Article
Environmental Sciences
Jingyao Zheng, Tianjie Zhao, Haishen Lu, Defu Zou, Nemesio Rodriguez-Fernandez, Arnaud Mialon, Philippe Richaume, Jianshe Xiao, Jun Ma, Lei Fan, Peilin Song, Yonghua Zhu, Rui Li, Panpan Yao, Qingqing Yang, Shaojie Du, Zhen Wang, Zhiqing Peng, Yuyang Xiong, Zanpin Xing, Lin Zhao, Yann Kerr, Jiancheng Shi
Summary: Soil moisture and freeze/thaw (F/T) play a crucial role in water and heat exchanges at the land-atmosphere interface. This study reports the establishment of a wireless sensor network for soil moisture and temperature over the permafrost region of Tibetan Plateau. Satellite-based surface soil moisture (SSM) and F/T products were evaluated using ground-based measurements. The results show the reliability of L-band passive microwave SSM and F/T products, while existing F/T products display earlier freezing and later thawing, leading to unsatisfactory accuracy.
REMOTE SENSING OF ENVIRONMENT
(2024)
Article
Environmental Sciences
Jamie Tolan, Hung - Yang, Benjamin Nosarzewski, Guillaume Couairon, Huy V. Vo, John Brandt, Justine Spore, Sayantan Majumdar, Daniel Haziza, Janaki Vamaraju, Theo Moutakanni, Piotr Bojanowski, Tracy Johns, Brian White, Tobias Tiecke, Camille Couprie
Summary: Vegetation structure mapping is crucial for understanding the global carbon cycle and monitoring nature-based approaches to climate adaptation and mitigation. This study presents the first high-resolution canopy height maps for California and Sao Paulo, achieved through the use of very high resolution satellite imagery and aerial lidar data. The maps provide valuable tools for forest structure assessment and land use monitoring.
REMOTE SENSING OF ENVIRONMENT
(2024)
Article
Environmental Sciences
Regina Eckert, Steffen Mauceri, David R. Thompson, Jay E. Fahlen, Philip G. Brodrick
Summary: In this paper, a mathematical framework is proposed to improve the retrieval of surface reflectance and atmospheric parameters by leveraging the expected spatial smoothness of the atmosphere. Experimental results show that this framework can reduce the surface reflectance retrieval error and surface-related biases.
REMOTE SENSING OF ENVIRONMENT
(2024)
Article
Environmental Sciences
Oksana V. Lunina, Anton A. Gladkov, Alexey V. Bochalgin
Summary: In this study, an unmanned aerial vehicle (UAV) was used to detect and map surface discontinuities with displacements of a few centimeters, indicating the presence of initial geological deformations. The study found that sediments of alluvial fans are susceptible to various tectonic and exogenous deformational processes, and the interpretation of ultra-high resolution UAV images can help recognize low-amplitude brittle deformations at an early stage. UAV surveys are critical for discerning neotectonic activity and its related hazards over short observation periods.
REMOTE SENSING OF ENVIRONMENT
(2024)
Article
Environmental Sciences
Daniel L. Goldberg, Madankui Tao, Gaige Hunter Kerr, Siqi Ma, Daniel Q. Tong, Arlene M. Fiore, Angela F. Dickens, Zachariah E. Adelman, Susan C. Anenberg
Summary: A novel method is applied in this study to directly use satellite data to evaluate the spatial patterns of urban NOx emissions inventories. The results show that the 108 spatial surrogates used by NEMO are generally appropriate, but there may be underestimation in areas with dense intermodal facilities and overestimation in wealthy communities.
REMOTE SENSING OF ENVIRONMENT
(2024)
Article
Environmental Sciences
Stephen Stehman, John E. Wagner
Summary: This article investigates optimal sample allocation in stratified random sampling for estimation of accuracy and proportion of area in applications where the target class is rare. The study finds that precision of estimated accuracy has a stronger impact on sample allocation than estimation of proportion of area, and the trade-offs among these estimates become more pronounced as the target class becomes rarer. The results provide quantitative evidence to guide sample allocation decisions in specific applications.
