A Review of Crop Water Stress Assessment Using Remote Sensing

Ahmad, Uzair; Alvino, A.; Marino, Stefano

doi:10.3390/rs13204155

Cited by 58 publications

(29 citation statements)

References 190 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…More capable techniques, such as quantifying leaf and stem water potential at midday [1], have been introduced; however, the methods are time consuming and destructive. Recently, the development of plant sensing equipment utilising remote sensing to assess environmental and plant physiological changes in a fast and nod-destructive manner has seen a significant leap in progress [2].…”

Section: Introductionmentioning

confidence: 99%

Lightweight deep CNN models for identifying drought stressed plant

Kamarudin

Ismail²

2022

IOP Conf. Ser.: Earth Environ. Sci.

View full text Add to dashboard Cite

Drought is one of the most severe climatological disasters that has negative impact on agricultural production around the world. Over the years, computer vision technology has been used in conjunction with machine learning applications to replace traditional destructive and time-consuming methods for real-time monitoring of drought-affected plant. Deep learning (DL) techniques have gained a stellar reputation in image classification recently, with convolutional neural network (CNN) emerging as the industry standard. However, the size of deep CNN models is frequently large due to massive number of parameters and field application is often not feasible due to limited storage and computational resources. Several lightweight CNN models have been selected based on the number of network parameters of less than 6M and were trained and tested. The EfficientNet model has achieved a classification accuracy of 88.12 and 88.97 percent for identifying severe drought, mild drought, and no drought plants on visible and near-infrared images respectively. The findings of this study can be used to assist in the development of automated early detection of drought stressed plant with model sizes suitable for real-time plant diagnosis on mobile or embedded devices.

show abstract

Section: Introductionmentioning

confidence: 99%

Lightweight deep CNN models for identifying drought stressed plant

Kamarudin

Ismail²

2022

IOP Conf. Ser.: Earth Environ. Sci.

View full text Add to dashboard Cite

show abstract

“…Another significant advancement is in the sand dune and bare areas; rising from only 4.1% of the total land area in 1990 to 4.6% in 10 years (2000), further increasing to 5.4% in another 10 years (2010) and surprisingly, 9% over the next 5 years (2010-2015). Elijah et al [34], also using satellite data analysis, reported a drastic increase in sand dunes in the study area between 2010-2013. Cumulatively, our results demonstrate that sand dunes and bare areas over the 25 years study period have more than doubled from the start year.…”

Section: Land Cover Changes From 1990 To 2015mentioning

confidence: 76%

“…Moreover, advanced vulnerability assessment and modelling techniques have facilitated the mapping and prediction of areas at the risk of desertification using complex desertification models, remote sensing data, climate records and other aridity indices [29][30][31][32][33]. Despite this, only a few scientific efforts exist in the Nigerian context, with respect to assessing and estimating desert areas using remote sensing and geographic information system (GIS) techniques and models [5,[33][34][35][36]. While the focus of most of these previous studies was on the assessment of the general time slices (i.e., intervals) of historic land cover change, and the measurement of the rate and impact of sand dune development, none evaluated the causes and drivers of desertification in the Nigerian context.…”

Section: Introductionmentioning

confidence: 99%

Desertification in the Sahel Region: A Product of Climate Change or Human Activities? A Case of Desert Encroachment Monitoring in North-Eastern Nigeria Using Remote Sensing Techniques

