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The current regional-scale process-based photosynthesis models use biome-specified values of maximum carboxylation rate at 25 °C ($V_{m25}$) in simulating ecosystem gross primary productivity (GPP). These models ignore the variations in $V_{m25}$ over time and space, resulting in substantial errors in regional estimates of cropland GPP. Thus, to resolve this problem, we used the ensemble Kalman filter (EnKF) to assimilate tower-based GPP from five maize flux sites into a process-based mode to obtain the “apparent” value of $V_{m25}$ and then modeled this parameter using machine learning (ML) algorithms. The results showed that $V_{m25}$ increased during the early growing season and then decreased after reaching a peak value in the middle of the growing season. The coefficient of determination ($R^{2}$) root mean square error (RMSE) for satellite-driven coupled photosynthesis and evapotranspiration simulator (SCOPES)-Crop with EnKF-derived varied $V_{m25}$ in simulating daily GPP across all site-days increased (decreased) by 0.17 (5.63 $\mu \text {mol}\,\text {m}^{-2}\,\text {s}^{-1}$) on average compared to that for the model with fixed $V_{m25}$. We used four ML algorithms, namely artificial neural network, random forest, extreme gradient enhancement, and convolutional neural network (CNN), to model the $V_{m25}$ of maize. The CNN algorithm yielded the best results. The average of the $R^{2}$ (RMSE) values of simulated GPP using CNN-based $V_{m25}$ over the three flux sites is 0.93 (1.95 $\mu \text {mol}\,\text {m}^{-2}\,\text {s}^{-1}$), higher (smaller) than that using fixed $V_{m25}$. This study implies that representing the seasonal variations in $V_{m25}$ can facilitate improved estimates of GPP and the ML methods are useful tools for modeling the variation in $V_{m25}$. [ABSTRACT FROM AUTHOR]

Predicting wildfire spread is critical for land management and disaster preparedness. To this end, we present “Next Day Wildfire Spread,” a curated, large-scale, multivariate dataset of historical wildfires aggregating nearly a decade of remote-sensing data across the United States. In contrast to existing fire datasets based on Earth observation satellites, our dataset combines 2-D fire data with multiple explanatory variables (e.g., topography, vegetation, weather, drought index, and population density) aligned over 2-D regions, providing a feature-rich dataset for machine learning. To demonstrate the usefulness of this dataset, we implement a neural network that takes advantage of the spatial information of these data to predict wildfire spread. We compare the performance of the neural network with other machine learning models: logistic regression and random forest. This dataset can be used as a benchmark for developing wildfire propagation models based on remote-sensing data for a lead time of one day. [ABSTRACT FROM AUTHOR]

Estimating gross primary production (GPP), the gross uptake of CO2 by vegetation, is a fundamental prerequisite for understanding and quantifying the terrestrial carbon cycle. Over the last decade, multiple approaches have been developed to derive spatiotemporal dynamics of GPP combining in situ observations and remote sensing data using machine learning techniques or semiempirical models. However, no high spatial resolution GPP product exists so far that is derived entirely from satellite-based remote sensing data. Sentinel-2 satellites are expected to open new opportunities to analyze ecosystem processes with spectral bands chosen to study vegetation between 10- and 20-m spatial resolutions with five-day revisit frequency. Of particular relevance is the availability of red-edge bands that are suitable for deriving estimates of canopy chlorophyll content that are expected to be much better than any previous global mission. Here, we analyzed whether red-edge-based and near-infrared-based vegetation indices (VIs) or machine learning techniques that consider VIs, all spectral bands, and their nonlinear interactions could predict daily GPP derived from 58 eddy covariance sites. Using linear regressions based on classic VIs, including near-infrared reflectance of vegetation (NIRv), we achieved prediction powers of $R^{2}_{\mathrm{10-fold}} = 0.51$ and an $RMSE_{\mathrm{10-fold}} = 2.95 $ [ $\mu \rm {mol \ CO_{2} m^{-2}s^{-1}}$ ] in a 10-fold cross validation. Chlorophyll index red (CIR) and the novel kernel NDVI (kNVDI) achieved significantly higher prediction powers of around $R^{2}_{\mathrm{10-fold}} \approx 0.61$ and $RMSE_{\mathrm{10-fold}} \approx 2.57$ [ $\mu \rm {mol \ CO_{2} m^{-2}s^{-1}}$ ]. Using all spectral bands and VIs jointly in a machine learning prediction framework allowed us to predict GPP with $R^{2}_{\mathrm{10-fold}} = 0.71$ and $RMSE_{\mathrm{10-fold}} = 2.68$ [ $\mu \rm {mol \ CO_{2} m^{-2}s^{-1}}$ ]. Despite the high-power prediction when machine learning techniques are used, under water-stress scenarios or heat waves, optical information alone is not enough to predict GPP properly. In general, our analyses show the potential of nonlinear combinations of spectral bands and VIs for monitoring GPP across ecosystems at a level of accuracy comparable to previous works, which, however, required additional meteorological drivers. [ABSTRACT FROM AUTHOR]

