Lesson learned from FORMOSAT-2 mission operations

Chern, Jeng-Shing; Wu, An-Ming; Lin, Shin-Fa

doi:10.1016/j.actaastro.2006.02.008

Cited by 25 publications

(14 citation statements)

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“…Unlike other systems operating at high spatial resolution and thanks to its orbital cycle of one day, FORMOSAT-2/RSI observes a particular area potentially every day with a constant viewing angle. However, only about the half of the Earth is observed by this satellite (Chern et al, 2006). During the 2006 experiment, thirty five FORMOSAT-2 images were acquired every 3 to 4 days during 8 months at 10:30 TU from March to October 2006, and with a constant viewing angle of 41 • over the Crau region.…”

Section: Formosat-2 Data: Description Processing and Derived Productsmentioning

confidence: 99%

Combined use of FORMOSAT-2 images with a crop model for biomass and water monitoring of permanent grassland in Mediterranean region

Courault

Hadria²,

Ruget³

et al. 2010

Hydrol. Earth Syst. Sci.

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Abstract. The aim of this study is to propose methods to improve crop and water management in Mediterranean regions. At landscape scale, there is a spatial variability of agricultural practices, particularly for grasslands irrigated by flooding. These grasslands are harvested three times per year and produce high quality hay, but their productions decreased significantly during the last few years because of the water scarcity. It is therefore important to assess the real water requirement for crops in order to predict productions in the case of agricultural practice modifications. Until now, the spatial variability of agricultural practices was obtained through surveys from farmers, but this method was tedious to describe an entire region. Thus, the specific aim of the study is to develop and assess a new method based on a crop model for estimating water balance and crop yield constrained by products derived from optical remote sensing data with high spatio-temporal resolution.A methodology, based on the combined use of FORMOSAT-2 images and the STICS crop model, was developed to estimate production, evapotranspiration and drainage of irrigated grasslands in "the Crau" region in the South Eastern France. Numerous surveys and ground measurements were performed during an experiment conducted in 2006. Simple algorithms were developed to retrieve the dynamic of Leaf Area Index (LAI) for each plot and the main agricultural practices such as mowing and irrigation dates. These variables computed from remote sensing were then used to parameterize STICS, applied at region scale to estimate the spatial variability of water budget associated with the biomass productions. Results are displayed at the farm Correspondence to: D. Courault (courault@avignon.inra.fr) scale. Satisfactory results were obtained when compared to ground measurements. The method for the extrapolation to other regions or crops is discussed as regard to data available.

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Section: Formosat-2 Data: Description Processing and Derived Productsmentioning

confidence: 99%

Combined use of FORMOSAT-2 images with a crop model for biomass and water monitoring of permanent grassland in Mediterranean region

Courault

Hadria²,

Ruget³

et al. 2010

Hydrol. Earth Syst. Sci.

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“…It produces optical images using push-broom sensors (in the visible and short-wave infrared domains) with high spatial resolution (8 m in multi-spectral and 2 m in panchromatic modes), with a constant viewing angle: 45˝lateral and 45˝forward-backward looking [37]. Each image covers an area of 24ˆ24 km 2 .…”

Section: Satellite Imagesmentioning

confidence: 99%

Early Detection of Summer Crops Using High Spatial Resolution Optical Image Time Series

et al. 2016

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Abstract:In the context of climate change, agricultural managers have the imperative to combine sufficient productivity with durability of the resources. Many studies have shown the interest of recent satellite missions as suitable tools for agricultural surveys. Nevertheless, they are not predictive methods. A system able to detect summer crops as early as possible is important in order to obtain valuable information for a better water management strategy. The detection of summer crops before the beginning of the irrigation period is therefore our objective. The study area is located near Toulouse (southwestern France), and is a region of mixed farming with a wide variety of irrigated and non-irrigated crops. Using the reference data for the years concerned, a set of fixed thresholds are applied to a vegetation index (the Normalized Difference Vegetation Index, NDVI) for each agricultural season of multi-spectral satellite optical imagery acquired at decametric spatial resolutions from 2006 to 2013. The performance (i.e., accuracy) is contrasted according to the agricultural practices, the development states of the different crops and the number of acquisition dates (one to three in the results presented here). The detection of summer crops reaches 64% to 88% with a single date, 80% to 88% with two dates and 90% to 99% with three dates. The robustness of this method is tested for several years (showing an impact of meteorological conditions on the actual choice of images), several sensors and several resolutions.

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“…It runs on a Sun-synchronous orbit with an orbit altitude of 891 km and collects images at the same local hour with a constant observation angle for the same site [33]. The multispectral image of FORMOSAT-2 covers four spectral band regions with a ground resolution of 8 m: blue (B) (450-520 nm), green (G) (520-600 nm), red (R) (630-690 nm) and NIR (760-900 nm) [34].…”

Section: Remote Sensing Images and Preprocessingmentioning

confidence: 99%

Satellite Remote Sensing-Based In-Season Diagnosis of Rice Nitrogen Status in Northeast China

et al. 2015

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Rice farming in Northeast China is crucially important for China's food security and sustainable development. A key challenge is how to optimize nitrogen (N) management to ensure high yield production while improving N use efficiency and protecting the environment. Handheld chlorophyll meter (CM) and active crop canopy sensors have been used to improve rice N management in this region. However, these technologies are still time consuming for large-scale applications. Satellite remote sensing provides a promising technology for large-scale crop growth monitoring and precision management. The objective of this study was to evaluate the potential of using FORMOSAT-2 satellite images to diagnose rice N status for guiding topdressing N OPEN ACCESS Remote Sens. 2015, 7 10647 application at the stem elongation stage in Northeast China. Five farmers' fields (three in 2011 and two in 2012) were selected from the Qixing Farm in Heilongjiang Province of Northeast China. FORMOSAT-2 satellite images were collected in late June. Simultaneously, 92 field samples were collected and six agronomic variables, including aboveground biomass, leaf area index (LAI), plant N concentration (PNC), plant N uptake (PNU), CM readings and N nutrition index (NNI) defined as the ratio of actual PNC and critical PNC, were determined. Based on the FORMOSAT-2 imagery, a total of 50 vegetation indices (VIs) were computed and correlated with the field-based agronomic variables. Results indicated that 45% of NNI variability could be explained using Ratio Vegetation Index 3 (RVI3) directly across years. A more practical and promising approach was proposed by using satellite remote sensing to estimate aboveground biomass and PNU at the panicle initiation stage and then using these two variables to estimate NNI indirectly (R 2 = 0.52 across years). Further, the difference between the estimated PNU and the critical PNU can be used to guide the topdressing N application rate adjustments.

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Lesson learned from FORMOSAT-2 mission operations

Cited by 25 publications

References 1 publication

Combined use of FORMOSAT-2 images with a crop model for biomass and water monitoring of permanent grassland in Mediterranean region

Combined use of FORMOSAT-2 images with a crop model for biomass and water monitoring of permanent grassland in Mediterranean region

Early Detection of Summer Crops Using High Spatial Resolution Optical Image Time Series

Satellite Remote Sensing-Based In-Season Diagnosis of Rice Nitrogen Status in Northeast China

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