Estimating Water Status and Biomass of Plant Communities by Remote Sensing

1976 ◽  
pp. 432-438 ◽  
Author(s):  
B. G. Drake
Keyword(s):  
Remote Sensing ◽  
Plant Communities ◽  
Water Status

scholarly journals Remote Sensing for Plant Water Content Monitoring: A Review

2021 ◽  
Vol 13 (11) ◽  
pp. 2088
Author(s):  
Carlos Quemada ◽  
José M. Pérez-Escudero ◽  
Ramón Gonzalo ◽  
Iñigo Ederra ◽  
Luis G. Santesteban ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Continuous Wave ◽  
Technological Development ◽  
Water Status ◽  
Irrigation Management ◽  
Coefficient Of Determination ◽  
Plant Water ◽  
Monitoring Methods

This paper reviews the different remote sensing techniques found in the literature to monitor plant water status, allowing farmers to control the irrigation management and to avoid unnecessary periods of water shortage and a needless waste of valuable water. The scope of this paper covers a broad range of 77 references published between the years 1981 and 2021 and collected from different search web sites, especially Scopus. Among them, 74 references are research papers and the remaining three are review papers. The different collected approaches have been categorized according to the part of the plant subjected to measurement, that is, soil (12.2%), canopy (33.8%), leaves (35.1%) or trunk (18.9%). In addition to a brief summary of each study, the main monitoring technologies have been analyzed in this review. Concerning the presentation of the data, different results have been obtained. According to the year of publication, the number of published papers has increased exponentially over time, mainly due to the technological development over the last decades. The most common sensor is the radiometer, which is employed in 15 papers (20.3%), followed by continuous-wave (CW) spectroscopy (12.2%), camera (10.8%) and THz time-domain spectroscopy (TDS) (10.8%). Excluding two studies, the minimum coefficient of determination (R2) obtained in the references of this review is 0.64. This indicates the high degree of correlation between the estimated and measured data for the different technologies and monitoring methods. The five most frequent water indicators of this study are: normalized difference vegetation index (NDVI) (12.2%), backscattering coefficients (10.8%), spectral reflectance (8.1%), reflection coefficient (8.1%) and dielectric constant (8.1%).

scholarly journals Potential of UAS-Based Remote Sensing for Estimating Tree Water Status and Yield in Sweet Cherry Trees

2020 ◽  
Vol 12 (15) ◽  
pp. 2359
Author(s):  
Víctor Blanco ◽  
Pedro José Blaya-Ros ◽  
Cristina Castillo ◽  
Fulgencio Soto-Vallés ◽  
Roque Torres-Sánchez ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Deficit Irrigation ◽  
Vegetation Index ◽  
Sweet Cherry ◽  
Prunus Avium ◽  
Water Status ◽  
Control Treatment ◽  
Stem Water Potential ◽  
Irrigation Treatments ◽  
Cherry Trees

The present work aims to assess the usefulness of five vegetation indices (VI) derived from multispectral UAS imagery to capture the effects of deficit irrigation on the canopy structure of sweet cherry trees (Prunus avium L.) in southeastern Spain. Three irrigation treatments were assayed, a control treatment and two regulated deficit irrigation treatments. Four airborne flights were carried out during two consecutive seasons; to compare the results of the remote sensing VI, the conventional and continuous water status indicators commonly used to manage sweet cherry tree irrigation were measured, including midday stem water potential (Ψs) and maximum daily shrinkage (MDS). Simple regression between individual VIs and Ψs or MDS found stronger relationships in postharvest than in preharvest. Thus, the normalized difference vegetation index (NDVI), resulted in the strongest relationship with Ψs (r2 = 0.67) and MDS (r2 = 0.45), followed by the normalized difference red edge (NDRE). The sensitivity analysis identified the optimal soil adjusted vegetation index (OSAVI) as the VI with the highest coefficient of variation in postharvest and the difference vegetation index (DVI) in preharvest. A new index is proposed, the transformed red range vegetation index (TRRVI), which was the only VI able to statistically identify a slight water deficit applied in preharvest. The combination of the VIs studied was used in two machine learning models, decision tree and artificial neural networks, to estimate the extra labor needed for harvesting and the sweet cherry yield.

Water deficit in plant communities

1970 ◽  
Vol 48 (6) ◽  
pp. 1199-1201 ◽  
Author(s):  
M. Gračanin ◽  
Lj. Ilijanić ◽  
V. Gaži ◽  
N. Hulina
Keyword(s):  
Water Deficit ◽  
Plant Communities ◽  
Water Regime ◽  
Water Status

Comparative investigations within the two different plant communities of Croatia (Fagetum silvaticae croaticum abietetosum Ht and Querco-Carpinetum croaticum erythronietosum Ht) indicate that (1) the two communities have their own range of water deficit values, (2) the Dw values are dependent on the capability of the species to regulate their water regime, (3) the same species behave differently within the two communities, (4) water deficit of leaves can be used as an indication of the water status of the site and plants, and consequently may have a significant place in the synecology and synchorology of plant communities.

