Tracking crop phenology in a highly dynamic landscape with knowledge-based Landsat–MODIS data fusion

Sisheber, Biniam; Marshall, Michael; Ayalew, Daniel; Nelson, Andrew

doi:10.1016/j.jag.2021.102670

Cited by 19 publications

(22 citation statements)

References 43 publications

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“…The differences in the length of their growing cycles further aid discrimination. There are limited differences in maximum and minimum values of the backscatters for the different crops, but differences can be observed in their amplitude and standard deviations [11][12][13]. For example, the amplitude and standard deviation of VV-polarized backscatters for maize and carrot were twice as high as those for Rhodes and alfalfa, demonstrating that the backscatters of the maize and carrot fields had larger dynamics than those of the Rhodes and alfalfa fields.…”

Section: Growing Seasonmentioning

confidence: 98%

“…The correlation analysis between SAR backscatters and both NDVI and NDWI demonstrated that the VH/VV ratio and VH-polarized backscatter had relatively high correlation with the optical indices (NDVI, NDWI), indicating that the VH/VV ratio and VH-polarized backscatter can potentially be used for VWC estimation, as NDVI and NDWI have previously been proven to be able to estimate VWC [6] and monitor the crop dynamics [11][12][13]. Exploiting this potential may provide an alternative when the NDVI and NDWI are not available under either cloudy or rainy conditions.…”

Section: Estimating Vwc and Monitoring Crop Dynamics With Combined Se...mentioning

confidence: 99%

“…For example, the moderate-resolution imaging spectroradiometer (MODIS) derived enhanced vegetation index has previously been used to determine crop phenological features (including planting/germination date, heading date and harvesting/maturity date) [11]. Similarly, the fusion of multi-platform/sensor data have been widely applied in crop phenology tracking [12,13] and crop intensity mapping [14]. However, optical sensors are limited to cloud-free conditions.…”

Section: Introductionmentioning

confidence: 99%

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Monitoring Irrigation Events and Crop Dynamics Using Sentinel-1 and Sentinel-2 Time Series

Johansen

McCabe

2022

Remote Sensing

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Capturing and identifying field-based agricultural activities, such as the start, duration and end of irrigation, together with crop sowing/germination, growing period and time of harvest, offer informative metrics that can assist in precision agricultural activities in addition to broader water and food security monitoring efforts. While optically based band-ratios, such as the normalized difference vegetation index (NDVI) and normalized difference water index (NDWI), have been used as descriptors for monitoring crop dynamics, data are not always available due to the influence of clouds and other atmospheric effects on optical sensors. Satellite-based microwave systems, such as the synthetic aperture radar (SAR), offer an all-weather advantage in monitoring soil and crop conditions. In this paper, we leverage the relative strengths of both optical- and microwave-based approaches by combining high resolution Sentinel-1 SAR and Sentinel-2 optical imagery to monitor irrigation events and crop dynamics in a dryland agricultural landscape. A microwave backscatter model was used to analyze the responses of simulated backscatters to soil moisture, NDVI and NDWI (both are correlated with vegetation water content and can be regarded as vegetation descriptors), allowing an empirical relationship between these two platforms. A correlation analysis was also performed using Sentinel-1 SAR and Sentinel-2 optical data over crops of maize, alfalfa, carrot and Rhodes grass in Al Kharj farm of Saudi Arabia to identify an appropriate SAR-based vegetation descriptor. The results illustrate the relationship between SAR and both NDVI and NDWI and demonstrated the relationship between the cross-polarization ratio (VH/VV) and the two optical indices. We explore the capacity of this multi-platform and multi-sensor approach to inform on the spatio-temporal dynamics of a range of agricultural activities, which can be used to facilitate field-based management decisions.

