Оптимизируйте свои подборки
Сохраняйте и классифицируйте контент в соответствии со своими настройками.
iSDAoil Эффективная емкость катионного обмена
Прогнозируемое среднее и стандартное отклонение эффективной емкости катионного обмена на глубине почвы 0–20 см и 20–50 см. Значения пикселей необходимо преобразовать обратно с помощью exp(x/10)-1. В районах густых джунглей (как правило, над центральной Африкой) точность модели низкая и поэтому такие артефакты, как полосатость (полосы)…
Общий углерод на глубине почвы 0–20 см и 20–50 см, прогнозируемое среднее и стандартное отклонение. Значения пикселей должны быть обратно преобразованы с помощью exp(x/10)-1. В районах густых джунглей (как правило, над центральной Африкой) точность модели низкая, и поэтому такие артефакты, как полосы (полосы), могут быть…
Класс текстуры Министерства сельского хозяйства США при глубине почвы 0–20 см и 20–50 см. В районах густых джунглей (как правило, над центральной Африкой) точность модели низкая, поэтому могут быть видны такие артефакты, как полосы. Прогнозы свойств почвы были сделаны компанией Innovative Solutions for Decision…
Извлекаемый алюминий на глубине почвы 0–20 см и 20–50 см, прогнозируемое среднее и стандартное отклонение. Значения пикселей должны быть обратно преобразованы с помощью exp(x/10)-1. Прогнозы свойств почвы были сделаны компанией Innovative Solutions for Decision Agriculture Ltd. (iSDA) с размером пикселя 30 м с использованием машинного обучения в сочетании…
[null,null,[],[[["\u003cp\u003eThis dataset provides soil property predictions for Africa at 30m pixel size, including extractable aluminum, total carbon, effective cation exchange capacity, and USDA texture class.\u003c/p\u003e\n"],["\u003cp\u003ePredictions are available for two soil depths: 0-20 cm and 20-50 cm, and include predicted mean and standard deviation.\u003c/p\u003e\n"],["\u003cp\u003eData is back-transformed using exp(x/10)-1 for analysis.\u003c/p\u003e\n"],["\u003cp\u003eModel accuracy is lower in dense jungle areas (generally over central Africa), potentially leading to artifacts like banding.\u003c/p\u003e\n"],["\u003cp\u003ePredictions were generated by Innovative Solutions for Decision Agriculture Ltd.(iSDA) using machine learning techniques.\u003c/p\u003e\n"]]],["iSDA provides soil data for Africa at 30m pixel size, focusing on depths of 0-20 cm and 20-50 cm. This includes extractable aluminium, total carbon, effective cation exchange capacity, and USDA texture class. Data includes predicted mean and standard deviation. Pixel values require back-transformation using the formula exp(x/10)-1. Model accuracy may be low in dense jungle areas, potentially showing banding artifacts. Machine learning is employed for soil property predictions.\n"],null,["# Datasets tagged aluminium in Earth Engine\n\n-\n\n |-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|\n | [### iSDAsoil Effective Cation Exchange Capacity](/earth-engine/datasets/catalog/ISDASOIL_Africa_v1_cation_exchange_capacity) |\n | Effective Cation Exchange Capacity predicted mean and standard deviation at soil depths of 0-20 cm and 20-50 cm, Pixel values must be back-transformed with exp(x/10)-1. In areas of dense jungle (generally over central Africa), model accuracy is low and therefore artifacts such as banding (striping) ... |\n | [africa](/earth-engine/datasets/tags/africa) [aluminium](/earth-engine/datasets/tags/aluminium) [isda](/earth-engine/datasets/tags/isda) [soil](/earth-engine/datasets/tags/soil) |\n\n-\n\n |-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|\n | [### iSDAsoil Total Carbon](/earth-engine/datasets/catalog/ISDASOIL_Africa_v1_carbon_total) |\n | Total carbon at soil depths of 0-20 cm and 20-50 cm, predicted mean and standard deviation. Pixel values must be back-transformed with exp(x/10)-1. In areas of dense jungle (generally over central Africa), model accuracy is low and therefore artifacts such as banding (striping) might be ... |\n | [africa](/earth-engine/datasets/tags/africa) [aluminium](/earth-engine/datasets/tags/aluminium) [isda](/earth-engine/datasets/tags/isda) [soil](/earth-engine/datasets/tags/soil) |\n\n-\n\n |-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|\n | [### iSDAsoil USDA Texture Class](/earth-engine/datasets/catalog/ISDASOIL_Africa_v1_texture_class) |\n | USDA Texture Class at soil depths of 0-20 cm and 20-50 cm. In areas of dense jungle (generally over central Africa), model accuracy is low and therefore artifacts such as banding (striping) might be seen. Soil property predictions were made by Innovative Solutions for Decision ... |\n | [africa](/earth-engine/datasets/tags/africa) [aluminium](/earth-engine/datasets/tags/aluminium) [isda](/earth-engine/datasets/tags/isda) [soil](/earth-engine/datasets/tags/soil) |\n\n-\n\n |---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|\n | [### iSDAsoil extractable Aluminium](/earth-engine/datasets/catalog/ISDASOIL_Africa_v1_aluminium_extractable) |\n | Extractable aluminium at soil depths of 0-20 cm and 20-50 cm, predicted mean and standard deviation. Pixel values must be back-transformed with exp(x/10)-1. Soil property predictions were made by Innovative Solutions for Decision Agriculture Ltd. (iSDA) at 30 m pixel size using machine learning coupled ... |\n | [africa](/earth-engine/datasets/tags/africa) [aluminium](/earth-engine/datasets/tags/aluminium) [isda](/earth-engine/datasets/tags/isda) [soil](/earth-engine/datasets/tags/soil) |"]]
