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Estimation of Crop Coefficients and Evapotranspiration through Remote Sensing

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A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing in Agriculture and Vegetation".

Deadline for manuscript submissions: 30 September 2024 | Viewed by 9860

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Special Issue Editors

Prof. Dr. Tongren Xu

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Guest Editor

Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
Interests: remote sensing; data assimilation; artificial intelligence; hydrology
Special Issues, Collections and Topics in MDPI journals

Dr. Sayed M. Bateni

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Guest Editor

Department of Civil and Environmental Engineering, Water Resources Research Center, University of Hawaii at Manoa, Honolulu, HI 96822, USA
Interests: satellite data processing; land surface product algorithm; remote sensing classification with machine learning;agrometeorology; agrometeorological disater monitoring with remote sensing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The accurate estimation of agro-meteorological variables such as crop coefficient, soil moisture, evapotranspiration, transpiration, irrigation water, crop yield, and gross primary productivity is very improtant for agricultural water management, irrigation scheduling, water use efficiency, and global food security. This Special Issue will focus on the estimation of agro-meteorological variables (e.g., crop coefficient, evapotranspiration, soil moisture, leaf area index, and land surface temperature) over crop lands using remote sensing data and hydrological models. We welcome original research articles and reviews in this Special Issue. Research areas may include (but are not limited to) the estimation of crop coefficient and evapotranspiration, soil moisture, irrigation water, crop yield, gross primary productivity, land surface temperature, water use efficiency, and leaf area index by incorporating remote sensing data into physical hydrologic, machine learning, data assimilation, and hybrid approaches.

We look forward to receiving your contributions.

Prof. Dr. Tongren Xu
Prof. Dr. Sayed M. Bateni
Guest Editors

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Keywords

crop coefficient
evapotranspiration
gross primary productivity
irrigation water
soil moisture
leaf area index
machine learning
data assimilation

Published Papers (5 papers)

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21 pages, 5100 KiB

Open AccessArticle

Groundwater Management in an Uncommon and Artificial Aquifer Based on K_c Approach and MODIS ET Products for Irrigation Assessment in a Subtropical Island

by Zhenglun Yang, Changyuan Tang, Hasi Bagan, Shunichi Satake, Madoka Orimo, Koichiro Fukumoto and Guangwei Wang

Remote Sens. 2022, 14(24), 6304; https://doi.org/10.3390/rs14246304 - 13 Dec 2022

Viewed by 1141

Abstract

Groundwater is a critical resource in remote and isolated islands where rainfall hardly provides a continuous and even water supply. In this paper, in a very rare and uncommonly found artificial aquifer on Miyako Island, far away from the main continent of Japan, with limited experimental results of evaluations of crop water requirement, MODIS ET together with crop ET_c estimated from K_c coefficient from the nearest island were compared to determine the reliability of the MODIS ET and FAO-56-based ET_c value. The testified K_c approach for sugarcane ET was used to assess the risk of irrigation water shortages using historical metrological data and to predict the future risk of irrigation agriculture under different scenarios of GCM models. It was shown that FAO-56-based ET_c and MOD16A2 were both applicable for crop evapotranspiration on the island. Then, the response of groundwater storage to gross irrigation water requirement was analyzed to clarify the effect of irrigation on groundwater storage and the risk of groundwater depletion under current and future climatic conditions. Results showed that the construction of the dam efficiently secured the irrigation of sugarcane. Using historical climatic data (1951–2021), the influence of estimated irrigation water requirements on groundwater showed that in 296 out of 852 months, irrigation was heavily required. Over a 71 year period, there was absolutely no water for irrigation four times, or nearly once every 18 years. Under the future projected climate from four bias-corrected GCM models with two emission scenarios (2022–2100), the risk of groundwater depletion both in terms of frequency and duration will increase. Therefore, there is a need for either improvement of irrigation water management or additional construction of artificial aquifers on the island. The study proved the value of ET derived from remote sensing in areas lacking the support of experimental results. The methodology developed in the study can be potentially used to evaluate long-term irrigation demand and groundwater management over dry periods for engineering design or dam construction globally. Full article

(This article belongs to the Special Issue Estimation of Crop Coefficients and Evapotranspiration through Remote Sensing)

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15 pages, 15262 KiB

Open AccessArticle

Can Data Assimilation Improve Short-Term Prediction of Land Surface Variables?

