Monitoring Sugarcane Growth Using ENVISAT ASAR Data

Lin, Hui; Chen, Jinsong; Pei, Zhiyuan; Zhang, Songling; Hu, Xianzhi

doi:10.1109/tgrs.2009.2015769

Cited by 89 publications

(16 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, acquiring ground measurement are commonly used [34]. Limitations exist within these models due to difficulties in their generalization and massive parameterization requirements [35]. In contrast, the Water Cloud Model (WCM) was revealed [36] as a relatively simple candidate and has been used extensively for decades for SSM retrieval over vegetated regions [37].…”

Section: Introductionmentioning

confidence: 99%

Retrieving Surface Soil Moisture over Wheat and Soybean Fields during Growing Season Using Modified Water Cloud Model from Radarsat-2 SAR Data

Xing

et al. 2019

Remote Sensing

View full text Add to dashboard Cite

Surface soil moisture (SSM) retrieval over agricultural fields using synthetic aperture radar (SAR) data is often obstructed by the vegetation effects on the backscattering during the growing season. This paper reports the retrieval of SSM from RADARSAT-2 SAR data that were acquired over wheat and soybean fields throughout the 2015 (April to October) growing season. The developed SSM retrieval algorithm includes a vegetation-effect correction. A method that can adequately represent the scattering behavior of vegetation-covered area was developed by defining the backscattering from vegetation and the underlying soil individually to remove the effect of vegetation on the total SAR backscattering. The Dubois model was employed to describe the backscattering from the underlying soil. A modified Water Cloud Model (MWCM) was used to remove the effect of backscattering that is caused by vegetation canopy. SSM was derived from an inversion scheme while using the dual co-polarizations (HH and VV) from the quad polarization RADARSAT-2 SAR data. Validation against ground measurements showed a high correlation between the measured and estimated SSM (R 2 = 0.71, RMSE = 4.43 vol.%, p < 0.01), which suggested an operational potential of RADARSAT-2 SAR data on SSM estimation over wheat and soybean fields during the growing season. of soil moisture data over a large region multiple times is very time-consuming and costly. Satellite remote sensing through optical or microwave sensors provides an effective and cost-efficient means of monitoring and assessing SSM at different scales [7-10]. For example, Sadeghi et al.[8] estimated SSM with a RMSE (root mean square error) less than 0.04 cm 3 /cm −3 with local calibration from Sentinel-2 and Landsat-8 observations for the Walnut Gulch and Little Washita watersheds. Tomer et al. [11] used the RADARSAT-2 images to calculate relative soil moisture. When comparing with the SMOS soil moisture, the results showed good temporal behavior with RMSE of approximately 0.05 m 3 /m 3 and a correlation coefficient of approximately 0.9. For passive microwave, the SMAP-radiometer-based soil moisture data product meets its expected performance of 0.04 m 3 /m 3 volumetric soil moisture (unbiased RMSE) [10]. Optical remote sensing is often hampered by cloud cover, while microwave remote sensing has all-weather day-and-night penetration capabilities [11]. However, the lower spatial resolutions of the passive microwave sensors in orbit limit their applicability to retrieve SSM over individual agricultural fields [12]. Currently, only active sensors, like radars, especially Synthetic Aperture Radars (SARs) (such as C-band Sentinel-1A and 1B, RADARSAT-2, and L-band ALOS-2), can provide observations at high spatial resolutions of about 10 m to 100 m with a relatively coarse temporal resolution (2-4 weeks) [2,12].There is a high correlation between radar backscattering coefficient and SSM since the dielectric constant of soil is directly proportional to the amount of water held in the soil [13]. Therefore, the...

show abstract

Section: Introductionmentioning

confidence: 99%

Retrieving Surface Soil Moisture over Wheat and Soybean Fields during Growing Season Using Modified Water Cloud Model from Radarsat-2 SAR Data

Xing

et al. 2019

Remote Sensing

View full text Add to dashboard Cite

show abstract

“…This model is briefly described in Figure 5. The reflectivity data obtained from the input images can be decomposed as follows [2]: After performing back-projection processing, two images were acquired that have amplitude and phase information. The information of interest is the phase difference between them, calculated as follows:…”

