demonstrate the efficacy of the proposed method compared to other state-of-the-art active learning classification methods.