REMOTE SENSING OF ENVIRONMENT
(2024)
Article
Environmental Sciences
Zhuoyue Hu, Xiaoyan Li, Liyuan Li, Xiaofeng Su, Lin Yang, Yong Zhang, Xingjian Hu, Chun Lin, Yujun Tang, Jian Hao, Xiaojin Sun, Fansheng Chen
Summary: This paper proposes a whisk-broom imaging method using a long-linear-array detector and high-precision scanning mirror to achieve high-resolution and wide-swath thermal infrared data. The method has been implemented in the SDGs satellite and has shown promising test results.
REMOTE SENSING OF ENVIRONMENT
(2024)
Article
Environmental Sciences
Dandan Wang, Leiqiu Hu, James A. Voogt, Yunhao Chen, Ji Zhou, Gaijing Chang, Jinling Quan, Wenfeng Zhan, Zhizhong Kang
Summary: This study evaluates different schemes for determining model coefficients to quantify and correct the anisotropic impact from remote sensing LST for urban applications. The schemes have consistent results and accurately estimate parameter values, facilitating the broadening of parametric models.
REMOTE SENSING OF ENVIRONMENT
(2024)
Article
Environmental Sciences
Feng Zhao, Weiwei Ma, Jun Zhao, Yiqing Guo, Mateen Tariq, Juan Li
Summary: This study presents a data-driven approach to reconstruct the terrestrial SIF spectrum using measurements from the TROPOMI instrument on Sentinel-5 precursor mission. The reconstructed SIF spectrum shows improved spatiotemporal distributions and demonstrates consistency with other datasets, indicating its potential for better understanding of the ecosystem function.
REMOTE SENSING OF ENVIRONMENT
(2024)
Article
Environmental Sciences
Chongya Jiang, Kaiyu Guan, Yizhi Huang, Maxwell Jong
Summary: This study presents the Field Rover method, which uses vehicle-mounted cameras to collect ground truth data on crop harvesting status. The machine learning approach and remote sensing technology are employed to upscale the results to a regional scale. The accuracy of the remote sensing method in predicting crop harvesting dates is validated through comparison with satellite data.
REMOTE SENSING OF ENVIRONMENT
(2024)
Article
Environmental Sciences
Ruonan Chen, Liangyun Liu, Xinjie Liu, Zhunqiao Liu, Lianhong Gu, Uwe Rascher
Summary: This study presents methods to accurately estimate sub-daily GPP from SIF in evergreen needleleaf forests and demonstrates that the interactions among light, canopy structure, and leaf physiology regulate the SIF-GPP relationship at the canopy scale.
REMOTE SENSING OF ENVIRONMENT
(2024)
Article
Environmental Sciences
Hao Tan, Xiangjian Xie, Junjun Sun, Yuqian Wang, Yuhong Jiang, Shuaishuai Huang
Summary: A multi-scale classification framework based on spectral-spatial features was proposed in this paper for monitoring red bed rocky desertification. Spectral indices and spatial features were used at pixel and patch scales, respectively, and validated using an OLI image in northwestern Jiangxi. The experimental results were satisfactory, providing a methodological supplement for monitoring red bed rocky desertification.
GEOCARTO INTERNATIONAL
(2023)
Article
Environmental Sciences
Khilola Amankulova, Nizom Farmonov, Uzbekkhon Mukhtorov, Laszlo Mucsi
Summary: In order to manage agricultural land and ensure food security, timely crop yield information is essential. This study explored the use of remote sensing data from Sentinel-2 to monitor sunflower crop phenology and predict crop yield at the field scale. Ten sunflower fields in Mezohegyes, southeastern Hungary, were studied in 2021, and Sentinel-2 images were collected throughout the monitoring period. Vegetation indices (VIs) were extracted to monitor crop growth. Multiple linear regression and two different machine learning approaches were used to predict crop yield, with random forest regression (RFR) showing the best performance. The study provides valuable insights for developing a robust and timely prediction method for sunflower crop yields to support decision-making regarding food security.
GEOCARTO INTERNATIONAL
(2023)
Article
Environmental Sciences
Kaushlendra Verma, J. Indu
Summary: The SWOT satellite mission, scheduled for launch in December 2022, is expected to effectively monitor freshwater resources. However, the infrequent temporal sampling of the SWOT orbit will lead to inconsistent estimation of river discharge. This study investigates the influence of unique temporal sampling on the calibration of a hydrological model, and suggests that using SWOT data for calibration can provide similar results to daily in-situ discharge measurements.
