A unified, fast, and extensible framework for calculating spectral and geospatial indices from multispectral raster datasets (e.g., NDVI - Normalized Difference Vegetation Index, NDWI - Normalized Difference Water Index, MNDWI - Modified Normalized Difference Water Index, NDBI - Normalized Difference Built-up Index, SAVI - Soil Adjusted Vegetation Index, EVI - Enhanced Vegetation Index, GNDVI - Green Normalized Difference Vegetation Index, NDMI - Normalized Difference Moisture Index, BSI - Bare Soil Index). Designed around 'terra' 'SpatRaster' objects, it supports multi-band rasters, automatic band resolution, sensor presets (Sentinel-2, Landsat-8/9), customizable index parameters, vectorized computations, and comprehensive validation. Methods based on Rouse et al. (1973) , McFeeters (1996) &lt;<a href="https://doi.org/10.1080%2F01431169608948714" target="_top">doi:10.1080/01431169608948714</a>&gt;, Xu (2006) &lt;<a href="https://doi.org/10.1080%2F01431160600589179" target="_top">doi:10.1080/01431160600589179</a>&gt;, Zha et al. (2003) &lt;<a href="https://doi.org/10.1016%2FS0034-4257%2803%2900087-7" target="_top">doi:10.1016/S0034-4257(03)00087-7</a>&gt;, Huete (1988) &lt;<a href="https://doi.org/10.1016%2F0034-4257%2888%2990106-X" target="_top">doi:10.1016/0034-4257(88)90106-X</a>&gt;, Gitelson et al. (1996) &lt;<a href="https://doi.org/10.1016%2FS0034-4257%2896%2900072-7" target="_top">doi:10.1016/S0034-4257(96)00072-7</a>&gt;, Wilson and Sader (2002) &lt;<a href="https://doi.org/10.1016%2FS0034-4257%2801%2900318-2" target="_top">doi:10.1016/S0034-4257(01)00318-2</a>&gt;, and Rikimaru et al. (2002).

A unified, fast, and extensible framework for calculating spectral and geospatial indices from multispectral raster datasets (e.g., NDVI - Normalized Difference Vegetation Index, NDWI - Normalized Difference Water Index, MNDWI - Modified Normalized Difference Water Index, NDBI - Normalized Difference Built-up Index, SAVI - Soil Adjusted Vegetation Index, EVI - Enhanced Vegetation Index, GNDVI - Green Normalized Difference Vegetation Index, NDMI - Normalized Difference Moisture Index, BSI - Bare Soil Index). Designed around 'terra' 'SpatRaster' objects, it supports multi-band rasters, automatic band resolution, sensor presets (Sentinel-2, Landsat-8/9), customizable index parameters, vectorized computations, and comprehensive validation. Methods based on Rouse et al. (1973) , McFeeters (1996) &lt;<a href="https://doi.org/10.1080%2F01431169608948714" target="_top">doi:10.1080/01431169608948714</a>&gt;, Xu (2006) &lt;<a href="https://doi.org/10.1080%2F01431160600589179" target="_top">doi:10.1080/01431160600589179</a>&gt;, Zha et al. (2003) &lt;<a href="https://doi.org/10.1016%2FS0034-4257%2803%2900087-7" target="_top">doi:10.1016/S0034-4257(03)00087-7</a>&gt;, Huete (1988) &lt;<a href="https://doi.org/10.1016%2F0034-4257%2888%2990106-X" target="_top">doi:10.1016/0034-4257(88)90106-X</a>&gt;, Gitelson et al. (1996) &lt;<a href="https://doi.org/10.1016%2FS0034-4257%2896%2900072-7" target="_top">doi:10.1016/S0034-4257(96)00072-7</a>&gt;, Wilson and Sader (2002) &lt;<a href="https://doi.org/10.1016%2FS0034-4257%2801%2900318-2" target="_top">doi:10.1016/S0034-4257(01)00318-2</a>&gt;, and Rikimaru et al. (2002).