Remote Sensing Techniques and Its Application in Arid Zones of India

2017 ◽  
pp. 193-211
Author(s):  
Lokesh Kumar Jain
Keyword(s):  
Remote Sensing ◽  
Water Status ◽  
Leaf Age ◽  
Nutrient Status ◽  
Arid Zones ◽  
Large Area ◽  
Leaf Reflectance ◽  
Agronomic Management ◽  
Crop Type ◽  
Remote Sensing Techniques

Remote sensing technologies offer the potential for contributing the security to human existence on arid zones in the country in variety of ways. Remote Sensing in agriculture particularly for natural resource management. It provides important coverage, mapping and classification of land cover features. The remote view of the sensor and the ability to store, analyze, and display the sensed data on field maps are make remote sensing a potentially important tool for agriculture. The aerial photography gives two main advantages viz., speedy survey in very large area or remote area and precise description and recording of resources status. Remotely sensed images provide a means to assess field conditions and gave valuable insights into agronomic management. It led to understanding of leaf reflectance and leaf emittance changes in response to leaf thickness, species, canopy shape, leaf age, nutrient status, and water status. Understanding of leaf reflectance has led to quantify various agronomic parameters, e.g., leaf area, crop cover, biomass, crop type, nutrient status, and yield.

scholarly journals Water Management Using Drones and Satellites in Agriculture

Water ◽  
2019 ◽  
Vol 11 (5) ◽  
pp. 874 ◽  
Author(s):  
Javier J. Cancela ◽  
Xesús P. González ◽  
Mar Vilanova ◽  
José M. Mirás-Avalos
Keyword(s):  
Water Management ◽  
Remote Sensing ◽  
Water Status ◽  
Irrigation Management ◽  
Irrigation Efficiency ◽  
Agricultural Systems ◽  
Special Issue ◽  
Crop Water ◽  
Water Requirements ◽  
Remote Sensing Techniques

This document intends to be a presentation of the Special Issue “Water Management Using Drones and Satellites in Agriculture”. The objective of this Special Issue is to provide an overview of recent advances in the methodology of using remote sensing techniques for managing water in agricultural systems. Its eight peer-reviewed articles focus on three topics: new equipment for characterizing water bodies, development of satellite-based technologies for determining crop water requirements in order to enhance irrigation efficiency, and monitoring crop water status through proximal and remote sensing. Overall, these contributions explore new solutions for improving irrigation management and an efficient assessment of crop water needs, being of great value for both researchers and advisors.

scholarly journals A Review of Current and Potential Applications of Remote Sensing to Study the Water Status of Horticultural Crops

Agronomy ◽  
2020 ◽  
Vol 10 (1) ◽  
pp. 140 ◽  
Author(s):  
Deepak Gautam ◽  
Vinay Pagay
Keyword(s):  
Remote Sensing ◽  
Water Status ◽  
Irrigation Management ◽  
3D Structure ◽  
Crop Water ◽  
Horticultural Crops ◽  
Potential Applications ◽  
Remote Measurements ◽  
The Individual ◽  
Precision Irrigation

With increasingly advanced remote sensing systems, more accurate retrievals of crop water status are being made at the individual crop level to aid in precision irrigation. This paper summarises the use of remote sensing for the estimation of water status in horticultural crops. The remote measurements of the water potential, soil moisture, evapotranspiration, canopy 3D structure, and vigour for water status estimation are presented in this comprehensive review. These parameters directly or indirectly provide estimates of crop water status, which is critically important for irrigation management in farms. The review is organised into four main sections: (i) remote sensing platforms; (ii) the remote sensor suite; (iii) techniques adopted for horticultural applications and indicators of water status; and, (iv) case studies of the use of remote sensing in horticultural crops. Finally, the authors’ view is presented with regard to future prospects and research gaps in the estimation of the crop water status for precision irrigation.

scholarly journals Comparing Stability in Random Forest Models to Map Northern Great Plains Plant Communities Using 2015 and 2016 Pleiades Imagery

2019 ◽  
Author(s):  
Jameson Brennan ◽  
Patricia Johnson ◽  
Niall Hanan
Keyword(s):  
Remote Sensing ◽  
Random Forest ◽  
Plant Communities ◽  
Great Plains ◽  
Northern Great Plains ◽  
Plant Structure ◽  
Prairie Dog ◽  
Sensing Applications ◽  
High Resolution Imagery ◽  
Misclassification Rates

Abstract. The use of high resolution imagery in remote sensing has the potential to improve understanding of patch level variability in plant structure and community composition that may be lost at coarser scales. Random forest (RF) is a machine learning technique that has gained considerable traction in remote sensing applications due to its ability to produce accurate classifications with highly dimensional data and relatively efficient computing times. The aim of this study was to test the ability of RF to classify five plant communities located both on and off prairie dog towns in mixed grass prairie landscapes of north central South Dakota, and assess the stability of RF models among different years. During 2015 and 2016, Pleiades satellites were tasked to image the study site for a total of five monthly collections each summer (June–October). Training polygons were mapped in 2016 for the five plant communities and used to train separate 2015 and 2016 RF models. The RF models for 2015 and 2016 were highly effective at predicting different vegetation types associated with, and remote from, prairie dog towns (misclassification rates

scholarly journals Remote sensing of β‐diversity: Evidence from plant communities in a semi‐natural system

2018 ◽  
Vol 22 (1) ◽  
pp. 13-26 ◽  
Author(s):  
Samuel Hoffmann ◽  
Thomas M. Schmitt ◽  
Alessandro Chiarucci ◽  
Severin D. H. Irl ◽  
Duccio Rocchini ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Plant Communities ◽  
Natural System ◽  
Β Diversity
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