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Section: Growing Seasonmentioning

confidence: 98%

Section: Estimating Vwc and Monitoring Crop Dynamics With Combined Se...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Monitoring Irrigation Events and Crop Dynamics Using Sentinel-1 and Sentinel-2 Time Series

Johansen

McCabe

2022

Remote Sensing

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“…This includes phenology of natural vegetation, which is a dominant aspect of plant ecology (Cleland et al, 2007) and has been shown to be affected by warming (Menzel et al, 2020; Piao et al, 2019; Stuble et al, 2021) and phenology of cultivated crops. For the latter, phenology must be taken into account for crop management (Sisheber et al, 2022), choice of cultivar or cultivar characteristics adapted to a particular region (Zhang et al, 2022) and for evaluating the impact of climate change on crop production (Rezaei et al, 2018). It is thus important to be able to predict phenology as a function of environment, in particular as a function of climate.…”

Section: Introductionmentioning

confidence: 99%

Proposal and extensive test of a calibration protocol for crop phenology models

Wallach

Palosuo

Thorburn

et al. 2022

Preprint

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A major effect of environment on crops is through crop phenology, and therefore, the capacity to predict phenology as a function of soil, weather, and management is important. Mechanistic crop models are a major tool for such predictions. It has been shown that there is a large variability between predictions by different modeling groups for the same inputs, and therefore, a need for shared improvement of crop models. Two pathways to improvement are through improved understanding of the mechanisms of the modeled system, and through improved model parameterization. This article focuses on improving crop model parameters through improved calibration, specifically for prediction of crop phenology. A detailed calibration protocol is proposed, which covers all the steps in the calibration work-flow, namely choice of default parameter values, choice of objective function, choice of parameters to estimate from the data, calculation of optimal parameter values and diagnostics. For those aspects where knowledge of the model and target environments is required, the protocol gives detailed guidelines rather than strict instructions. The protocol includes documentation tables, to make the calibration process more transparent. The protocol was applied by 19 modeling groups to three data sets for wheat phenology. All groups first calibrated their model using their "usual" calibration approach. Evaluation was based on data from sites and years not represented in the training data. Compared to usual calibration, l calibration following the new protocol significantly reduced the error in predictions for the evaluation data, and reduced the variability between modeling groups by 22%.

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“…The satellite sensors with medium spatial resolution, such as Landsat and Sentinel, could provide fine texture, but frequent images are not available to capture the fast changes. The fusion methods consist of unmixing, weight function, and dictionary-pair learning methods (Sisheber et al, 2022;Zhu et al, 2016).…”

mentioning

confidence: 99%

The Capability of high spatial-temporal remote sensing imagery for monitoring surface morphology of lake ice in Chagan Lake of Northeast China

Yang

Shi

et al. 2022

Preprint

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Abstract. The surface morphology of lake ice undergoes remarkable changes under the combined influence of thermal and mechanical forces, which has been rarely observed by remote sensing. A large-scale linear structure has repeatedly appeared on satellite images of Chagan Lake in recent years. We prosed a method to extract linear structure on the lake ice surface. We applied it to high spatial-temporal images merged by the Landsat and GOCI images using an enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM). We monitored the changes in surface morphology in Chagan Lake from November 2018 to March 2019, which were further verified as ice ridges during the field investigation. The average length of the ice ridges during the completely frozen period was 21141.57 ± 68.36 m. The average azimuth angle was 335.48° ± 0.23°, perpendicular to the wind domain. Besides, we discovered spherical ice balls along the southwestern coast. The deformation of surface morphology is closed related to wind direction, snowfall, and air temperature.

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Tracking crop phenology in a highly dynamic landscape with knowledge-based Landsat–MODIS data fusion

Cited by 19 publications

References 43 publications

Monitoring Irrigation Events and Crop Dynamics Using Sentinel-1 and Sentinel-2 Time Series

Monitoring Irrigation Events and Crop Dynamics Using Sentinel-1 and Sentinel-2 Time Series

Proposal and extensive test of a calibration protocol for crop phenology models

The Capability of high spatial-temporal remote sensing imagery for monitoring surface morphology of lake ice in Chagan Lake of Northeast China

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