Article
Engineering, Civil
Wei Xue, Jonghan Ko
Summary: Reliable estimations of evapotranspiration in paddy rice ecosystems are crucial for regional hydrological processes and climate change. However, current satellite products do not have good correlations with global paddy rice ET observations. This research conducted sensitivity analyses and optimization strategies to address this issue. The introduction of a new algorithm, LTDG_PhenoS, improved the estimation accuracy by considering multiple factors and resulted in significant decreases in RMSE.
JOURNAL OF HYDROLOGY
(2022)
Article
Environmental Sciences
Jie Hu, Yunping Chen, Zhiwen Cai, Haodong Wei, Xinyu Zhang, Wei Zhou, Cong Wang, Liangzhi You, Baodong Xu
Summary: The study proposed a method called the feature selection and hierarchical classification (FSHC) method, to effectively identify paddy rice and its rotation types in South China. The FSHC method consists of three processes: cropping intensity mapping, feature selection, and decision tree model development. The results demonstrated that this method can accurately extract diverse paddy rice cropping patterns from fragmented croplands.
Article
Agronomy
Xiao-Peng Song, Haijun Li, Peter Potapov, Matthew C. Hansen
Summary: This study combines long-term satellite and climate data, municipality-level crop yield statistics, and machine learning models to map soybean yield in Brazil. The models achieved good performance with a high-resolution yield map for 2020, demonstrating their predictive capability for future operational yield mapping.
AGRICULTURAL AND FOREST METEOROLOGY
(2022)
Article
Multidisciplinary Sciences
Mirza Waleed, Muhammad Mubeen, Ashfaq Ahmad, Muhammad Habib-ur-Rahman, Asad Amin, Hafiz Umar Farid, Sajjad Hussain, Mazhar Ali, Saeed Ahmad Qaisrani, Wajid Nasim, Hafiz Muhammad Rashad Javeed, Nasir Masood, Tariq Aziz, Fatma Mansour, Ayman EL Sabagh
Summary: This research compares the performance of Sentinel-2, Landsat-8, and MODIS satellite data in rice crop classification. The results show that Sentinel-2 has the highest accuracy and efficiency, followed by Landsat-8 and MODIS. The study also finds that increasing spatial resolution improves rice mapping accuracy.
SCIENTIFIC REPORTS
(2022)
Article
Geography, Physical
Murali Krishna Gumma, Prasad S. Thenkabail, Pranay Panjala, Pardhasaradhi Teluguntla, Takashi Yamano, Ismail Mohammed
Summary: Cropland products play a significant role in assessing water and food security in South Asia. This study aimed to produce three distinct products that would be useful overall in the region, including assessing irrigated versus rainfed croplands, identifying major crop types, and evaluating cropping intensity. By utilizing remote sensing data and machine learning algorithms, the study successfully generated accurate cropland products.
GISCIENCE & REMOTE SENSING
(2022)
Article
Remote Sensing
Zhuting Tan, Zhengyu Tan, Juhua Luo, Hongtao Duan
Summary: This study proposes a new method for cotton sample selection and employs machine learning to effectively identify long time series cotton planting areas at a 30-meter resolution scale. The study uses Bortala and Shuanghe in Xinjiang, China as case studies to demonstrate the approach. The results show that the method can achieve high accuracy and reveal the spatiotemporal distribution characteristics of cotton planting areas.
GEO-SPATIAL INFORMATION SCIENCE
(2023)
Article
Environmental Sciences
Chang Fan, Jilin Yang, Guosong Zhao, Junhu Dai, Mengyao Zhu, Jinwei Dong, Ruoqi Liu, Geli Zhang
Summary: This study compared ground and satellite observations and found that the 30m Landsat/Sentinel-2 data was more consistent with ground observations in wetland vegetation phenology, indicating its advantage over the 500m MODIS data. The study also highlighted the complexity of wetland phenology and its role in global climate change.
Article
Environmental Sciences
Nguyen-Sy Toan, Do Hong Hanh, Nguyen Thi Dong Phuong, Phan Thi Thuy, Pham Duy Dong, Nguyen Thanh Gia, Le Duc Tam, Tran Thi Ngoc Thu, Do Thi Van Thanh, Kuan Shiong Khoo, Pau Loke Show
Summary: This study investigated the effects of rice straw burning on the carbohydrate content in paddy soil. The results showed that burning rice straw did not change soil organic carbon, but reduced the labile carbon pool.