by Yingze Tian, Tongren Xu, Fei Chen, Xinlei He and Shi Li

Remote Sens. 2022, 14(20), 5172; https://doi.org/10.3390/rs14205172 - 16 Oct 2022

Viewed by 1201

Abstract

Data assimilation methods have been used to improve the performances of land surface models by integrating remote sensing and in situ measurements. However, the impact of data assimilation on improving the forecast of land surface variables has not been well studied, which is essential for weather and hydrology forecasting. In this study, a multi-pass land data assimilation scheme (MLDAS) based on the Noah-MP model was used to predict short-term land surface variables (e.g., sensible heat fluxes (H), latent heat fluxes (LE), and surface soil moisture (SM)) by jointly assimilating soil moisture, leaf area index (LAI) and solar-induced chlorophyll fluorescence (SIF). The test was conducted at the Mead site during the growing season (1 May to 30 September) in 2003, 2004, and 2005. Four assimilation-prediction scenarios (assimilating for 15 days, 45 days, 75 days, and 105 days from 1 May, then predicting one future month) are adapted to evaluate the influence of assimilation on subsequent prediction against Noah-MP open-loop simulation (OL). On average, MLDAS produces 28.65%, 27.79%, and 19.15% lower root square deviations (RMSD) for daily H, LE, and SM prediction compared to open-loop run, respectively. The influence of assimilation on prediction can reach around 60 days and 100 days for H (LE) and SM, respectively. Our findings indicate that data assimilation can improve the accuracy of land surface variables in a short-term prediction period. Full article

(This article belongs to the Special Issue Estimation of Crop Coefficients and Evapotranspiration through Remote Sensing)

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18 pages, 7598 KiB

Open AccessArticle

Different Vegetation Information Inputs Significantly Affect the Evapotranspiration Simulations of the PT-JPL Model

by Zelin Luo, Mengjing Guo, Peng Bai and Jing Li

Remote Sens. 2022, 14(11), 2573; https://doi.org/10.3390/rs14112573 - 27 May 2022

Cited by 8 | Viewed by 1711

Abstract

Evapotranspiration (ET) is an essential part of the global water cycle, and accurate quantification of ET is of great significance for hydrological research and practice. The Priestley-Taylor Jet Propulsion Laboratory (PT-JPL) model is a commonly used remotely sensed (RS) ET model. The original PT-JPL model includes multiple vegetation variables but only requires the Normalized Difference Vegetation Index (NDVI) as the vegetation input. Other vegetation inputs (e.g., Leaf Area Index (LAI) and Fraction of Absorbed Photosynthetically Active Radiation (FAPAR)) are estimated by the NDVI-based empirical methods. Here we investigate whether introducing more RS vegetation variables beyond NDVI can improve the PT-JPL model’s performance. We combine the vegetation variables derived from RS and empirical methods into four vegetation input schemes for the PT-JPL model. The model performance under four schemes is evaluated at the site scale with the eddy covariance (EC)-based ET measurements and at the basin scale with the water balance-based ET estimates. The results show that the vegetation variables derived by RS and empirical methods are quite different. The ecophysiological constraints of the PT-JPL model constructed by the former are more reasonable in spatial distribution than those constructed by the latter. However, as vegetation input of the PT-JPL model, the scheme derived from empirical methods performs best among the four schemes. In other words, introducing more remotely sensed vegetation variables beyond NDVI into the PT-JPL model degrades the model performance to varying degrees. One possible reason for this is the unrealistic ET partitioning. It is necessary to re-parameterize the biophysical constraints of the PT-JPL model to ensure that the model obtains reasonable internal process simulations, that is, “getting the right results for right reasons.” Full article

(This article belongs to the Special Issue Estimation of Crop Coefficients and Evapotranspiration through Remote Sensing)

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27 pages, 4938 KiB

Open AccessArticle

Estimation of Global Cropland Gross Primary Production from Satellite Observations by Integrating Water Availability Variable in Light-Use-Efficiency Model

by Dandan Du, Chaolei Zheng, Li Jia, Qiting Chen, Min Jiang, Guangcheng Hu and Jing Lu