Ibrahim

Ahmed²,

Arodudu

et al. 2022

Geographies

View full text Add to dashboard Cite

Desertification has become one of the most pronounced ecological disasters, affecting arid and semi-arid areas of Nigeria. This phenomenon is more pronounced in the northern region, particularly the eleven frontline states of Nigeria, sharing borders with the Niger Republic. This has been attributed to a range of natural and anthropogenic factors. Rampant felling of trees for fuelwood, unsustainable agriculture, overgrazing, coupled with unfavourable climatic conditions are among the key factors that aggravate the desertification phenomenon. This study applied geospatial analysis to explore land use/land cover changes and detect major conversions from ecologically active land covers to sand dunes. Results indicate that areas covered by sand dunes (a major indicator of desertification) have doubled over the 25 years under consideration (1990 to 2015). Even though 0.71 km2 of dunes was converted to vegetation, indicative of the success of various international, national, local and individual afforestation efforts, conversely about 10.1 km2 of vegetation were converted to sand dunes, implying around 14 times more deforestation compared to afforestation. On average, our results revealed that the sand dune in the study area is progressing at a mean annual rate of 15.2 km2 annually. The land cover conversion within the 25-year study period was from vegetated land to farmlands. Comparing the progression of a sand dune with climate records of the study area and examining the relationship between indicators of climate change and desertification suggested a mismatch between both processes, as increasing rainfall and lower temperatures observed in 1994, 2005, 2012, and 2014 did not translate into positive feedbacks for desertification in the study area. Likewise, the mean annual Normalized Difference Vegetation Index (NDVI) from 2000 to 2015 shows a deviation between vegetation peaks, mean temperatures and rainfall. On average, our results reveal that the sand dune is progressing at a mean annual rate of about 15.2 km2 in the study area. Based on this study’s land cover change, trend and conversion assessment, visual reconciliation of climate records of land cover data, statistical analysis, observations from ground-truthing, as well as previous literature, it can be inferred that desertification in Nigeria is less a function of climate change, but more a product of human activities driven by poverty, population growth and failed government policies. Further projections by this study also reveal a high probability of more farmlands being converted to sand dunes by the years 2030 and 2045 if current practices prevail.

show abstract

“…VRI has become technically feasible; however, crop water status mapping is necessary to combine irrigation amounts with site-specific crop water demands (Gobbo et al, 2019), although deciding when and where water should be applied has proven problematic (Chastain et al, 2016). An alternative that has been evolving for decades is remote sensing (RS), which can provide maps with sufficient details in a timely manner (Peschechera et al, 2019;Ahmad et al, 2021) and, in some situations, with constant data.…”

Section: Introductionmentioning

confidence: 99%

Orbital Remote Sensing for the Management of Areas Irrigated With a Central Pivot System

et al. 2023

View full text Add to dashboard Cite

Irrigated agriculture is considered one of the most important techniques for agricultural production in the world, despite being the sector that uses water resources the most. Thus, technological advances have been constantly developed and applied in the sector as a way to improve water management and reduce its consumption, considering that some cultures are affected by economic costs and feasibilities of production, because the water cost has increased significantly in recent years. Remote sensing is a technology that has provided good results for estimating evapotranspiration (ETr) in producing areas, enabling more efficient irrigation management for low costs. This study aimed to answer the question about how the generation of irrigation management zones (IMZs) can be used to optimize water use and improve irrigation systems and which ETr models are better for it. Three ETr estimation models (Surface Energy Balance Algorithm for Land [SEBAL], Mapping Evapotranspiration at High Resolution and with Internalized Calibration [METRIC] and Simple Algorithm for Evapotranspiration Retrieving [SAFER]) were used to design IMZs in center pivot cultivated with cotton. The design of IMZs with ETr data proved to be a viable alternative, making it possible to improve water management in irrigated systems, reducing costs with viability irrigation for cotton and others cultures. The METRIC method displayed the greatest ease in obtaining the data used in the ETr estimation for generating the IMZs.

show abstract

A Review of Crop Water Stress Assessment Using Remote Sensing

Cited by 58 publications

References 190 publications

Lightweight deep CNN models for identifying drought stressed plant

Lightweight deep CNN models for identifying drought stressed plant

Desertification in the Sahel Region: A Product of Climate Change or Human Activities? A Case of Desert Encroachment Monitoring in North-Eastern Nigeria Using Remote Sensing Techniques

Orbital Remote Sensing for the Management of Areas Irrigated With a Central Pivot System

Contact Info

Product

Resources

About