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SOIL & WATER MANAGEMENT & CONSERVATION

Identification of Saline Soils with Multiyear Remote Sensing of Crop Yields

^a Energy and Environment Directorate, Lawrence Livermore National Lab., Livermore, CA 94550
^b International Maize and Wheat Improvement Center (CIMMYT), Wheat Program, Apdo, Postal 6-641, 06600 Mexico DF, Mexico
^c Centro de Estudios Superiores, del Estado de Sonora, C.P. 83400, San Luis Rio Colorado, Mexico
^d Centro de Estudios Superiores, del Estado de Sonora, C.P. 83400, San Luis Rio Colorado, Mexico

^* Corresponding author (dlobell{at}llnl.gov).

	ABSTRACT

TOP NOTES ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS AND DISCUSSION DISCUSSION REFERENCES

 
Soil salinity is an important constraint to agricultural sustainability, but accurate information on its variation across agricultural regions and its impact on regional crop productivity are difficult to obtain. We evaluated the relationships between remotely sensed wheat (Triticum aestivum L.) yields and salinity in an irrigation district in the Colorado River Delta region. The goals of this study were to: (i) document the relative importance of salinity as a constraint to regional wheat production; and (ii) develop techniques to accurately identify saline fields. Estimates of wheat yield from 6 yr of Landsat data agreed well with ground-based records on individual fields (R² = 0.65). Salinity measurements on 122 randomly selected fields revealed that average 0- to 60-cm salinity levels >4 dS m¹ reduced wheat yields, but the relative scarcity of such fields resulted in <1% regional yield loss attributable to salinity. Moreover, low yield was not a reliable indicator of high salinity, because many other factors contributed to yield variability in individual years; however, temporal analysis of yield images derived from remote sensing data showed that a significant fraction of fields exhibited consistently low yields during the 6-yr period. A subsequent survey of 60 additional fields, half of which were consistently low yielding, revealed that this targeted subset had significantly higher salinity at 30- to 60-cm depth than the control group (P = 0.02). These results suggest that consistently low yields are an indicator of high subsurface salinity, and that multiyear yield maps derived from remote sensing therefore hold promise for mapping salinity across agricultural regions.

Abbreviations: ECa, apparent soil electrical conductivity • ECe, electrical conductivity of a saturated soil extract • GLASOD, Global Assessment of Human-induced Soil Degradation • NDVI, normalized difference vegetation index

	INTRODUCTION

TOP NOTES ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS AND DISCUSSION DISCUSSION REFERENCES

 
Accumulation of salts in irrigated soils has represented an important threat to agriculture throughout human history (e.g., Hillel, 1991). Presently, roughly 20% of irrigated agriculture worldwide is thought to be negatively affected by salinization (Ghassemi et al., 1995). Large-scale assessments such as the Global Assessment of Human-Induced Soil Degradation (GLASOD; Oldeman et al., 1990) typically rely on expert judgments from individual countries or regions, however, and are therefore "qualitative and (potentially) subjective" (description of GLASOD project available at www.isric.org/UK/About+ISRIC/Projects/Track+Record/GLASOD.htm, verified 7 Jan. 2007). As Lal et al. (2004) pointed out, "Despite its significance, the available information on soil degradation is often based on reconnaissance surveys, public opinion, extrapolations based on sketchy data, and casual observations by interested travelers (p. 24)."

Improved inventories of the extent and impact of salinity in agricultural lands are needed to more accurately assess the threat of salinization and to guide management decisions and remediation efforts that can reduce productivity losses. The lack of objective, quantitative data reflects the difficulty of acquiring such information, in large part because of the high degree of spatial and temporal heterogeneity of soil salinity. Major advances have been made in the development and application of ground sensors that can rapidly measure apparent soil electrical conductivity (ECa; see Corwin and Lesch [2003] for a review of ground sensor techniques). The ECa measurements are often highly correlated with variations in traditional measurements of salinity, such as the electrical conductivity of a saturated soil extract (ECe), in particular when soil moisture is near field capacity (Lesch and Corwin, 2003), thereby allowing one to map soil ECe with noninvasive techniques. The ECa sensors are thus invaluable tools for mapping salinity within individual fields, but their ability to provide a comprehensive, regional view of salinity's extent and impact remains limited because of the time and expense required for each individual ECa survey.