Timely assessment of crop maturity contributes to optimized harvesting schedules while limiting food loss/waste at the farm level. Maturity assessments are typically performed via costly and time-consuming in situ methods. This study aimed to evaluate pod size crop maturity using imaging spectroscopy via unmanned aerial systems (UASs), as well as identifying discriminating wavelengths, using snap bean as a proxy crop. The research utilized a UAS-mounted hyperspectral imager in the visible-to-near-infrared region. Two years’ worth of data were collected at two different geographical locations for six different snap bean cultivars. Our approach consisted of calibration to reflectance, vegetation detection, noise reduction, creating classification bins, and feature selection. We used our previously published feature selection library, Jostar, and utilized ant colony optimization and simulated annealing to detect five spectral features and Plus-L Minus-R to identify one to ten features. We utilized decision trees and random forest classifiers for the classification task. Our findings show that, given the proper wavelengths, accurate pod maturity assessment is feasible for large-sieve cultivars (F1 score = 0.79–0.91), separating sieve sizes between ready-to-harvest and not ready-to-harvest pods. These spectral features were in the ~450, ~530, ~660, 700–720, ~740, and ~760 nm regions. This bodes well for the potential extension of results to an operational, multispectral sensor, tuned with the identified bands, thereby negating the need for a costly hyperspectral system. However, this proposition mandates further investigation, including data acquisition from geographical locations with variable climates, and quantifying noise for the hyperspectral imager to compare results with noisier datasets. [ABSTRACT FROM AUTHOR]

The solutions of vegetation height and the mapping of terrain in forest have always been concerning and are still challenging problems. The purpose of this article is to provide a new technical approach for the digital terrain model (DTM) and forest topographic survey by aerial photogrammetry, in consideration of the forest vegetation height modeling problem. Based on an aerial digital orthophoto map and a digital surface model (DSM), the spectral features and geometric features that are related to forest vegetation height are analyzed and extracted. The nonlinear correlation maximal information coefficient, maximum asymmetry score, and Pearson linear correlation coefficient between feature factors and vegetation height are listed, and the correlations are evaluated as the basis for factors selection. Two kinds of support vector regression algorithms were adopted to establish the machine learning for forest vegetation height model (VHM). Therefore, the DSM can be corrected to DTM. The experimental results show that the accuracy of the forest VHM is better than 1 m. Thus, the proposed method is proved to be feasible and practical. It provides a low-cost and high-efficiency method for the VHM and DTM in forest areas by photogrammetry. [ABSTRACT FROM AUTHOR]