GEOCARTO INTERNATIONAL
(2023)
Article
Environmental Sciences
Projo Danoedoro, Prima Widayani, Iswari Nur Hidayati, Sanjiwana Arjasakusuma, Diwyacitta Dirda Gupita, Huwaida Nur Salsabila
Summary: This study compared the capabilities of forest cover density (FCD) transformation and random decision forest (RDF) classification in complex tropical landscapes. Landsat-8 OLI imagery was used for vegetation structural composition mapping in Central Java, Indonesia. The FCD transformation achieved an accuracy of 69.32%, while the RDF classification achieved accuracies ranging from 70.76% to 75.19% depending on the parameter setting.
GEOCARTO INTERNATIONAL
(2023)
Article
Environmental Sciences
Trung Nguyen Nhu, Nam Bui Van, Kha Tran Van
Summary: This paper uses three-dimensional direct gravity inversion to determine the Moho and basement depths of Tuchinh-Vungmay basin offshore southeastern Vietnam. The Moho depth is predicted from the mantle residual gravity anomaly with lithosphere thermal gravity correction, while the basement depth is determined by enhancing the resolution of the basement topography through the downward continuation of the basement residual gravity anomaly. The depths of the Moho and basement surfaces are constrained by the power density spectrum of the residual gravity anomalies and oceanic bottom seismic data.
GEOCARTO INTERNATIONAL
(2023)
Article
Environmental Sciences
Ruiqi Yang, Chen Zheng, Leiguang Wang, Yili Zhao, Zhitao Fu, Qinling Dai
Summary: In this study, a dual-stream network MAE-BG was proposed, consisting of an edge detection (ED) branch and a smooth branch with boundary guidance (BG). The ED branch enhances weak edges and suppresses false responses caused by local texture, while the MAE networks extract multiscale edge information to complement detail loss. The segmentation results with improved boundaries are obtained by stacking the output of the ED and smooth branches. The proposed method achieves precise object boundary location and improved segmentation performance.
GEOCARTO INTERNATIONAL
(2023)
Article
Environmental Sciences
Divesh Pangtey, Hitendra Padalia, Rahul Bodh, Ishwari Datt Rai, Subrata Nandy
Summary: Global decline in biodiversity requires systematic monitoring. This study assessed the performance of Rao's Q index derived from multi-date Sentinel-2 NDVI in estimating tree diversity in seasonal tropical forests. The approach showed good correlation with tree diversity, especially during the leaf flushing period.
GEOCARTO INTERNATIONAL
(2023)
Article
Environmental Sciences
Muhammad Asif, Jamil Hasan Kazmi, Aqil Tariq, Na Zhao, Rufat Guluzade, Walid Soufan, Khalid F. Almutairi, Ayman El Sabagh, Muhammad Aslam
Summary: We used the CA-Markov integrated technique to study LULC changes in the Cholistan and Thal deserts in Punjab, Pakistan. The data from Landsat satellites were classified using the Random Forest methodology. The projection for 2038 showed increased urbanization and development in croplands and residential centers.
GEOCARTO INTERNATIONAL
(2023)
Article
Environmental Sciences
V. F. Sales, D. C. Zanotta, A. Marques Jr, G. Racolte, M. Muller, C. L. Cazarin, D. Ibanez, L. Gonzaga Jr, M. R. Veronez
Summary: Accurate characterization of carbonate rocks is essential for evaluating potential outcrops in the oil industry. Remote sensing instruments have played a key role in estimating mineral characteristics at various scales. However, existing mathematical expressions for converting radiance values to mineral information are often limited to specific regions and general solutions tend to yield poor results. This paper proposes an adaptive approach using OLI-Landsat 8 image data to estimate subpixel amounts of carbonate rocks. The method is derived from extensive spectral analysis and further optimized through a genetic algorithm, demonstrating its adaptability and superior performance compared to existing carbonate indices.
GEOCARTO INTERNATIONAL
(2023)