Satellite-based remote sensing has been widely explored as an alternative to direct field sampling because of its potential to cover large areas repeatedly through time. These efforts have seen limited success, however, due to a range of factors, as reviewed by Metternicht and Zinck (2003). Approaches to detecting salinity with remote sensing can be classified as either direct, in which the reflectance of bare soil itself is evaluated, or indirect, in which vegetation type or condition is used as an indicator of salinity (Metternicht and Zinck, 2003). Successful application of the direct approach using optical remote sensing data requires low soil moisture, a high percentage of exposed bare soil, and little variation in soil surface roughness due to factors other than salinity, such as cultivation. In agricultural regions, all of these conditions are difficult to obtain because of the predominance of crop and residue cover and the high spatial variability of management practices. An additional limitation of the direct approach is that only surface properties can be assessed and not the entire root zone.

Alternatively, several studies have investigated the use of remotely sensed indicators of canopy condition, such as the normalized difference vegetation index (NDVI), to map soil salinity (Madrigal et al., 2003; Wiegand et al., 1996, 1994). These approaches generally assume that salinity is the only factor affecting crop condition, however, and therefore will only be successful in situations where other factors are held constant (for instance by looking at variations within an individual field with fixed management) or where salinity has an extremely large impact on crop condition.

Given the shortcomings of the traditional direct and indirect methodologies, we sought to develop and test a new indirect approach that is useful under a broader range of realistic agricultural settings. Rather than consider crop condition for any single date or growing season, we used maps of crop yields for multiple years derived from satellite data. As described below, statistics from multiple years of yield data may be less influenced by non-soil factors than yields in any single year. A comparison of field measurements of salinity with remotely sensed yields was used to evaluate the degree to which salinity is predictable from single-year and multiyear yield maps. The comparison of salinity with yields also provided insight into the overall impact of salinity on regional production.

	MATERIALS AND METHODS

TOP NOTES ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS AND DISCUSSION DISCUSSION REFERENCES

 
Site Description
The San Luis Rio Colorado Valley (SLRCV) in Sonora, Mexico, is situated at the mouth of the Colorado River just south of the U.S. border (32.4° N, 114.8° W; Fig. 1 ). The valley consists of roughly 27000 irrigated ha, sown predominantly to wheat and a mix of vegetable crops. This study focused on the most northern of three irrigation districts in the SLRVC, which covers roughly 13000 ha. The SLRCV lies within a region classified in GLASOD as having strong (not reclaimable) degradation from salinization, but with infrequent extent (<5% of area; Oldeman et al., 1990). In contrast, local researchers often identify salinization as one of the most important constraints to crop production, with some reporting that up to 47% of land in this region is affected by salinity (López, 2001).

View larger version (102K): [in this window] [in a new window]   Fig. 1. The San Luis Rio Colorado Valley study region, as seen in Band 3 of a Landsat Thematic Mapper image from 31 Mar. 2002. Pixels with wheat appear dark in this image. Locations of field samples in surveys are also shown.

Remote Sensing Analysis
A combination of Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), Landsat Thematic Mapper (TM), and Enhanced Thematic Mapper Plus (ETM+) images was acquired for each of the six growing seasons of wheat from 2000 to 2005 (Table 1). The Landsat images were first converted to top-of-atmosphere reflectance using standard sensor calibration values (Irish, 1999) and georeferenced to within 30 m. The ratio of near-infrared to red reflectance (i.e., Landsat Band 4/Band 3), which is positively correlated with vegetation abundance (Tucker, 1979), exhibited a bimodal distribution for most images. A simple threshold applied to each image therefore provided an indicator of pixels with active crops (Lobell et al., 2003). Pixels that contained active crops in all images acquired (e.g., both February and March) during the wheat growing season were classified as wheat, as no other major crops in this region have an identical growing season to wheat. To validate this approach, the area of pixels identified as wheat was summed across the irrigation district and compared with official area reports from SAGARPA, the state agricultural agency (Secretaría de Agricultura, 2005), revealing errors <2% in all but 1 yr (Table 2) and an RMSE of just 2.4%.

