Assessment of Maize Growth and Development with High- and Medium-Resolution Remote Sensing Products

Ballesteros, Rocío; Moreno, M.; Barroso, Fellype; González-Gómez, Laura; Álvarez, José Fernando Ortega

doi:10.3390/agronomy11050940

Cited by 9 publications

(9 citation statements)

References 32 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In slightly over one quarter of the analyzed articles, the authors used ground-based data. Unmanned aerial platforms equipped with devices recording electromagnetic radiation in the range reflected and absorbed by plants are a relatively new source of spatial information about plant phenology [43,47,54,55,65,84,[108][109][110]118,[140][141][142][143][144][145][146][147][148][149][150][151][152][153]. Investigations conducted with such devices accounted for less than 10% of all the analyzed publications.…”

Section: Source Datamentioning

confidence: 99%

“…The analyses of wheat and rice represented over 50% of all studies of arable crops [13,24,28,34,38,39,41]. Papers on maize accounted for 10% of the analyzed publications [3,5,65,292], whereas slightly less research has been carried out on vineyards and other crops such as soybean [98,101,132], oat [285], barley [269,301], and horticultural plants [30,212,354] (Figure 13).…”

Section: Vegetation Typesmentioning

confidence: 99%

“…Plant analyses are mainly focused on the chlorophyll content [25] or substances contained in plants [91] and, consequently, the condition of plants [24,27]. Research on the growth and development of vegetation can be carried out in both strictly controlled laboratory conditions [29,79,92,93] and using satellite techniques [42,44,46,[49][50][51]56,59,61,62,[64][65][66]68,71,[75][76][77][78]80,88, or other means of transporting remote sensing devices (UAV, airships, airplanes) [43,47,54,55,65,84,[108][109][110]118,[140][141][142][143][144][145][146][147][148]…”

Section: Introductionmentioning

confidence: 99%

“…For many years, measurements were based on traditional sources of information (i.e., direct phenological observations combined with meteorological data). The dynamic development of technology in recent decades has allowed for the use of remote sensing data, mainly properly processed satellite data with different temporal and spatial resolutions [10,14,23,26,28,[30][31][32][33][34][35][40][41][42][43][44][45][46][47][48][49][50][51][52][53][54][55][56][58][59][60][61][62][63][64][65][66][67][68][69][70][71][72]94,95,[134][135][136][137]…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Remote Sensing in Studies of the Growing Season: A Bibliometric Analysis

2022

View full text Add to dashboard Cite

Analyses of climate change based on point observations indicate an extension of the plant growing season, which may have an impact on plant production and functioning of natural ecosystems. Analyses involving remote sensing methods, which have added more detail to results obtained in the traditional way, have been carried out only since the 1980s. The paper presents the results of a bibliometric analysis of papers related to the growing season published from 2000–2021 included in the Web of Science database. Through filtering, 285 publications were selected and subjected to statistical processing and analysis of their content. This resulted in the identification of author teams that mostly focused their research on vegetation growth and in the selection of the most common keywords describing the beginning, end, and duration of the growing season. It was found that most studies on the growing season were reported from Asia, Europe, and North America (i.e., 32%, 28%, and 28%, respectively). The analyzed articles show the advantage of satellite data over low-altitude and ground-based data in providing information on plant vegetation. Over three quarters of the analyzed publications focused on natural plant communities. In the case of crops, wheat and rice were the most frequently studied plants (i.e., they were analyzed in over 30% and over 20% of publications, respectively).

show abstract

Section: Source Datamentioning

confidence: 99%

Section: Vegetation Typesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Remote Sensing in Studies of the Growing Season: A Bibliometric Analysis

2022

View full text Add to dashboard Cite

show abstract

“…Satellite remote sensing (RS) data have been widely used for phenological monitoring of vegetation in large geographic domains, using various observation methods [9][10][11][12]. Emerging techniques are based on time series analysis of remote detection multispectral imaging which can be used to estimate the crops' phenological phases (e.g., beginning of the cycle, peak vegetative development, and the duration of phenological phases) [13][14][15]. As recently discussed by Mahlayeye et al [16], there is a need to further study the cropping patterns at larger scales such as irrigation schemes and watersheds.…”

