Early assessment of crop yield from remotely sensed water stress and solar radiation data

Holzman, Mauro; Carmona, Facundo; Rivas, Raúl Eduardo; Niclòs, Raquel

doi:10.1016/j.isprsjprs.2018.03.014

Cited by 98 publications

(47 citation statements)

References 55 publications

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“…However, in arid areas with precipitation less than 200 mm, the correlation becomes weaker due to insufficient precipitation and a more complex water-heat flux interaction [46]. It should be noticed that those correlations by remote sensing were calculated mostly on an annual basis [47]. It did not reflect the instantaneous response of ET c to daily precipitation as was done in this study.…”

Section: Simulating Crop Evapotranspiration On Rainy Daysmentioning

confidence: 64%

Insentek Sensor: An Alternative to Estimate Daily Crop Evapotranspiration for Maize Plants

Qin

Ning

Liu

et al. 2018

Water

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Estimation of ground-truth daily evapotranspiration (ETc) is very useful for developing sustainable water resource strategies, particularly in the North China Plain (NCP) with limited water supplies. Weighing lysimetry is a well-known approach for measuring actual ETc. Here, we introduced an alternative to lysimetry for ETc determination using Insentek sensors. A comparison experiment was conducted for maize plants at Xuchang Irrigation Experiment Station, in the NCP, in 2015 and 2016. Insentek ETc was evaluated using data on clear days and rainy days independently. We found that daily ETc increased gradually from VE (emergence) to VT (tasseling) stages, peaked at the R1 (silking) stage with the highest value of 7.8 mm·d−1, and then declined until maturity. On average, cumulative total of lysimetric ETc was 19% higher than that of Insentek ETc. The major depth of soil water extraction might be 60 cm for maize plants on lysimeters according to soil water depletion depth monitored by Insentek sensors. Daily ETc significantly related to soil water content (SWC) in topsoil (0–30 cm) in an exponential function (coefficients of determination (R2) = 0.32–0.53), and to precipitation (Pre) in a power function (R2 = 0.84–0.87). The combined SWC (0–30 cm)–Pre–ETc model may offer significant potential for accurate estimation of maize ETc in semi-humid environment of the NCP.

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Section: Simulating Crop Evapotranspiration On Rainy Daysmentioning

confidence: 64%

Insentek Sensor: An Alternative to Estimate Daily Crop Evapotranspiration for Maize Plants

Qin

Ning

Liu

et al. 2018

Water

View full text Add to dashboard Cite

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“…The perturbations of R dif are required when using Yale Interactive terrestrial Biosphere (YIBs) model to study the response of global carbon cycle to fire pollutions 12 . Besides, the fraction of diffuse and direct solar radiation as well as their variations are essential for modelling radiation-use efficiency of wheat during its vegetative phase 13 and the early assessment of crop (i.e., soybean, wheat and sunflower) yield on a daily or shorter basis 14 .…”

Section: Background and Summarymentioning

confidence: 99%

Hourly 5-km surface total and diffuse solar radiation in China, 2007–2018

Jiang

Qin

et al. 2020

Sci Data

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Surface solar radiation is an indispensable parameter for numerical models, and the diffuse component contributes to the carbon uptake in ecosystems. We generated a 12-year (2007–2018) hourly dataset from Multi-functional Transport Satellite (MTSAT) satellite observations, including surface total solar radiation (Rs) and diffuse radiation (Rdif), with 5-km spatial resolution through deep learning techniques. The used deep network tacks the integration of spatial pattern and the simulation of complex radiation transfer by combining convolutional neural network and multi-layer perceptron. Validation against ground measurements shows the correlation coefficient, mean bias error and root mean square error are 0.94, 2.48 W/m2 and 89.75 W/m2 for hourly Rs and 0.85, 8.63 W/m2 and 66.14 W/m2 for hourly Rdif, respectively. The correlation coefficient of Rs and Rdif increases to 0.94 (0.96) and 0.89 (0.92) at daily (monthly) scales, respectively. The spatially continuous hourly maps accurately reflect regional differences and restore the diurnal cycles of solar radiation at fine resolution. This dataset can be valuable for studies on regional climate changes, terrestrial ecosystem simulations and photovoltaic applications.

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“…Additionally, farmland SMC is an essential parameter for the development of irrigated agriculture. A farmland irrigation system can be more effectively managed when the exact soil moisture status of the farmland is known; moreover, information on farmland SMC can also help improve the soil moisture status at the critical stage of crop growth to improve crop yield and quality (Holzman et al, 2018; Kang et al, 2017; Park et al, 2017). The Xinjiang Uygur Autonomous Region is one of the principal grain producing areas in northwest China.…”

Section: Introductionmentioning

confidence: 99%

Combining UAV-based hyperspectral imagery and machine learning algorithms for soil moisture content monitoring

Wang

Ding

et al. 2019

PeerJ

142

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Soil moisture content (SMC) is an important factor that affects agricultural development in arid regions. Compared with the space-borne remote sensing system, the unmanned aerial vehicle (UAV) has been widely used because of its stronger controllability and higher resolution. It also provides a more convenient method for monitoring SMC than normal measurement methods that includes field sampling and oven-drying techniques. However, research based on UAV hyperspectral data has not yet formed a standard procedure in arid regions. Therefore, a universal processing scheme is required. We hypothesized that combining pretreatments of UAV hyperspectral imagery under optimal indices and a set of field observations within a machine learning framework will yield a highly accurate estimate of SMC. Optimal 2D spectral indices act as indispensable variables and allow us to characterize a model’s SMC performance and spatial distribution. For this purpose, we used hyperspectral imagery and a total of 70 topsoil samples (0–10 cm) from the farmland (2.5 × 104 m2) of Fukang City, Xinjiang Uygur AutonomousRegion, China. The random forest (RF) method and extreme learning machine (ELM) were used to estimate the SMC using six methods of pretreatments combined with four optimal spectral indices. The validation accuracy of the estimated method clearly increased compared with that of linear models. The combination of pretreatments and indices by our assessment effectively eliminated the interference and the noises. Comparing two machine learning algorithms showed that the RF models were superior to the ELM models, and the best model was PIR (R2val = 0.907, RMSEP = 1.477, and RPD = 3.396). The SMC map predicted via the best scheme was highly similar to the SMC map measured. We conclude that combining preprocessed spectral indices and machine learning algorithms allows estimation of SMC with high accuracy (R2val = 0.907) via UAV hyperspectral imagery on a regional scale. Ultimately, our program might improve management and conservation strategies for agroecosystem systems in arid regions.

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Early assessment of crop yield from remotely sensed water stress and solar radiation data

Cited by 98 publications

References 55 publications

Insentek Sensor: An Alternative to Estimate Daily Crop Evapotranspiration for Maize Plants

Insentek Sensor: An Alternative to Estimate Daily Crop Evapotranspiration for Maize Plants

Hourly 5-km surface total and diffuse solar radiation in China, 2007–2018

Combining UAV-based hyperspectral imagery and machine learning algorithms for soil moisture content monitoring

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