Despite the previous validation in a region with similar characteristics, we sought to independently evaluate the wheat yield estimates in the SLRCV. Ground-based measurements of field-averaged yields across a commercial landscape inevitably require reliance on farmer records of grain harvests. This is especially true when attempting to validate yield estimates for prior years. As a result, substantial errors in "ground-truth" yields may exist because of inaccuracies in farmer reports. We obtained records from local credit unions that had farmer-reported yields for 3 yr: 2000, 2002, and 2005. Any yields below 3 or above 9 t ha^–1 were deemed unreliable and were omitted from comparison with remote sensing estimates. In addition, the locations of some fields were ambiguously identified, and these were therefore also omitted. A total of 43 farmer-reported yield values remained for validation.

Soil Sampling
This study consisted of two primary field experiments. In January 2005, an exploratory survey was conducted where soil samples were taken from 122 randomly selected fields (5 ha each in size) in the irrigation district. The main goal of this survey was to document the distribution of salinity values within the SLRCV and compare field-average salinity levels to remotely sensed yields.

An important consideration for estimating field-average salinity was within-field heterogeneity and the appropriate sampling design. We therefore performed an initial survey of 10 fields in late April 2004 using an EM-38 (Geonics Ltd, Ontario, Canada) to map ECa levels at 30-m intervals, resulting in 42 to 66 measurements per field. These fields were measured within 3 to 6 d following irrigation to ensure the high moisture conditions needed for successful measurement of salinity with the EM-38 (Corwin and Lesch, 2003). Soil cores taken at six locations within each field were used to convert ECa to ECe. Different potential sampling schemes were then evaluated by computing the average ECe for different subsamples, and comparing the subsample averages to the average of all samples within the field. Figure 2 displays the RMSE for field-average ECe for random and stratified random sampling designs with different sample sizes. Based on a desired RMSE of, at most, 0.5 dS m^–1, a stratified random sample with n = 4 was selected and used for the 122 fields surveyed in January 2005.

View larger version (17K): [in this window] [in a new window]   Fig. 2. Simulated root mean square error for predicting field-average soil extract electrical conductivity using different sample sizes and sampling designs (random vs. stratified random). These calculations were made using maps of within-field salinity for 10 fields derived from EM-38 surveys. Error bars show standard error of RMSE for 100 separate simulations.

	RESULTS AND DISCUSSION

TOP NOTES ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS AND DISCUSSION DISCUSSION REFERENCES

 
Yield Estimation
The yield estimates from remote sensing agreed reasonably well with farmer-reported values, with 65% of the variance explained and most values falling near the 1:1 line (Fig. 3 ). As discussed above, the farmer-reported values represent an independent estimate of yields but are not without error. Unfortunately, a reliable estimate of the RMSE between farmer-reported values and actual yields is not available, as it would require an extensive effort to measure harvests in each field. Given that errors in farmer reported yields can be assumed independent of remote sensing errors, however, the ability of remote sensing to predict farmer-reported yields represents a lower bound on the ability to predict actual yields, since any errors in the farmer reported yields will simply add noise to the relationship. The agreement between reported and remotely sensed estimates therefore gives confidence that remote sensing measurements provide a reliable indicator of wheat productivity in this region.

View larger version (18K): [in this window] [in a new window]   Fig. 3. Comparison of image-based yield estimates with farmer-reported yields for 43 fields.

[1]

Thus, values of 3.0 and 4.0 dS m^–1 for 0- to 60-cm salinity correspond to 3.1 and 4.1 dS m^–1, respectively, for 0 to 90 cm.

View larger version (10K): [in this window] [in a new window]   Fig. 4. Histograms of field-average soil electrical conductivity (EC) at depths of 0 to 30 and 30 to 60 cm.