I want to get a band mean across images from NDMI data but I don't find the correct pattern or the right script to print it in the console. How can I do this? My script here: // CALCULO DE INDICES DE HUMEDAD DE DIFERENCIA NORMALIZADA EN EARTH ENGINE // Coleccion Sentinel 2 nivel 2 var ColeccionSentinel = ee.ImageCollection("COPERNICUS/S2_SR") .filterDate ('2022-06-01' ,'2022-06-30') //Definir fecha .filterBounds (geometry) //Definir geometria .filterMetadata ('CLOUDY_PIXEL_PERCENTAGE', 'Less_Than', 20); var Moisture = ee.Image(ColeccionSentinel.mean()); // NDMI (Normalized Difference Moisture Index) var NDMI = Moisture.expression ('float ((NIR - SWIR) / (NIR + SWIR))',{ 'NIR': Moisture.select ('B8'), 'SWIR': Moisture.select ('B11')}); // Simbologia comun para los indices var Simbologia = {max: 1.0, min: -0.5, 'palette': ['1A3678', '2955BC', '5699FF','8DBAE9', 'ACD1FF', 'CAEBFF', 'E5F9FF', 'FDFFB4', 'FFE6A2', 'FFC969', 'FFA12D', 'FF7C1F', 'CA531A', 'FF0000', 'AB0000']}; Map.centerObject (geometry, 15); //Ajustar zoom // Representacion de indice Map.addLayer (NDMI, Simbologia, 'NDMI',1);

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I want to get a band mean across images from NDMI data but I don't find the correct pattern or the right script to print it in the console. How can I do this? My script here: // CALCULO DE INDICES DE HUMEDAD DE DIFERENCIA NORMALIZADA EN EARTH ENGINE // Coleccion Sentinel 2 nivel 2 var ColeccionSentinel = ee.ImageCollection("COPERNICUS/S2_SR") .filterDate ('2022-06-01' ,'2022-06-30') //Definir fecha .filterBounds (geometry) //Definir geometria .filterMetadata ('CLOUDY_PIXEL_PERCENTAGE', 'Less_Than', 20); var Moisture = ee.Image(ColeccionSentinel.mean()); // NDMI (Normalized Difference Moisture Index) var NDMI = Moisture.expression ('float ((NIR - SWIR) / (NIR + SWIR))',{ 'NIR': Moisture.select ('B8'), 'SWIR': Moisture.select ('B11')}); // Simbologia comun para los indices var Simbologia = {max: 1.0, min: -0.5, 'palette': ['1A3678', '2955BC', '5699FF','8DBAE9', 'ACD1FF', 'CAEBFF', 'E5F9FF', 'FDFFB4', 'FFE6A2', 'FFC969', 'FFA12D', 'FF7C1F', 'CA531A', 'FF0000', 'AB0000']}; Map.centerObject (geometry, 15); //Ajustar zoom // Representacion de indice Map.addLayer (NDMI, Simbologia, 'NDMI',1);

I work with Qgis 3.4. I got several multi date optical landsat 5 images over an area of interest. Sometimes images are full cloud free over the aera. For others dates, some pixels are valid and ohers are 'not valid' (shadow, cloud) : there is need to manage no data value. From those images stacks, i perform several spectral index :NDVI, SAVI, NDWI, NDMI (via the raster calculator). And, i perform plots via the usefull plugin temporal/spectral profile tool. How to ignore no data values (cloud/shadow pixels) in order there are not take into consideration for the spectral index generation and/or for the ploting (zonal stats like average, median...) ?

Currently, i work with Qgis over an area in Europe, I am using a Landsat 5 time series from the 1980s to 2011. I am generating various indicators for vegetation (NDVI), water content in vegetation (NDMI), and the presence of open or even subsurface water. These indicators often have values ​​between -1 and 1 (like the NDVI). These indicators are generated for multiple acquisition dates (at different months/seasons of the year). I was thinking of prioritizing a "month-to-month" or season-to-season comparison for the time being: January 1990 / January 2010 or Winter 1990 / Winter 2010, for example. Once d those indices have been generated for each acquisition date, what indicators/measurements would allow us to assess their variation, evolution, and spatio-temporal change/dynamics? I imagine a percentage would allow us to see the dynamics between two extreme dates, as this post suggests: Calculating NDVI percentage change in QGIS But are there other indicators/measures of spatio-temporal variation and change that could be used? Ideally, one that doesn't just focus on the two "extreme" dates oriented of a time series (1990 and 2010) but also takes into account intermediate dates (1995, 1998, 2004, 2008, for example)? So that the change indicator reflects the entire time series and not just two extreme time steps?