Article
Environmental Sciences
Shize Chen, Linlin Zhang, Xinli Hu, Qingyan Meng, Jiangkang Qian, Jianfeng Gao
Summary: The study proposes a new long time-series spatiotemporal fusion model (LOTSFM) for land surface temperature data, which effectively improves the spatial and temporal resolution of remote sensing data. The model demonstrates stable accuracy performance and a fast fusion process, making it suitable for monitoring urban thermal environments and extreme thermal phenomena.
Article
Geochemistry & Geophysics
Jun Li, Yunfei Li, Runlin Cai, Lin He, Jin Chen, Antonio Plaza
Summary: This paper introduces a new linear regression-based spatiotemporal fusion strategy (LiSTF), which reconstructs MODIS-like images from finer spatial resolution images to reduce model uncertainty errors and better preserve spatial information.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2022)
Article
Biotechnology & Applied Microbiology
Nguyen-Sy Toan, Thi Dong Phuong Nguyen, Tran Thi Ngoc Thu, Duong Thi Lim, Pham Duy Dong, Nguyen Thanh Gia, Kuan Shiong Khoo, Kit Wayne Chew, Pau Loke Show
Summary: The study investigated the effects of rice straw and potential nitrogen fixing Bacillus subtilis on carbohydrate- and nitrogen mineralization in long-term rice paddy soil through anaerobic incubation. Results showed that decomposed carbohydrate ranged from 83-447 mg kg(-1) soil, with extracted carbohydrate not affected by rice straw application but significantly decreased with Bacillus sp. or rice straw-Bacillus sp. inoculation. Nitrogen mineralization increased with Bacillus sp. inoculation, while rice straw and combine treatments resulted in more nitrogen immobilization. Inoculation of Bacillus subtilis is recommended to enhance soil fertility and reduce nitrogen immobilization, with further research within rice plants needed to confirm these findings.
ENVIRONMENTAL TECHNOLOGY & INNOVATION
(2021)
Article
Geography, Physical
Lifu Zhang, Liaoran Gao, Changping Huang, Nan Wang, Sa Wang, Mingyuan Peng, Xia Zhang, Qingxi Tong
Summary: In this study, a new random forest classifier based on accumulated temperature and vegetation index was proposed to classify crops in different years without ground samples. Comparing different series, it was found that the classification performance based on the universal normalized vegetation index was the best, indicating good stability of this method for crop classification in different years.
INTERNATIONAL JOURNAL OF DIGITAL EARTH
(2022)
Article
Remote Sensing
Sajad Jamshidi, Shahrokh Zand-Parsa, Dev Niyogi
Summary: By measuring canopy temperature of Orange trees, this study determined the crop water stress index (CWSI) under different irrigation levels and strategies. The remotely-sensed CWSI demonstrated higher accuracy in assessing crop water status, particularly when using the approach that combined Sentinel-2 data.
INTERNATIONAL JOURNAL OF REMOTE SENSING
(2021)
Article
Environmental Sciences
Akhilesh Kumar, Manu Mehta
Summary: This study explores the applicability of a simple iterative approach for retrieving aerosol optical depth (AOD) over diverse land surface types. The results show a strong correspondence between retrieved AOD and actual measurements, indicating that the iterative approach can be adopted for most sensors.
ATMOSPHERIC ENVIRONMENT
(2023)
Article
Geochemistry & Geophysics
Edward H. Bair, Timbo Stillinger, Jeff Dozier
Summary: The study introduces a new spectral mixture analysis approach for retrieval of snow properties, offering accurate estimation of grain size and concentrations of light absorbing particles with low error rates. The open-source code allows for fast computation, suitable for any multispectral sensor.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2021)
Article
Ecology
Sean P. Kearney, Lauren M. Porensky, David J. Augustine, Justin D. Derner, Feng Gao
Summary: Adaptive management of large herbivores requires understanding the spatial-temporal fluctuations in forage biomass and quality. This study combined satellite data, field observations, and modeling to investigate the relationship between diet quality, phenological metrics, and performance of free-ranging cattle. The results show that diet quality, influenced by vegetation green-up and senescence, strongly affects cattle's mass gains.
ECOLOGICAL APPLICATIONS
(2022)
Article
Agronomy
Noa Ohana-Levi, Feng Gao, Kyle Knipper, William P. Kustas, Martha C. Anderson, Maria del Mar Alsina, Luis A. Sanchez, Arnon Karnieli
Summary: Management zones (MZs) generated using time-series clustering (TSC) can enable time-specific management in agricultural fields. This study applied TSC to daily remote sensing data in a California vineyard to delineate distinct MZs based on pixel-level temporal dynamics in different datasets. The findings show that LAI TSC achieved the best cluster separation, and there were significant differences in yield values among MZs for all TSC maps.