Section: Introductionmentioning

confidence: 99%

Assessing Spatio-Temporal Dynamics of Deep Percolation Using Crop Evapotranspiration Derived from Earth Observations through Google Earth Engine

Ferreira

Rolim

Paredes

et al. 2022

Water

View full text Add to dashboard Cite

Excess irrigation may result in deep percolation and nitrate transport to groundwater. Furthermore, under Mediterranean climate conditions, heavy winter rains often result in high deep percolation, requiring the separate identification of the two sources of deep percolated water. An integrated methodology was developed to estimate the spatio-temporal dynamics of deep percolation, with the actual crop evapotranspiration (ETc act) being derived from satellite images data and processed on the Google Earth Engine (GEE) platform. GEE allowed to extract time series of vegetation indices derived from Sentinel-2 enabling to define the actual crop coefficient (Kc act) curves based on the observed lengths of crop growth stages. The crop growth stage lengths were then used to feed the soil water balance model ISAREG, and the standard Kc values were derived from the literature; thus, allowing the estimation of irrigation water requirements and deep drainage for independent Homogeneous Units of Analysis (HUA) at the Irrigation Scheme. The HUA are defined according to crop, soil type, and irrigation system. The ISAREG model was previously validated for diverse crops at plot level showing a good accuracy using soil water measurements and farmers’ irrigation calendars. Results show that during the crop season, irrigation caused 11 ± 3% of the total deep percolation. When the hotspots associated with the irrigation events corresponded to soils with low suitability for irrigation, the cultivated crop had no influence. However, maize and spring vegetables stood out when the hotspots corresponded to soils with high suitability for irrigation. On average, during the off-season period, deep percolation averaged 54 ± 6% of the annual precipitation. The spatial aggregation into the Irrigation Scheme scale provided a method for earth-observation-based accounting of the irrigation water requirements, with interest for the water user’s association manager, and at the same time for the detection of water losses by deep percolation and of hotspots within the irrigation scheme.

show abstract

Cumulative temporal vegetation indices from unoccupied aerial systems allow maize (Zea mays L.) hybrid yield to be estimated across environments with fewer flights

Chatterjee

Adak²,

Wilde³

et al. 2023

PLoS ONE

View full text Add to dashboard Cite

Unoccupied aerial systems (UAS) based high throughput phenotyping studies require further investigation to combine different environments and planting times into one model. Here 100 elite breeding hybrids of maize (Zea mays L.) were evaluated in two environment trials–one with optimal planting and irrigation (IHOT), and one dryland with delayed planting (DHOT). RGB (Red-Green-Blue) based canopy height measurement (CHM) and vegetation indices (VIs) were estimated from a UAS platform. Time series and cumulative VIs, by both summation (ΣVI-SUMs) and area under the curve (ΣVI-AUCs), were fit via machine learning regression modeling (random forest, linear, ridge, lasso, elastic net regressions) to estimate grain yield. VIs were more valuable predictors of yield to combine different environments than CHM. Time series VIs and CHM produced high accuracies (~68–72%), but inconsistent models. A little sacrifice in accuracy (~60–65%) produced consistent models using ΣVI-SUMs and CHM during pre-reproductive vegetative growth. Absence of VIs produced poorer accuracies (by about ~5–10%). Normalized difference type VIs produced maximum accuracies, and flowering times were the best times for UAS data acquisition. This study suggests that the best yielding varieties can be accurately predicted in new environments at or before flowering when combining multiple temporal flights and predictors.

show abstract

Assessment of Maize Growth and Development with High- and Medium-Resolution Remote Sensing Products

Cited by 9 publications

References 32 publications

Remote Sensing in Studies of the Growing Season: A Bibliometric Analysis

Remote Sensing in Studies of the Growing Season: A Bibliometric Analysis

Assessing Spatio-Temporal Dynamics of Deep Percolation Using Crop Evapotranspiration Derived from Earth Observations through Google Earth Engine

Cumulative temporal vegetation indices from unoccupied aerial systems allow maize (Zea mays L.) hybrid yield to be estimated across environments with fewer flights

Contact Info

Product

Resources

About