Section: Estimation Model For Corn Crop Growthmentioning

confidence: 99%

“…Several studies have shown the SAR remote sensing capabilities in growth monitoring of various crops [2,3] and crop classification [4][5][6]. Crop growth monitoring has been explored by using techniques based on statistical analysis between radar backscattering and crop height [2,3,7], with the use of techniques like interferometric SAR (InSAR) [8], polarimetric decomposition [9], polarimetric interferometric SAR (Pol-InSAR) [10] and differential SAR Interferometry (DInSAR) [10]. The DInSAR methodology presents high accuracy and spatial resolution, as it takes advantage of the phase difference between images.…”

Section: Introductionmentioning

confidence: 99%

Crop Growth Monitoring with Drone-Borne DInSAR

et al. 2020

View full text Add to dashboard Cite

Accurate, high-resolution maps of for crop growth monitoring are strongly needed by precision agriculture. The information source for such maps has been supplied by satellite-borne radars and optical sensors, and airborne and drone-borne optical sensors. This article presents a novel methodology for obtaining growth deficit maps with an accuracy down to 5 cm and a spatial resolution of 1 m, using differential synthetic aperture radar interferometry (DInSAR). Results are presented with measurements of a drone-borne DInSAR operating in three bands—P, L and C. The decorrelation time of L-band for coffee, sugar cane and corn, and the feasibility for growth deficit maps generation are discussed. A model is presented for evaluating the growth deficit of a corn crop in L-band, starting with 50 cm height. This work shows that the drone-borne DInSAR has potential as a complementary tool for precision agriculture.

show abstract

“…In addition, obtaining GRND returns throughout the study allowed the GRND to Non-GRND ratio of points from Survey6 (final survey) to be utilised in the comparative analysis of this optical data to the physical biomass samples collected in the field. One advantage of using a ratio is that factors that may affect the absolute backscatter will not impact the relationship with the biophysical variation as long as these factors are affecting the GRND and Non-GRND returns to the same magnitude [137]. This resulted in much stronger coefficient of determinations than what was observed in the RedEdge SfM photogrammetry and were comparable to the results from terrestrial LiDAR employed by Eitel et al [155].…”

Section: Crop Height Analysismentioning

confidence: 61%

“…Developments in remote sensing continue to change agricultural science and has demonstrated the capability not only to help identify the crop planted, and estimate planted area, but also estimate yield [134][135][136]. Remote sensing techniques have been widely used over the past several decades in agriculture surveys because of its unique capability of monitoring crop growth and estimating crop yield with known accuracy [137]. Rudorr and Remote sensing from space-borne and manned airborne operations has been widely utilised for their large spatial coverage, however, these systems have historically been associated with relatively low spatio-temporal resolution and relatively high cost associated [22].…”

Section: Introductionmentioning

confidence: 99%

Examining the utility of Simultaneous Localisation and Mapping (SLAM) LiDAR on a low-altitude unmanned aerial vehicle (UAV) across environments of various composition and dynamic complexity

Sofonia¹

View full text Add to dashboard Cite

Rapid technological advancements in aerospace technology and imagery capture have seen a dramatic rise in sensor types available, which has expanded the potential use unmanned aerial vehicles (UAVs) in remote sensing research applications. The proliferation of this technology has resulted in numerous instrument and configuration options available across a broad range of scientific, industrial and management applications, with each configuration having specific strengths and limitations. Information provided by manufacturers, however, is often limited, providing little, or no, information on how these systems may perform across different environments or operating conditions. As a result,

show abstract

Monitoring Sugarcane Growth Using ENVISAT ASAR Data

Cited by 89 publications

References 22 publications

Retrieving Surface Soil Moisture over Wheat and Soybean Fields during Growing Season Using Modified Water Cloud Model from Radarsat-2 SAR Data

Retrieving Surface Soil Moisture over Wheat and Soybean Fields during Growing Season Using Modified Water Cloud Model from Radarsat-2 SAR Data

Crop Growth Monitoring with Drone-Borne DInSAR

Examining the utility of Simultaneous Localisation and Mapping (SLAM) LiDAR on a low-altitude unmanned aerial vehicle (UAV) across environments of various composition and dynamic complexity

Contact Info

Product

Resources

About