Estimating Processing Tomato Water Consumption, Leaf Area Index, and Height Using Sentinel-2 and VENµS Imagery

Can Satellites Predict Yield? Ensemble Machine Learning and Statistical Analysis of Sentinel-2 Imagery for Processing Tomato Yield Prediction

In this paper, we propose an innovative approach for robust prediction of processing tomato yield using open-source AutoML techniques and statistical analysis. Sentinel-2 satellite imagery was deployed to obtain values of five (5) selected vegetation indices (VIs) during the growing season of 2021 (April to September) at 5-day intervals. Actual recorded yields were collected across 108 fields, corresponding to a total area of 410.10 ha of processing tomato in central Greece, to assess the performance of Vis at different temporal scales. In addition, VIs were connected with the crop phenology to establish the annual dynamics of the crop. The highest Pearson coefficient (r) values occurred during a period of 80 to 90 days, indicating the strong relationship between the VIs and the yield. Specifically, RVI presented the highest correlation values of the growing season at 80 (r = 0.72) and 90 days (r = 0.75), while NDVI performed better at 85 days (r = 0.72). This output was confirmed by the AutoML technique, which also indicated the highest performance of the VIs during the same period, with the values of the adjusted R² ranging from 0.60 to 0.72. The most precise results were obtained with the combination of ARD regression and SVR, which was the most successful combination for building an ensemble (adj. R² = 0.67 ± 0.02).

Continuous seasonal monitoring of nitrogen and water content in lettuce using a dual phenomics system

The collection and analysis of large amounts of information on a plant-by-plant basis contributes to the development of precision fertigation and may be achieved by combining remote-sensing technology with high-throughput phenotyping methods. Here, lettuce plants (Lactuca sativa) were grown under optimal and suboptimal nitrogen and irrigation treatments from seedlings to harvest. A Plantarray system was used to calculate and log weights, daily transpiration, and momentary transpiration rates throughout the experiment. From 15 d after planting until experiment termination, the entire array of plants was imaged hourly (from 09.00 h to 14.00 h) using a hyperspectral moving camera. Three vegetation indices were calculated from the plants' reflectance signal: red-edge chlorophyll index (RECI), photochemical reflectance index (PRI), and water index (WI), and combined treatments, physiological measurements, and vegetation indices were compared. RECI values differed significantly between nitrogen treatments from the first day of imaging, and WI values distinguished well-irrigated from drought-treated groups before detecting significant differences in daily transpiration rate. The PRI, calculated hourly during the drought-treatment phase, changed with the momentary transpiration rate. Thus, hyperspectral imaging might be used in growing facilities to detect nitrogen or water shortages in plants before their physiological response affects yields.

Comparing Methods to Extract Crop Height and Estimate Crop Coefficient from UAV Imagery Using Structure from Motion

Although it is common to consider crop height in agricultural management, variation in plant height within the field is seldom addressed because it is challenging to assess from discrete field measurements. However, creating spatial crop height models (CHMs) using structure from motion (SfM) applied to unmanned aerial vehicle (UAV) imagery can easily be done. Therefore, looking into intra- and inter-season height variability has the potential to provide regular information for precision management. This study aimed to test different approaches to deriving crop height from CHM and subsequently estimate the crop coefficient (Kc). CHMs were created for three crops (tomato, potato, and cotton) during five growing seasons, in addition to manual height measurements. The Kc time-series were derived from eddy-covariance measurements in commercial fields and estimated from multispectral UAV imagery in small plots, based on known relationships between Kc and spectral vegetation indices. A comparison of four methods (Mean, Sample, Median, and Peak) was performed to derive single height values from CHMs. Linear regression was performed between crop height estimations from CHMs against manual height measurements and Kc. Height was best predicted using the Mean and the Sample methods for all three crops (R2 = 0.94, 0.84, 0.74 and RMSE = 0.056, 0.071, 0.051 for cotton, potato, and tomato, respectively), as was the prediction of Kc (R2 = 0.98, 0.84, 0.8 and RMSE = 0.026, 0.049, 0.023 for cotton, potato, and tomato, respectively). The Median and Peak methods had far less success in predicting both, and the Peak method was shown to be sensitive to the size of the area analyzed. This study shows that CHMs can help growers identify spatial heterogeneity in crop height and estimate the crop coefficient for precision irrigation applications.

Studying the Feasibility of Assimilating Sentinel-2 and PlanetScope Imagery into the SAFY Crop Model to Predict Within-Field Wheat Yield

Spatial information embedded in a crop model can improve yield prediction. Leaf area index (LAI) is a well-known crop variable often estimated from remote-sensing data and used as an input into crop models. In this study, we evaluated the assimilation of LAI derived from high-resolution (both spatial and temporal) satellite imagery into a mechanistic crop model, a simple algorithm for yield estimate (SAFY), to assess the within-field crop yield. We tested this approach on spring wheat grown in Israel. Empirical LAI models were derived from the biophysical processor for Sentinel-2 LAI and spectral vegetation indices from Sentinel-2 and PlanetScope images. The predicted grain yield obtained from the SAFY model was compared against the harvester’s yield map. LAI derived from PlanetScope and Sentinel-2 fused images achieved higher yield prediction (RMSE = 69 g/m2) accuracy than that of Sentinel-2 LAI (RMSE = 88 g/m2). Even though the spatial yield estimation was only moderately correlated to the ground truth (R2 = 0.45), this is consistent with current studies in this field, and the potential to capture within-field yield variations using high-resolution imagery has been demonstrated. Accordingly, this is the first application of PlanetScope and Sentinel-2 images conjointly used to obtain a high-density time series of LAI information to model within-field yield variability.

Spaceborne Estimation of Leaf Area Index in Cotton, Tomato, and Wheat Using Sentinel-2

Satellite remote sensing is a useful tool for estimating crop variables, particularly Leaf Area Index (LAI), which plays a pivotal role in monitoring crop development. The goal of this study was to identify the optimal Sentinel-2 bands for LAI estimation and to derive Vegetation Indices (VI) that are well correlated with LAI. Linear regression models between time series of Sentinel-2 imagery and field-measured LAI showed that Sentinel-2 Band-8A—Narrow Near InfraRed (NIR) is more accurate for LAI estimation than the traditionally used Band-8 (NIR). Band-5 (Red edge-1) showed the lowest performance out of all red edge bands in tomato and cotton. A novel finding was that Band 9 (Water vapor) showed a very high correlation with LAI. Bands 1, 2, 3, 4, 5, 11, and 12 were saturated at LAI ≈ 3 in cotton and tomato. Bands 6, 7, 8, 8A, and 9 were not saturated at high LAI values in cotton and tomato. The tomato, cotton, and wheat LAI estimation performance of ReNDVI (R2 = 0.79, 0.98, 0.83, respectively) and two new VIs (WEVI (Water vapor red Edge Vegetation Index) (R2 = 0.81, 0.96, 0.71, respectively) and WNEVI (Water vapor narrow NIR red Edge Vegetation index) (R2 = 0.79, 0.98, 0.79, respectively)) were higher than the LAI estimation performance of the commonly used NDVI (R2 = 0.66, 0.83, 0.05, respectively) and other common VIs tested in this study. Consequently, reNDVI, WEVI, and WNEVI can facilitate more accurate agricultural monitoring than traditional VIs.