Evaluating the effect of soil moisture, surface temperature, and humidity variations on MODIS-derived NDVI values

Shehhi, Maryam R. Al; Saffarini, Rasha; Farhat, Ali; Al-Meqbali, Nada K.; Ghedira, Hosni

doi:10.1109/igarss.2011.6049889

Cited by 4 publications

(2 citation statements)

References 8 publications

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“…[ 4 , 5 , 6 , 7 , 8 , 9 ] explored the relationship between NDVI and climate influences such as climate temperature and rainfall. Some studies found that the normalized NDVI has some correlation with soil moisture [ 10 , 11 , 12 ]. A positive correlation between NDVI and indices such as precipitation and potential evapotranspiration is revealed [ 13 ].…”

Section: Introductionmentioning

confidence: 99%

Research on Tea Tree Growth Monitoring Model Using Soil Information

Huang

Jiang

Wang

2022

Plants

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Crop growth monitoring is an important component of agricultural information, and suitable soil temperature (ST), soil moisture content (SMC) and soil electrical conductivity (SEC) play a key role in crop growth. Real-time monitoring of the three soil parameters to predict the growth of tea plantation helps tea trees grow healthily and to accurately grasp the growth trend of tea trees. In this paper, five different models based on the polynomial model and power model were used to construct the soil temperature, soil water content and soil conductivity and tea plantation growth monitoring models. Experiments proved that tea plantation growth were positively correlated with ST and negatively correlated with SMC and SEC, and among the constructed models, the ternary cubic polynomial model was the best, and R square (R2) of the constructed models were 0.6369, 0.4510 and 0.5784, respectively, indicating that SEC was the most relevant to tea plantation growth maximum. To improve the prediction accuracy, a model based on sum of soil temperature (SST), sum of soil water content (SSMC) and sum of soil conductivity (SSEC) was proposed, and the experiments also showed that the ternary cubic polynomial model was the best, with 0.9638, 0.9733 and 0.9660, respectively. At the same time, a model incorporating three parameters such as soil temperature, soil water content and soil conductivity was also suggested, with 0.6605 and 0.9761, respectively, which effectively improved the prediction accuracy. Validation experiments were conducted. Twelve data sets were utilized to verify the performance of the model. The experiments showed that the regressions in the polynomial models achieved a better prediction effect. Finally, a long short-term memory (LSTM) network prediction model optimized by the bald eagle search algorithm (BES) was also constructed, and R2, root mean square error (RMSE), mean squared error (MSE), mean absolute error (MAE) and mean absolute percentage error (MAPE) of prediction were 0.8666, 0.0629, 0.0040, 0.0436 and 10.5257, respectively, which significantly outperformed the LSTM network and achieved better performance. The model proposed in this paper can be used to predict the actual situation during the growing period of tea leaves, which can improve the production management of tea plantations and also provide a scientific basis for accurate tea planting and a decision basis for agricultural policy formulation, as well as provide technical support for the realization of agricultural modernization.

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Section: Introductionmentioning

confidence: 99%

Research on Tea Tree Growth Monitoring Model Using Soil Information

Huang

Jiang

Wang

2022

Plants

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“…The first approach used the spectral indices generated by a simple combination of several wavebands to predict SMC. Some studies have found that the normalized difference vegetation index has a certain correlation with soil moisture (Al‐Shehhi, Saffarini, Farhat, Al‐Meqbali, & Ghedira, 2011; Chen, de Jeu, Liu, van der Werf, & Dolman, 2014; Mattar et al., 2012). Wang and Qu (2007) proposed the normalized multi‐band drought index based on three sensitive wavebands (860, 1,640, and 2,130 nm) to estimate soil moisture over vegetated surfaces.…”

Section: Introductionmentioning

confidence: 99%

Predicting soil moisture content over partially vegetation covered surfaces from hyperspectral data with deep learning

Zhang

Liu

et al. 2021

Soil Science Soc of Amer J

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Previous studies for retrieving soil moisture content (SMC) from visible and nearinfrared hyperspectral data over vegetation-covered surfaces using spectral unmixing, non-negative matrix factorization, and albedo/vegetation coverage in trapezoid spaces have required mass spectral preprocessing and offered only limited improvements in prediction accuracy. Recently, deep learning has triggered some improvements in soil properties prediction from hyperspectral data because of its automatic feature extraction and high accuracy. In this study, hyperspectral data in a simulation experiment with different vegetation coverages, SMCs, and soil types were acquired. Deep learning models, one-dimensional convolutional neural network (1D-CNN), and long short-term memory network (LSTM) are proposed to predict SMC. The results showed that two deep learning models achieved excellent predictions (residual prediction deviation [RPD] > 2.5) using the unpreprocessed mixed spectra and partial least squares regression (PLSR) had a good prediction (RPD = 1.88). The 1D-CNN (R 2 p = .91) and LSTM (R 2 p = .90) significantly outperformed PLSR (R 2 p = .72), which demonstrated that deep learning could improve SMC prediction over partially vegetation-covered surfaces. However, when only using bare soil spectra, the prediction accuracy was commensurate, whether through the 1D-CNN, LSTM, or PLSR models; additionally, 1D-CNN and LSTM had better performance on all mixed spectra than bare soil spectra. These results indicated that deep learning had no advantage on smaller datasets. We also found that SMC prediction with deep learning was affected by vegetation coverage and soil type but was still very good. The 1D-CNN and LSTM are effective models for predicting SMC with large hyperspectral datasets acquired from complex soil surface conditions.

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Assessing how irrigation practices and soil moisture affect crop growth through monitoring Sentinel-1 and Sentinel-2 data

Ibrahim,

Rasul,

Abdullah

2023

Environ Monit Assess

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This study authorizes processes and approaches using optical and microwave data to determine the availability of water in the study area at any given moment. This will aid in identifying the optimal time and location for irrigation to enhance crop growth. For this purpose, a set of spectral vegetation parameters (from Sentinel-2), soil moisture (from Sentinel-1), evapotranspiration, and surface temperature (from Landsat-8) were used, along with field data on water content and irrigation timing. The results showed that both NDVI and NDMI are highly sensitive to moisture, making them the best indices for determining the timing and location of irrigation. This research contributes to sustainable agricultural development. It has implications for farmers, policymakers, and researchers in optimizing irrigation schedules, developing policies for sustainable agriculture, and enhancing crop productivity while conserving water resources. This approach can be particularly useful in regions facing water scarcity, where the efficient use of water resources is crucial for sustainable agricultural development

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Evaluating the effect of soil moisture, surface temperature, and humidity variations on MODIS-derived NDVI values

Cited by 4 publications

References 8 publications

Research on Tea Tree Growth Monitoring Model Using Soil Information

Research on Tea Tree Growth Monitoring Model Using Soil Information

Predicting soil moisture content over partially vegetation covered surfaces from hyperspectral data with deep learning

Assessing how irrigation practices and soil moisture affect crop growth through monitoring Sentinel-1 and Sentinel-2 data

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