IRRIGATION SCIENCE
(2022)
Article
Agronomy
William P. Kustas, Hector Nieto, Omar Garcia-Tejera, Nicolas Bambach, Andrew J. McElrone, Feng Gao, Joseph G. Alfieri, Lawrence E. Hipps, John H. Prueger, Alfonso Torres-Rua, Martha C. Anderson, Kyle Knipper, Maria Mar Alsina, Lynn G. McKee, Einara Zahn, Elie Bou-Zeid, Nick Dokoozlian
Summary: Water conservation efforts are crucial for the sustainability of California's agricultural industry, especially for perennial crops such as vineyards and orchards. Remote sensing-based models, particularly the two-source energy balance model, can be used to map crop water use and achieve significant water savings. However, modifications are needed to accurately estimate evapotranspiration and partitioning between evaporation and transpiration, especially in irrigated croplands affected by advection of hot dry air masses.
IRRIGATION SCIENCE
(2022)
Article
Meteorology & Atmospheric Sciences
Rong Li, Danica Lombardozzi, Mingjie Shi, Christian Frankenberg, Nicholas C. Parazoo, Philipp Kohler, Koong Yi, Kaiyu Guan, Xi Yang
Summary: Recent advances in satellite observations of solar-induced chlorophyll fluorescence (SIF) provide a new opportunity to constrain the simulation of terrestrial gross primary productivity (GPP). In this study, leaf-level fluorescence yield was simulated by a parametric simplification of the SCOPE model, and an efficient and accurate method based on escape probability was developed to scale SIF from leaf-level to top-of-canopy. The simulated SIF agreed well with observations at specific sites and captured the spatial and seasonal patterns of satellite-observed SIF globally. Improving the representation of radiative transfer for SIF simulation allows for better comparisons between simulated and observed SIF, thus constraining GPP simulations.
JOURNAL OF ADVANCES IN MODELING EARTH SYSTEMS
(2022)
Editorial Material
Chemistry, Physical
Derek R. Vardon, Bryan J. Sherbacow, Kaiyu Guan, Joshua S. Heyne, Zia Abdullah
Correction
Agronomy
Ziqi Qin, Kaiyu Guan, Wang Zhou, Bin Peng, Maria B. Villamil, Zhenong Jin, Jinyun Tang, Robert Grant, Lowell Gentry, Andrew J. Margenot, German Bollero, Ziyi Li
FIELD CROPS RESEARCH
(2022)
Article
Soil Science
Eric Potash, Kaiyu Guan, Andrew Margenot, DoKyoung Lee, Evan DeLucia, Sheng Wang, Chunhwa Jang
Summary: This study compares different strategies for estimating deep SOC stocks in agricultural fields and finds that using readily available auxiliary information can significantly improve the accuracy of SOC estimation. The Sentinel-2 SOC index and the topographic wetness index are identified as the most important auxiliary information. The study recommends future research to implement Bayesian methods for simulated evaluation of SOC estimation strategies in more fields to test the generalizability of these findings.
Article
Agronomy
Alison Thieme, W. Dean Hively, Feng Gao, Jyoti Jennewein, Steven Mirsky, Alex Soroka, Jason Keppler, Dawn Bradley, Sergii Skakun, Gregory W. McCarty
Summary: The Maryland Department of Agriculture's Winter Cover Crop Program introduced a delayed termination incentive in 2019 to promote springtime biomass accumulation. Using satellite imagery and in situ measurements, the study found that delayed termination fields accumulated significantly more biomass, nitrogen, and carbon compared to fields with standard termination dates. The delayed termination incentive yielded substantial additional springtime biomass, C, and N accumulation across Maryland.
Article
Biodiversity Conservation
Jillian M. Deines, Kaiyu Guan, Bruno Lopez, Qu Zhou, Cambria S. White, Sheng Wang, David B. Lobell
Summary: Research suggests that the adoption of cover crops may reduce crop yields, particularly in fields with better soil quality, lower temperatures, and less rainfall. However, in order to achieve widespread adoption and associated benefits, it is necessary to improve cover crop management to minimize yield penalties.
