scholarly journals Application of nanosatellites PlanetScope data to monitor crop growth

2020 ◽  
Vol 171 ◽  
pp. 02014
Author(s):  
Svitlana Kokhan ◽  
Anatoliy Vostokov
Keyword(s):  
Time Series ◽  
Satellite Data ◽  
Growth And Development ◽  
High Spatial Resolution ◽  
Crop Growth ◽  
Surface Reflectance ◽  
Planet Surface ◽  
Ndvi Time Series ◽  
Greenseeker Optical Sensor ◽  
Crop Growth And Development

In this research, an approach to monitor crop growth and development is presented using time series satellite data of high spatial resolution. Monitoring of winter wheat phenology based on images of PlanetScope constellations is considered. By applying various PlanetScope data processing types and ground based GreenSeeker data, differences of NDVI values at two variants of crop fertilization are determined. In particular, the following approaches were used in the research: obtaining the Top of Atmosphere Reflectance (TOA), the Planet Surface Reflectance (SR), and receiving NDVI image in Python using a Rasterio module. It was estimated that NDVI values derived from the surface reflectance imagery were significantly correlated to the ground data of a manual active GreenSeeker optical sensor (p < 0.05). The proposed simplified technique, based on PlanetScope NDVI time series, demonstrates the possibilities to monitor temporal changes in crop growth.

Crop growth and development dynamics of two fodder beet (Beta vulgaris L.) cultivars sown on different dates in New Zealand

2019 ◽  
Vol 63 (3) ◽  
pp. 449-466 ◽  
Author(s):  
Edith N. Khaembah ◽  
Shane Maley ◽  
Mike George ◽  
Emmanuel Chakwizira ◽  
John de Ruiter ◽  
...  
Keyword(s):  
New Zealand ◽  
Beta Vulgaris ◽  
Growth And Development ◽  
Crop Growth ◽  
Fodder Beet ◽  
Crop Growth And Development

Sources with Different Spectra Radiation Influence on Plants Growth and Development

2014 ◽  
Vol 1040 ◽  
pp. 830-834 ◽  
Author(s):  
Alexey N. Yakovlev ◽  
S.B. Turanov ◽  
I.N. Kozyreva ◽  
Darja V. Starodubtseva
Keyword(s):  
Growth And Development ◽  
Light Emitting Diodes ◽  
Experimental Studies ◽  
Crop Growth ◽  
Light Emitting ◽  
Differential Characteristic ◽  
Crop Growth And Development

The article contains results of experimental studies, concerning the influence of sources with different spectra radiation on lettuce crop growth and development. The differential characteristic of the research is the use of RGBW light emitting diodes (LED) based modules. Prospects of LED based modules for radiation of plants are shown.

Novel approaches to mitigate heat stress impacts on crop growth and development

2020 ◽  
Vol 25 (4) ◽  
pp. 627-644 ◽  
Author(s):  
Shulbhi Verma ◽  
Narendra Kumar ◽  
Amit Verma ◽  
Hukum Singh ◽  
Kadambot H. M. Siddique ◽  
...  
Keyword(s):  
Heat Stress ◽  
Growth And Development ◽  
Crop Growth ◽  
Novel Approaches ◽  
Crop Growth And Development

scholarly journals STAIR 2.0: A Generic and Automatic Algorithm to Fuse Modis, Landsat, and Sentinel-2 to Generate 10 m, Daily, and Cloud-/Gap-Free Surface Reflectance Product

2020 ◽  
Vol 12 (19) ◽  
pp. 3209
Author(s):  
Yunan Luo ◽  
Kaiyu Guan ◽  
Jian Peng ◽  
Sibo Wang ◽  
Yizhi Huang
Keyword(s):  
Time Series ◽  
High Resolution ◽  
Satellite Data ◽  
Temporal Frequency ◽  
Fusion Product ◽  
High Temporal Resolution ◽  
Surface Reflectance ◽  
Land Surfaces ◽  
Fine Resolution ◽  
Sentinel 2

Remote sensing datasets with both high spatial and high temporal resolution are critical for monitoring and modeling the dynamics of land surfaces. However, no current satellite sensor could simultaneously achieve both high spatial resolution and high revisiting frequency. Therefore, the integration of different sources of satellite data to produce a fusion product has become a popular solution to address this challenge. Many methods have been proposed to generate synthetic images with rich spatial details and high temporal frequency by combining two types of satellite datasets—usually frequent coarse-resolution images (e.g., MODIS) and sparse fine-resolution images (e.g., Landsat). In this paper, we introduce STAIR 2.0, a new fusion method that extends the previous STAIR fusion framework, to fuse three types of satellite datasets, including MODIS, Landsat, and Sentinel-2. In STAIR 2.0, input images are first processed to impute missing-value pixels that are due to clouds or sensor mechanical issues using a gap-filling algorithm. The multiple refined time series are then integrated stepwisely, from coarse- to fine- and high-resolution, ultimately providing a synthetic daily, high-resolution surface reflectance observations. We applied STAIR 2.0 to generate a 10-m, daily, cloud-/gap-free time series that covers the 2017 growing season of Saunders County, Nebraska. Moreover, the framework is generic and can be extended to integrate more types of satellite data sources, further improving the quality of the fusion product.

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