תפריט נגישות
ניגודיות עדינהניגודיות גבוהה
מונוכרום
הדגשת קישורים
חסימת אנימציה
פונט קריא
סגור
איפוס הגדרות נגישות
להורדת מודול נגישות חינם תפריט נגישותניגודיות עדינהניגודיות גבוההמונוכרוםהדגשת קישוריםחסימת אנימציהפונט קריאסגוראיפוס הגדרות נגישותלהורדת מודול נגישות חינםBeeri, O.; Pelta, R.; Shilo, T.; Mey-Tal, S.
The crop coefficient (Kc) is a crucial factor in irrigation decision making. The ability to estimate it directly from satellite imagery can immensely assist growers around the globe in their crop monitoring. Direct estimations of Kc from diverse spectral indices, enable the use of various multispectral sensors and increase the ability to map relatively small plots (<1.0 ha). The goal of this study was to examine the accuracy of Kc estimations using different spectral indices with different satellite sensors. To achieve this goal, a database was created. Datasets included 126 satellite images and corresponding Kc from five flux tower sites over four irrigated crops (citrus, corn, alfalfa, and soybean). Kc was estimated remotely from five sensors, by applying previously published indices equations. The results show that the normalized difference vegetation index (NDVI) achieved the highest accuracy (RMSE<0.1 and nRMSE of 10%) among the indices, characterized by only minor accuracy differences between the sensors. The lowest accuracy among all crops was observed in citrus. © Wageningen Academic Publishers 2019
Beeri, O.; Pelta, R.; Shilo, T.; Mey-Tal, S.
The crop coefficient (Kc) is a crucial factor in irrigation decision making. The ability to estimate it directly from satellite imagery can immensely assist growers around the globe in their crop monitoring. Direct estimations of Kc from diverse spectral indices, enable the use of various multispectral sensors and increase the ability to map relatively small plots (<1.0 ha). The goal of this study was to examine the accuracy of Kc estimations using different spectral indices with different satellite sensors. To achieve this goal, a database was created. Datasets included 126 satellite images and corresponding Kc from five flux tower sites over four irrigated crops (citrus, corn, alfalfa, and soybean). Kc was estimated remotely from five sensors, by applying previously published indices equations. The results show that the normalized difference vegetation index (NDVI) achieved the highest accuracy (RMSE<0.1 and nRMSE of 10%) among the indices, characterized by only minor accuracy differences between the sensors. The lowest accuracy among all crops was observed in citrus. © Wageningen Academic Publishers 2019
