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ANN-based Rainfall-Runoff Model and Its Performance Evaluation of Sabarmati River Basin, Gujarat, India
ANN-based Rainfall-Runoff Model and Its Performance Evaluation of Sabarmati River Basin, Gujarat, India
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ANN-based Rainfall-Runoff Model and Its Performance Evaluation of Sabarmati River Basin, Gujarat, India

Verma, Rekha

Water conservation science and engineering, 2022-12, Vol.7 (4), p.525-532 [Periódico revisado por pares]

Singapore: Springer Nature Singapore

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Comparing Visible–Near‐Infrared Spectroscopy and a Pedotransfer Function for Predicting the Dry Region of the Soil‐Water Retention Curve
Comparing Visible–Near‐Infrared Spectroscopy and a Pedotransfer Function for Predicting the Dry Region of the Soil‐Water Retention Curve
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Comparing Visible–Near‐Infrared Spectroscopy and a Pedotransfer Function for Predicting the Dry Region of the Soil‐Water Retention Curve

Pittaki-Chrysodonta, Zampela ; Arthur, Emmanuel ; Moldrup, Per ; Knadel, Maria ; Norgaard, Trine ; Iversen, Bo V. ; Jonge, Lis Wollesen

Vadose zone journal, 2019, Vol.18 (1), p.1-13 [Periódico revisado por pares]

Madison: The Soil Science Society of America, Inc

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3
An Improved Flexible Spatiotemporal DAta Fusion (IFSDAF) method for producing high spatiotemporal resolution normalized difference vegetation index time series
An Improved Flexible Spatiotemporal DAta Fusion (IFSDAF) method for producing high spatiotemporal resolution normalized difference vegetation index time series
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An Improved Flexible Spatiotemporal DAta Fusion (IFSDAF) method for producing high spatiotemporal resolution normalized difference vegetation index time series

Liu, Meng ; Yang, Wei ; Zhu, Xiaolin ; Chen, Jin ; Chen, Xuehong ; Yang, Linqing ; Helmer, Eileen H.

Remote sensing of environment, 2019-06, Vol.227, p.74-89 [Periódico revisado por pares]

New York: Elsevier Inc

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Advantages of the mean absolute error (MAE) over the root mean square error (RMSE) in assessing average model performance
Advantages of the mean absolute error (MAE) over the root mean square error (RMSE) in assessing average model performance
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Advantages of the mean absolute error (MAE) over the root mean square error (RMSE) in assessing average model performance

Willmott, Cort J. ; Matsuura, Kenji

Climate research, 2005-12, Vol.30 (1), p.79-82 [Periódico revisado por pares]

Inter-Research

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5
Field performance of a low-cost sensor in the monitoring of particulate matter in Santiago, Chile
Field performance of a low-cost sensor in the monitoring of particulate matter in Santiago, Chile
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Field performance of a low-cost sensor in the monitoring of particulate matter in Santiago, Chile

Tagle, Matías ; Rojas, Francisca ; Reyes, Felipe ; Vásquez, Yeanice ; Hallgren, Fredrik ; Lindén, Jenny ; Kolev, Dimitar ; Watne, Ågot K. ; Oyola, Pedro

Environmental monitoring and assessment, 2020, Vol.192 (3), p.171, Article 171 [Periódico revisado por pares]

Cham: Springer International Publishing

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6
Forest inventories for small areas using drone imagery without in-situ field measurements
Forest inventories for small areas using drone imagery without in-situ field measurements
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Forest inventories for small areas using drone imagery without in-situ field measurements

Kotivuori, Eetu ; Kukkonen, Mikko ; Mehtätalo, Lauri ; Maltamo, Matti ; Korhonen, Lauri ; Packalen, Petteri

Remote sensing of environment, 2020-02, Vol.237, p.111404, Article 111404 [Periódico revisado por pares]

New York: Elsevier Inc

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7
Evaluation analysis of NASA SMAP L3 and L4 and SPoRT-LIS soil moisture data in the United States
Evaluation analysis of NASA SMAP L3 and L4 and SPoRT-LIS soil moisture data in the United States
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Evaluation analysis of NASA SMAP L3 and L4 and SPoRT-LIS soil moisture data in the United States

Tavakol, Ameneh ; Rahmani, Vahid ; Quiring, Steven M. ; Kumar, Sujay V.

Remote sensing of environment, 2019-08, Vol.229, p.234-246 [Periódico revisado por pares]

Goddard Space Flight Center: Elsevier Inc

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8
Prediction of meteorological drought and standardized precipitation index based on the random forest (RF), random tree (RT), and Gaussian process regression (GPR) models
Prediction of meteorological drought and standardized precipitation index based on the random forest (RF), random tree (RT), and Gaussian process regression (GPR) models
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Prediction of meteorological drought and standardized precipitation index based on the random forest (RF), random tree (RT), and Gaussian process regression (GPR) models

Elbeltagi, Ahmed ; Pande, Chaitanya B. ; Kumar, Manish ; Tolche, Abebe Debele ; Singh, Sudhir Kumar ; Kumar, Akshay ; Vishwakarma, Dinesh Kumar

Environmental science and pollution research international, 2023-03, Vol.30 (15), p.43183-43202 [Periódico revisado por pares]

Berlin/Heidelberg: Springer Berlin Heidelberg

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9
Gap Filling of High‐Resolution Soil Moisture for SMAP/Sentinel‐1: A Two‐Layer Machine Learning‐Based Framework
Gap Filling of High‐Resolution Soil Moisture for SMAP/Sentinel‐1: A Two‐Layer Machine Learning‐Based Framework
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Gap Filling of High‐Resolution Soil Moisture for SMAP/Sentinel‐1: A Two‐Layer Machine Learning‐Based Framework

Mao, Hanzi ; Kathuria, Dhruva ; Duffield, Nick ; Mohanty, Binayak P.

Water resources research, 2019-08, Vol.55 (8), p.6986-7009 [Periódico revisado por pares]

Washington: John Wiley & Sons, Inc

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10
Error Analysis and Correction of FENGYUN-4A GIIRS Temperature Profile Products in Summer over the Qinghai–Tibet Plateau
Error Analysis and Correction of FENGYUN-4A GIIRS Temperature Profile Products in Summer over the Qinghai–Tibet Plateau
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Error Analysis and Correction of FENGYUN-4A GIIRS Temperature Profile Products in Summer over the Qinghai–Tibet Plateau

Jiang, Xiaofei ; Wang, Qiguang ; Niu, Ning

Remote sensing (Basel, Switzerland), 2024-06, Vol.16 (11), p.1881 [Periódico revisado por pares]

Basel: MDPI AG

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