Field validation of NDVI to identify crop phenological signatures

IF 5.4 2区农林科学 Q1 AGRICULTURE, MULTIDISCIPLINARY

Precision Agriculture Pub Date : 2024-07-12 DOI:10.1007/s11119-024-10165-6

Muhammad Tousif Bhatti, Hammad Gilani, Muhammad Ashraf, Muhammad Shahid Iqbal, Sarfraz Munir

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引用次数: 0

Abstract

Purpose and Methods

Crop identification using remotely sensed imagery provides useful information to make management decisions about land use and crop health. This research used phonecams to acquire the Normalized Difference Vegetation Index (NDVI) of various crops for three crop seasons. NDVI time series from Sentinel (L121-L192) images was also acquired using Google Earth Engine (GEE) for the same period. The resolution of satellite data is low therefore gap filling and smoothening filters were applied to the time series data. The comparison of data from satellite images and phenocam provides useful insight into crop phenology. The results show that NDVI is generally underestimated when compared to phenocam data. The Savitzky-Golay (SG) and some other gap filling and smoothening methods are applied to NDVI time series based on satellite images. The smoothened NDVI curves are statistically compared with daily NDVI series based on phenocam images as a reference.

Results

The SG method has performed better than other methods like moving average. Furthermore, polynomial order has been found to be the most sensitive parameter in applying SG filter in GEE. Sentinel (L121-L192) image was used to identify wheat during the year 2022–2023 in Sargodha district where experimental fields were located. The Random Forest Machine Leaning algorithm was used in GEE as a classifier.

Conclusion

The classification accuracy has been found 97% using this algorithm which suggests its usefulness in applying to other areas with similar agro-climatic characteristics.

Abstract Image

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对 NDVI 进行实地验证，以确定作物物候特征

目的和方法利用遥感图像识别作物可为有关土地利用和作物健康的管理决策提供有用信息。本研究使用摄像头获取了三个作物季节中各种作物的归一化植被指数（NDVI）。此外，还使用谷歌地球引擎（GEE）从哨兵（L121-L192）图像中获取了同期的归一化植被指数时间序列。卫星数据的分辨率较低，因此对时间序列数据采用了间隙填充和平滑滤波器。卫星图像和 phenocam 数据的比较有助于深入了解作物物候。结果表明，与 phenocam 数据相比，NDVI 通常被低估。对基于卫星图像的 NDVI 时间序列采用了 Savitzky-Golay（SG）和其他一些间隙填充和平滑方法。将平滑后的 NDVI 曲线与基于 phenocam 图像作为参考的每日 NDVI 序列进行统计比较。此外，还发现多项式阶数是在 GEE 中应用 SG 滤波时最敏感的参数。哨兵（L121-L192）图像被用来识别 2022-2023 年期间位于试验田所在的 Sargodha 地区的小麦。在 GEE 中使用随机森林机器倾斜算法作为分类器。结论使用该算法发现分类准确率为 97%，这表明该算法适用于具有类似农业气候特征的其他地区。

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来源期刊

Precision Agriculture 农林科学-农业综合

CiteScore

12.30

自引率

8.10%

发文量

103

审稿时长

>24 weeks

期刊介绍： Precision Agriculture promotes the most innovative results coming from the research in the field of precision agriculture. It provides an effective forum for disseminating original and fundamental research and experience in the rapidly advancing area of precision farming. There are many topics in the field of precision agriculture; therefore, the topics that are addressed include, but are not limited to: Natural Resources Variability: Soil and landscape variability, digital elevation models, soil mapping, geostatistics, geographic information systems, microclimate, weather forecasting, remote sensing, management units, scale, etc. Managing Variability: Sampling techniques, site-specific nutrient and crop protection chemical recommendation, crop quality, tillage, seed density, seed variety, yield mapping, remote sensing, record keeping systems, data interpretation and use, crops (corn, wheat, sugar beets, potatoes, peanut, cotton, vegetables, etc.), management scale, etc. Engineering Technology: Computers, positioning systems, DGPS, machinery, tillage, planting, nutrient and crop protection implements, manure, irrigation, fertigation, yield monitor and mapping, soil physical and chemical characteristic sensors, weed/pest mapping, etc. Profitability: MEY, net returns, BMPs, optimum recommendations, crop quality, technology cost, sustainability, social impacts, marketing, cooperatives, farm scale, crop type, etc. Environment: Nutrient, crop protection chemicals, sediments, leaching, runoff, practices, field, watershed, on/off farm, artificial drainage, ground water, surface water, etc. Technology Transfer: Skill needs, education, training, outreach, methods, surveys, agri-business, producers, distance education, Internet, simulations models, decision support systems, expert systems, on-farm experimentation, partnerships, quality of rural life, etc.