Over a french area, I would like to generate index such as NDVI, NDWI, and NDMI from Landsat MSS, Landsat 4, 5, 7, 8, and 9 , and probably from Sentinel-2 also. My goal is to analyze a long-term time series and compare the indices over the greatest possible time depth up to the present day. I have no particular difficulty generating these indicators technically. However, is it relevant,rational, possible to compare the NDVI, NDWI, and NDMI values ​​generated from different Landsat/sentinel 2 satellites/sensors ? Is a normalization step necessary to compare these indices? If so, which one?

I want to get a band mean across images from NDMI data but I don't find the correct pattern or the right script to print it in the console. How can I do this? My script here: // CALCULO DE INDICES DE HUMEDAD DE DIFERENCIA NORMALIZADA EN EARTH ENGINE // Coleccion Sentinel 2 nivel 2 var ColeccionSentinel = ee.ImageCollection("COPERNICUS/S2_SR") .filterDate ('2022-06-01' ,'2022-06-30') //Definir fecha .filterBounds (geometry) //Definir geometria .filterMetadata ('CLOUDY_PIXEL_PERCENTAGE', 'Less_Than', 20); var Moisture = ee.Image(ColeccionSentinel.mean()); // NDMI (Normalized Difference Moisture Index) var NDMI = Moisture.expression ('float ((NIR - SWIR) / (NIR + SWIR))',{ 'NIR': Moisture.select ('B8'), 'SWIR': Moisture.select ('B11')}); // Simbologia comun para los indices var Simbologia = {max: 1.0, min: -0.5, 'palette': ['1A3678', '2955BC', '5699FF','8DBAE9', 'ACD1FF', 'CAEBFF', 'E5F9FF', 'FDFFB4', 'FFE6A2', 'FFC969', 'FFA12D', 'FF7C1F', 'CA531A', 'FF0000', 'AB0000']}; Map.centerObject (geometry, 15); //Ajustar zoom // Representacion de indice Map.addLayer (NDMI, Simbologia, 'NDMI',1);

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國科會今天表示，轄下的國家災害防救科技中心近日與韓國國家災害管理研究院（NDMI）簽署合作備忘錄，雙方將優先聚焦豪雨等研究，並建立防災研究的協作機制，共同強化區域災害風險治理與社會防災韌性。

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I want to get a band mean across images from NDMI data but I don't find the correct pattern or the right script to print it in the console. How can I do this? My script here: // CALCULO DE INDICES DE HUMEDAD DE DIFERENCIA NORMALIZADA EN EARTH ENGINE // Coleccion Sentinel 2 nivel 2 var ColeccionSentinel = ee.ImageCollection("COPERNICUS/S2_SR") .filterDate ('2022-06-01' ,'2022-06-30') //Definir fecha .filterBounds (geometry) //Definir geometria .filterMetadata ('CLOUDY_PIXEL_PERCENTAGE', 'Less_Than', 20); var Moisture = ee.Image(ColeccionSentinel.mean()); // NDMI (Normalized Difference Moisture Index) var NDMI = Moisture.expression ('float ((NIR - SWIR) / (NIR + SWIR))',{ 'NIR': Moisture.select ('B8'), 'SWIR': Moisture.select ('B11')}); // Simbologia comun para los indices var Simbologia = {max: 1.0, min: -0.5, 'palette': ['1A3678', '2955BC', '5699FF','8DBAE9', 'ACD1FF', 'CAEBFF', 'E5F9FF', 'FDFFB4', 'FFE6A2', 'FFC969', 'FFA12D', 'FF7C1F', 'CA531A', 'FF0000', 'AB0000']}; Map.centerObject (geometry, 15); //Ajustar zoom // Representacion de indice Map.addLayer (NDMI, Simbologia, 'NDMI',1);