鲁莽
发表于 2025-3-27 00:47:23
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chemical-peel
发表于 2025-3-27 04:48:46
https://doi.org/10.1007/978-981-19-0280-2ence Water Index (MNDWI) was used to delineate water pixels from the multispectral satellite datasets, while the turbidity was assessed spatially for different river sections using widely used Turbidity retrieval algorithms. The relative consistency among the selected algorithms was evaluated using
Palatial
发表于 2025-3-27 05:56:19
https://doi.org/10.1007/978-3-030-98896-8s especially using band ratio methods, they need accurate spectral and specific absorption characteristics which are challenging to obtain for many inland water bodies. Machine learning algorithms, on the other hand can statistically derive the spatio-temporal distribution of chlorophyll-a and suspe
讨人喜欢
发表于 2025-3-27 11:39:59
Lahcene Makhloufi,Fateh Belaïd,Karima Zidanelgorithms are considered to be more powerful and known to have higher accuracy compared to traditional approaches. Specifically, time-series remotely sensed (both optical and SAR) data are used for crop mapping by capturing key phenological crop stages that differ for each crop type. In this study,
transient-pain
发表于 2025-3-27 16:53:26
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斥责
发表于 2025-3-27 20:05:30
https://doi.org/10.1007/978-3-030-56849-8 and temporal high-resolution data, in conjunction with the GPS-guided ground truth collection, was utilized in these studies. Hybrid semi-automatic classification techniques employing both digital classification and visual image interpretation were employed for spatial delineation of extent of hort
Dissonance
发表于 2025-3-28 00:59:24
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索赔
发表于 2025-3-28 04:48:20
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抑制
发表于 2025-3-28 08:24:12
Physical Fundamentals of Energy Utilization,olution) microwave data through Sentinel-1 Toolbox “SNAP”, the accuracy of crop map was assessed with geotagged ground truth. Pixel level estimates were developed and accumulated at the district level using an area-weighted approach. The estimated yield was compared with the actual estimate obtained
brother
发表于 2025-3-28 13:11:27
Daniel M. Kammen,Gregory F. Nemetanagement to assess the risk, simulation models, forecasting/early warning, monitoring, damage assessment, and prevention/planning during the aforementioned disasters. This chapter emphasizes the usage of geospatial technologies to manage desert locusts and dust storms.