Spectral measurements are commonly applied for the nondestructive estimation of leaf parameters, such as the concentrations of chlorophyll a and b, carotenoid, anthocyanin, brown pigment, leaf water content, and leaf mass per area for the quantification of vegetation physiology. The most popular way to estimate these parameters is by using spectral vegetation indices. The use of biochemical models allows us to use the full wavelength range (400–2500 nm) and to physically interpret the result. However, their performance is usually lower than that of supervised machine learning regression techniques. Machine learning regression techniques, on the other hand, have the disadvantage that the relationship between estimated parameters and the reflectance/transmission spectra is unclear. In this article, a hybrid between a supervised learning method and physical modeling for the estimation of leaf parameters is proposed. In this method, a machine learning regression technique is applied to learn a mapping from the true hyperspectral data set to a data set that follows the PROSPECT model. The PROSPECT model then reveals the actual leaf parameters. Two mapping methods, based on Gaussian processes (GPs) and kernel ridge regression (KRR) are proposed. As an alternative, mapping onto the leaf absorption spectra is proposed as well. The proposed methodology not only estimates the leaf parameters with a lower error but also solves the interpretation problem of the parameters estimated by the advanced machine learning regression techniques. This method is validated on the ANGERS and LOPEX data set. [ABSTRACT FROM AUTHOR]

This paper presents spiking neural networks (SNNs) for remote sensing spatiotemporal analysis of image time series, which make use of the highly parallel and low-power-consuming neuromorphic hardware platforms possible. This paper illustrates this concept with the introduction of the first SNN computational model for crop yield estimation from normalized difference vegetation index image time series. It presents the development and testing of a methodological framework which utilizes the spatial accumulation of time series of Moderate Resolution Imaging Spectroradiometer 250-m resolution data and historical crop yield data to train an SNN to make timely prediction of crop yield. The research work also includes an analysis on the optimum number of features needed to optimize the results from our experimental data set. The proposed approach was applied to estimate the winter wheat (Triticum aestivum L.) yield in Shandong province, one of the main winter-wheat-growing regions of China. Our method was able to predict the yield around six weeks before harvest with a very high accuracy. Our methodology provided an average accuracy of 95.64%, with an average error of prediction of 0.236 t/ha and correlation coefficient of 0.801 based on a nine-feature model. [ABSTRACT FROM PUBLISHER]

The new generation of Earth observation sensors with high spatial resolution can provide detailed information for change detection. The widely used methods for high-resolution image change detection rely on textural/structural features. However, these spatial features always produce high-dimensional data space since they are related to a series of parameters, e.g., window sizes and directions. Machine learning methods are also commonly employed, but their performances are subject to the quantity and quality of the training samples, and hence, much effort should be made to collect the high-quality samples. To address these problems, in this study, a novel multiindex automatic change detection method is proposed for the high-resolution imagery. The notable advantages of the proposed model include the following: 1) Complicated urban scenes are represented by a set of low dimensional but semantic information indexes, replacing the high-dimensional but low-level features (e.g., textural and structural features), and 2) the change detection model is carried out automatically without using training samples since the information indexes can directly indicate the primitive urban classes. The multiindex representation refers to the enhanced vegetation index, the water index, and the recently developed morphological building index. Experiments were conducted on the multitemporal WorldView-2 images over Shenzhen City (south of China) and Kuala Lumpur (the capital of Malaysia), where promising results were achieved by the proposed method. Moreover, the traditional methods based on the state-of-the-art textural/morphological features were also implemented for the purpose of comparison, which further validates the advantages of our proposed model. [ABSTRACT FROM AUTHOR]

An elementary piece of our prior knowledge about images of the physical world is that they are spatially smooth, in the sense that neighboring pixels are more likely to belong to the same object (class) than to different ones. The smoothness assumption becomes more important as sensor resolutions keep increasing, both because the radiometric variability within classes increases and because remote sensing is employed in more heterogeneous areas (e.g., cities), where shadow and shading effects, a multitude of materials, etc., degrade the measurement data, and prior knowledge plays a greater role. This paper gives a systematic overview of image classification methods, which impose a smoothness prior on the labels. Both local filtering-type approaches and global random field models developed in other fields of image processing are reviewed, and two new methods are proposed. Then follows a detailed experimental comparison and analysis of the presented methods, using two different aerial data sets from urban areas with known ground truth. A main message of the paper is that when classifying data of high spatial resolution, smoothness greatly improves the accuracy of the result-in our experiments up to 33%. A further finding is that global random field models outperform local filtering methods and should be more widely adopted for remote sensing. Finally, the evaluation confirms that all methods already oversmooth when most effective, pointing out that there is a need to include more and more complex prior information into the classification process. [ABSTRACT FROM AUTHOR]