GLOBAL CHANGE BIOLOGY
(2023)
Article
Environmental Sciences
Sheng Wang, Kaiyu Guan, Chenhui Zhang, Chongya Jiang, Qu Zhou, Kaiyuan Li, Ziqi Qin, Elizabeth A. Ainsworth, Jingrui He, Jun Wu, Dan Schaefer, Lowell E. Gentry, Andrew J. Margenot, Leo Herzberger
Summary: This study utilized airborne hyperspectral imaging techniques and developed new process-guided machine learning approaches (PGML) for accurate monitoring of cover crop traits. The results showed that PGML models, pretrained with synthetic data and fine-tuned with field data, achieved high accuracy in predicting cover crop aboveground biomass and nitrogen content. Airborne hyperspectral data with PGML can provide valuable information for sustainable agricultural management.
REMOTE SENSING OF ENVIRONMENT
(2023)
Article
Environmental Sciences
Rui Gao, Alfonso F. Torres-Rua, Hector Nieto, Einara Zahn, Lawrence Hipps, William P. Kustas, Maria Mar Alsina, Nicolas Bambach, Sebastian J. Castro, John H. Prueger, Joseph Alfieri, Lynn G. McKee, William A. White, Feng Gao, Andrew J. McElrone, Martha Anderson, Kyle Knipper, Calvin Coopmans, Ian Gowing, Nurit Agam, Luis Sanchez, Nick Dokoozlian
Summary: Evapotranspiration (ET) is a crucial factor in commercial grapevine production, and its partitioning allows for separate assessment of soil and vine water and energy fluxes. Small unmanned aerial systems (sUAS) coupled with the two-source energy balance (TSEB) model provide a method for ET estimation and partitioning in vineyards. This study explores the assessment of ET and its partitioning using ground-based information and high-resolution sUAS imagery.
Article
Environmental Sciences
Kyle Knipper, Martha Anderson, Nicolas Bambach, William Kustas, Feng Gao, Einara Zahn, Christopher Hain, Andrew McElrone, Oscar Rosario Belfiore, Sebastian Castro, Maria Mar Alsina, Sebastian Saa
Summary: Accurate estimation of evapotranspiration (ET) is crucial for water-limited cropping systems. In this study, two formulations of the atmosphere-land exchange inverse model (ALEXI) and associated flux disaggregation technique (DisALEXI), namely DisALEXI-PT and DisALEXI-PM, were evaluated for partitioned evaporation (E) and transpiration (T) in vineyards and orchards. The results showed that DisALEXI-PT overestimated E and slightly underestimated T, while DisALEXI-PM agreed better with partitioned fluxes, albeit overestimating T under certain conditions. The analysis suggested that DisALEXI-PM achieved convergence with ALEXI ET by adjusting E and T proportionally, while DisALEXI-PT convergence relied on adjustments to E. These findings have implications for improving modeling frameworks and water use efficiency in water-limited systems.
Article
Agronomy
Uvirkaa Akumaga, Feng Gao, Martha Anderson, Wayne P. P. Dulaney, Rasmus Houborg, Andrew Russ, W. Dean Hively
Summary: Crop models can be improved by incorporating remotely sensed and field observations as inputs, which helps in assessing crop growth and yield. This study used remote sensing and field observation data to estimate the growth and yield of soybean and maize, and the model performances were compared with actual data. Results showed that the model accurately simulated the phenology and yield of maize and soybean, consistent with other evaluation studies.
Article
Chemistry, Analytical
Simon Kraatz, Brian T. Lamb, W. Dean Hively, Jyoti S. Jennewein, Feng Gao, Michael H. Cosh, Paul Siqueira
Summary: A general limitation in land cover mapping accuracy assessment is the lack of ground truth data. This study compares the accuracy of an optical approach (CDL) and a radar-based approach (CA) for cropland mapping. The results show that the radar-based approach is more accurate than the optical approach.
Article
Environmental Sciences
Malgorzata Borchers, Daniela Thraen, Yaxuan Chi, Nicolaus Dahmen, Roland Dittmeyer, Tobias Dolch, Christian Dold, Johannes Foerster, Michael Herbst, Dominik Hess, Aram Kalhori, Ketil Koop-Jakobsen, Zhan Li, Nadine Mengis, Thorsten B. H. Reusch, Imke Rhoden, Torsten Sachs, Cornelia Schmidt-Hattenberger, Angela Stevenson, Terese Thoni, Jiajun Wu, Christopher Yeates
Summary: In its latest assessment report, the IPCC emphasizes the importance of carbon dioxide removal (CDR) to achieve net zero carbon dioxide or greenhouse gas emissions. The potential and feasibility of CDR measures depend on specific conditions, such as site characteristics and resource availability. This study selected 13 CDR concepts and estimated their CO2 removal potentials in 2050. Northern Germany appears to be a preferable area for deployment, but successful implementation requires further socio-economic analysis and policy incentives.
FRONTIERS IN CLIMATE
(2022)