Salinity–Yield Relationships
As soil samples were acquired during the 2005 season, we first compared soil ECe with yields from this season alone (Fig. 5 ). (Because fields were selected randomly without regard to crop type, only 72 of the 122 sampled fields had wheat in 2005.) Salinity at 0 to 30 and 30 to 60 cm both were weakly related to yields, although all fields near or above 4 dS m^–1 in average 0- to 60-cm salinity exhibited relatively low yields. Interestingly, average yields exhibited a slight decline with increased salinity even at fairly low ECe (Fig. 5). This suggests that the threshold model of salinity response may be an oversimplification (Katerji et al., 2003), or that fields with an average ECe of, e.g., 2 dS m^–1 are more likely to have parts of the field above critical salinity levels than fields with lower average ECe. In either case, the effect of salinity appears only minor until average ECe exceeded 4 dS m^–1. This, combined with the fact that few fields exceeded an ECe of 4 dS m^–1, confirms the notion that salinity has an overall small impact on regional wheat productivity. For example, the average yield estimate for fields with ECe <1 dS m^–1 was 6.77 t ha^–1, while the average for all surveyed fields was 6.72 t ha^–1. If one assumes that salinity is uncorrelated with other factors that affect yields, then the regional yield loss due to salinity in this region was just 0.8% in 2005.

View larger version (12K): [in this window] [in a new window]   Fig. 5. Comparison of soil electrical conductivity (EC) measured in January 2005 at (a) 0 to 30 cm, (b) 30 to 60 cm, and (c) 0 to 60 cm with image-based yield estimates for 2005.

Unfortunately, the low number of fields exceeding 4 dS m^–1 in the January survey prohibited a reliable estimate of multiyear statistics for high-salinity fields. As an alternative way to test the hypothesis that saline fields result in consistently low yields, we computed the proportion of fields that exhibited consistently low yields and compared it with the proportion expected by chance. If the former is significantly larger than the latter, then the presence of a factor that consistently suppresses yields is indicated.

For example, Fig. 6 shows the proportion of image pixels (out of those that had wheat in all 6 yr) that were above a specified yield threshold for 0, 1, 2, 3, 4, 5, and 6 yr. Since the average yield varied between years, yield images for each year were converted to percentiles instead of yields, with 0 and 100% corresponding to the minimum and maximum estimated yield throughout the valley for each year. The null distribution (i.e., the number of pixels, x, expected by chance) was calculated based on the binomial distribution:

[2]

where p is the threshold used. Figure 6 shows the observed and null distribution for p = 50% and p = 80%. In both cases, significantly more pixels were observed to exceed the threshold in 0 yr than expected by chance, indicating the presence of a consistent, yield-suppressing factor. For example, roughly 39% of pixels never exceeded 80%, whereas only 26% of such pixels were expected by chance. While it is, of course, possible that factors other than salinity, such as poor management, contribute to consistently low yields, the high proportion of consistently low-yielding fields suggests that this multiyear statistic provides useful information on some yield controlling factor(s), which may or may not include salinity.

View larger version (14K): [in this window] [in a new window]   Fig. 6. Histograms of the number of years a pixel exceeded the (a) 50th and (b) 80th percentiles of yield in the San Luis Rio Colorado Valley (black lines). Only pixels with yields in all 6 yr were included in histogram. Dashed gray lines show null distribution expected for random yield variations. Significantly more fields than expected by chance were never above the given yield percentiles, suggesting the existence of factors that consistently suppress yields.

View larger version (28K): [in this window] [in a new window]   Fig. 7. Histograms of field-average soil electrical conductivity (EC) at depths of 0 to 30, 30 to 60, and 60 to 90 cm for 30 randomly chosen fields (left) and 30 "targeted" fields (right), which had remotely sensed yields always below the 80th percentile.