Remote Sens. 2022, 14(7), 1722; https://doi.org/10.3390/rs14071722 - 02 Apr 2022

Cited by 4 | Viewed by 3047

Abstract

Satellite-based models have been widely used to estimate gross primary production (GPP) of terrestrial ecosystems. Although they have many advantages for mapping spatiotemporal variations of regional or global GPP, the performance in agroecosystems is relatively poor. In this study, a light-use-efficiency model for cropland GPP estimation, named EF-LUE, driven by remote sensing data, was developed by integrating evaporative fraction (EF) as limiting factor accounting for soil water availability. Model parameters were optimized first using CO₂ flux measurements by eddy covariance system from flux tower sites, and the optimized parameters were further spatially extrapolated according to climate zones for global cropland GPP estimation in 2001–2019. The major forcing datasets include the fraction of absorbed photosynthetically active radiation (FAPAR) data from the Copernicus Global Land Service System (CGLS) GEOV2 dataset, EF from the ETMonitor model, and meteorological forcing variables from ERA5 data. The EF-LUE model was first evaluated at flux tower site-level, and the results suggested that the proposed EF-LUE model and the LUE model without using water availability limiting factor, both driven by flux tower meteorology data, explained 82% and 74% of the temporal variations of GPP across crop sites, respectively. The overall KGE increased from 0.73 to 0.83, NSE increased from 0.73 to 0.81, and RMSE decreased from 2.87 to 2.39 g C m⁻² d⁻¹ in the estimated GPP after integrating EF in the LUE model. These improvements may be largely attributed to parameters optimized for different climatic zones and incorporating water availability limiting factor expressed by EF into the light-use-efficiency model. At global scale, the verification by GPP measurements from cropland flux tower sites showed that GPP estimated by the EF-LUE model driven by ERA5 reanalysis meteorological data and EF from ETMonitor had overall the highest R², KGE, and NSE and the smallest RMSE over the four existing GPP datasets (MOD17 GPP, revised EC-LUE GPP, GOSIF GPP and PML-V2 GPP). The global GPP from the EF-LUE model could capture the significant negative GPP anomalies during drought or heat-wave events, indicating its ability to express the impacts of the water stress on cropland GPP. Full article

(This article belongs to the Special Issue Estimation of Crop Coefficients and Evapotranspiration through Remote Sensing)

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16 pages, 2027 KiB

Open AccessTechnical Note

Evapotranspiration Components Dynamic of Highland Barley Using PML ET Product in Tibet

by Jilong Chen, Haiyun Tan, Yongyue Ji, Qingqing Tang, Lingyun Yan, Qiao Chen and Daming Tan

Remote Sens. 2021, 13(23), 4884; https://doi.org/10.3390/rs13234884 - 01 Dec 2021

Cited by 3 | Viewed by 1658

Abstract

Highland barley is the unique germplasm resource and dominant crop in Tibet with low-level precipitation and a severe shortage of available water resources. Understanding the characteristics and dynamics of evapotranspiration (ET) components (vegetation transpiration (Ec), soil evaporation (Es), and canopy interception evaporation (Ei)) of highland barley can help better optimize water management practices. The seasonal and interannual variations in ET components of highland barley were investigated using the PML-V2 ET product during 2001–2020. The results suggested that Es was the most important ET component and accounted for 77% of total ET for highland barley in Tibet. ET components varied obviously over the altitude, Es, and Es/ET ratio; a decreasing trend was observed with the increase in altitude from 3500 m to 3800 m and then this changed to an increasing trend until reaching the altitude of 4100 m, while Ec, Ei, and their ratios presented an opposite changing pattern to that of Es. Seasonal variation in daily ET components of highland barley displayed a parabolic pattern, peaked in August, while the temporal distributions differed considerably among different ET component ratios. The seasonal variations in ET components were correlated significantly with air temperature, relative humidity, and precipitation, while ET components ratios were more influenced by the environment, irrigation practice, and management rather than meteorological variables. Es and its ratio in highland barley decreased significantly during 2001–2020, while the Ec/ET ratio generally showed an opposite trend to the Es/ET ratio, and Ei and its ratio presented an insignificantly decreasing trend. The interannual variations in ET components were not correlated significantly with meteorological variables, while Ei was more influenced by meteorological variables, especially the precipitation characteristics. Full article

(This article belongs to the Special Issue Estimation of Crop Coefficients and Evapotranspiration through Remote Sensing)

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