The importance of 30- to 60-cm salinity illustrates that measures of surface salinity, such as those made with the direct remote sensing techniques discussed above, may be of limited relevance to crop production even if they are perfectly accurate. Indirect methods that rely on measures of crop stress, such as the approach presented here, may therefore provide more reliable indicators of crop-relevant salinity. This conclusion, though, may depend on region-specific cropping patterns, salinity levels, and correlations between 0- to 30- and 30- to 60-cm salinity values.

	DISCUSSION

TOP NOTES ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS AND DISCUSSION DISCUSSION REFERENCES

 
Given the difficulty of assessing soil salinity and its impact on productivity at the regional scale using traditional approaches, we evaluated the potential contribution of yield data sets derived from remote sensing. Remote sensing allows a fairly rapid and accurate assessment of wheat yields at hundreds of individual fields through time, a data set that would be very difficult to obtain by other means. Comparison of yields with salinity measurements acquired randomly throughout the region revealed a very small impact of salinity on regional wheat production. The low frequency of ECe values >4 dS m^–1, the relative tolerance of wheat to salinity, and the presence of other factors that reduce yields combine to explain the insubstantial effect of salinity on production in this region. It is possible that remotely sensed yield or biomass estimates for other crops, such as alfalfa or vegetables, which are more sensitive to salinity, would present greater correlations with salinity; however, the area surveyed using these crops would be significantly smaller.

A previous study (Madrigal et al., 2003) reported much stronger relationships between wheat yields and salinity in a nearby region in northwest Mexico than found here. They then used this correlation along with NDVI images to calculate that 58% of soils were salt affected. Their training sample was not obtained randomly, however, but rather by selecting areas with visible salinity problems. This led, for instance, to the inclusion of ECe values as high as 20 dS m^–1 in the training set. While this approach may be useful for investigating yield responses to high levels of salinity, their implicit assumption that the training set was representative of the entire region was unjustified. As shown in our study, many factors other than salinity contribute to yield losses throughout an entire agricultural region, and yields in a single year therefore do not generally provide a reliable predictor of soil salinity.

Based on the hypothesis that yield-reducing factors other than soils will tend to vary between years, we evaluated the use of multiyear yield images to identify problem areas. Samples acquired on consistently low-yielding fields exhibited significantly higher salinity levels at the 30- to 60-cm depth, indicating that subsoil salinity affects wheat yields in this region. The use of multiyear statistics therefore appears promising for identifying saline hotspots, although additional work is needed to test this approach, particularly in regions where salinity is a more common problem in crop productivity. Future work should evaluate both the accuracy and cost of an approach that uses remote sensing data, which requires available images and nontrivial processing time, relative to approaches that rely solely on ground-based methodologies, such as ECa surveys. Any increase in the efficiency and accuracy of salinity surveys would be a welcome advance, given the expense and difficulty of regional salinity mapping with traditional methodologies.

	ACKNOWLEDGMENTS

 
This project was supported by Fundacion Produce Sonora and an Environmental Protection Agency Science to Achieve Results (EPA STAR) fellowship to D. Lobell. This work was performed under the auspices of the U.S. Department of Energy by the University of California, Lawrence Livermore National Laboratory, under Contract no. W-7405-Eng-48.

	NOTES

TOP NOTES ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS AND DISCUSSION DISCUSSION REFERENCES

 
All rights reserved. No part of this periodical may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopying, recording, or any information storage and retrieval system, without permission in writing from the publisher. Permission for printing and for reprinting the material contained herein has been obtained by the publisher.

Received for publication August 21, 2006.

	REFERENCES

TOP NOTES ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS AND DISCUSSION DISCUSSION REFERENCES

 

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This Article Abstract Figures Only Full Text (PDF) Free Alert me when this article is cited Alert me if a correction is posted Services Similar articles in this journal Alert me to new issues of the journal Download to citation manager Citing Articles Citing Articles via Google Scholar Google Scholar Articles by Lobell, D. B. Articles by Valenzuela, L. Search for Related Content PubMed Articles by Lobell, D. B. Articles by Valenzuela, L. Agricola Articles by Lobell, D. B. Articles by Valenzuela, L. Related Collections Soil Salinity Remote Sensing Soil Classification and Mapping